US20020128544A1 - Signal processing apparatus - Google Patents
Signal processing apparatus Download PDFInfo
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- US20020128544A1 US20020128544A1 US10/062,859 US6285902A US2002128544A1 US 20020128544 A1 US20020128544 A1 US 20020128544A1 US 6285902 A US6285902 A US 6285902A US 2002128544 A1 US2002128544 A1 US 2002128544A1
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Definitions
- the present invention relates to the field of signal processing. More specifically, the present invention relates to the processing of measured signals, containing a primary signal portion and a secondary signal portion, for the removal or derivation of either the primary or secondary signal portion when little is known about either of these components. More particularly, the present invention relates to modeling the measured signals in a novel way which facilitates minimizing the correlation between the primary signal portion and the secondary signal portion in order to produce a primary and/or secondary signal. The present invention is especially useful for physiological monitoring systems including blood oxygen saturation systems.
- Signal processors are typically employed to remove or derive either the primary or secondary signal portion from a composite measured signal including a primary signal portion and a secondary signal portion.
- a composite signal may contain noise and desirable portions. If the secondary signal portion occupies a different frequency spectrum than the primary signal portion, then conventional filtering techniques such as low pass, band pass, and high pass filtering are available to remove or derive either the primary or the secondary signal portion from the total signal. Fixed single or multiple notch filters could also be employed if the primary and/or secondary signal portion(s) exist at a fixed frequency(s).
- correlation cancelers such as adaptive noise cancelers, dynamically change their transfer function to adapt to and remove portions of a composite signal.
- correlation cancelers require either a secondary reference or a primary reference which correlates to either the secondary signal portion only or the primary signal portion only. For instance, for a measured signal containing noise and desirable signal, the noise can be removed with a correlation canceler if a noise reference is available. This is often the case.
- the amplitude of the reference signals are not necessarily the same as the amplitude of the corresponding primary or secondary signal portions, they have a frequency spectrum which is similar to that of the primary or secondary signal portions.
- Physiological monitoring generally involves measured signals derived from a physiological system, such as the human body. Measurements which are typically taken with physiological monitoring systems include electrocardiographs, blood pressure, blood gas saturation (such as oxygen saturation), capnographs, other blood constituent monitoring, heart rate, respiration rate, electro-encephalograph (EEG) and depth of anesthesia, for example.
- EEG electro-encephalograph
- measurements include those which measure the pressure and quantity of a substance within the body such as cardiac output, venous oxygen saturation, arterial oxygen saturation, bilirubin, total hemoglobin, breathalyzer testing, drug testing, cholesterol testing, glucose testing, extra vasation, and carbon dioxide testing, protein testing, carbon monoxide testing, and other in-vivo measurements, for example. Complications arising in these measurements are often due to motion of the patient, both external and internal (muscle movement, vessel movement, and probe movement, for example), during the measurement process.
- a blood gas monitor is one example of a physiological monitoring system which is based upon the measurement of energy attenuated by biological tissues or substances.
- Blood gas monitors transmit light into the test medium and measure the attenuation of the light as a function of time.
- the output signal of a blood gas monitor which is sensitive to the arterial blood flow contains a component which is a waveform representative of the patient's arterial pulse.
- This type of signal, which contains a component related to the patient's pulse is called a plethysmographic wave, and is shown in FIG. 1 as curve s.
- Plethysmographic waveforms are used in blood gas saturation measurements.
- the amount of blood in the arteries increases and decreases, causing increases and decreases in energy attenuation, illustrated by the cyclic wave s in FIG. 1.
- a digit such as a finger, an ear lobe, or other portion of the body where blood flows close to the skin
- the finger comprises skin, fat, bone, muscle, etc., shown schematically in FIG. 2, each of which attenuates energy incident on the finger in a generally predictable and constant manner.
- fleshy portions of the finger are compressed erratically, for example by motion of the finger, energy attenuation becomes erratic.
- FIG. 3 An example of a more realistic measured waveform S is shown in FIG. 3, illustrating the effect of motion.
- the primary plethysmographic waveform portion of the signal s is the waveform representative of the pulse, corresponding to the sawtooth-like pattern wave in FIG. 1.
- the large, secondary motion-induced excursions in signal amplitude obscure the primary plethysmographic signal s. Even small variations in amplitude make it difficult to distinguish the primary signal component s in the presence of a secondary signal component n.
- a pulse oximeter is a type of blood gas monitor which non-invasively measures the arterial saturation of oxygen in the blood.
- the pumping of the heart forces freshly oxygenated blood into the arteries causing greater energy attenuation.
- the arterial saturation of oxygenated blood may be determined from the depth of the valleys relative to the peaks of two plethysmographic waveforms measured at separate wavelengths.
- Patient movement introduces motion artifacts to the composite signal as illustrated in the plethysmographic waveform illustrated in FIG. 3. These motion artifacts distort the measured signal.
- a signal processor acquires a first measured signal and a second measured signal that is correlated to the first measured signal.
- the first signal comprises a first primary signal portion and a first secondary signal portion.
- the second signal comprises a second primary signal portion and a second secondary signal portion.
- the signals may be acquired by propagating energy through a medium and measuring an attenuated signal after transmission or reflection. Alternatively, the signals may be acquired by measuring energy generated by the medium.
- the first and second measured signals are processed to generate a secondary reference which does not contain the primary signal portions from either of the first or second measured signals.
- This secondary reference is correlated to the secondary signal portion of each of the first and second measured signals.
- the secondary reference is used to remove the secondary portion of each of the first and second measured signals via a correlation canceler, such as an adaptive noise canceler.
- the correlation canceler is a device which takes a first and second input and removes from the first input all signal components which are correlated to the second input. Any unit which performs or nearly performs this function is herein considered to be a correlation canceler.
- An adaptive correlation canceler can be described by analogy to a dynamic multiple notch filter which dynamically changes its transfer function in response to a reference signal and the measured signals to remove frequencies from the measured signals that are also present in the reference signal.
- a typical adaptive correlation canceler receives the signal from which it is desired to remove a component and receives a reference signal of the undesired portion.
- the output of the correlation canceler is a good approximation to the desired signal with the undesired component removed.
- the first and second measured signals may be processed to generate a primary reference which does not contain the secondary signal portions from either of the first or second measured signals.
- the primary reference may then be used to remove the primary portion of each of the first and second measured signals via a correlation canceler.
- the output of the correlation canceler is a good approximation to the secondary signal with the primary signal removed and may be used for subsequent processing in the same instrument or an auxiliary instrument.
- the approximation to the secondary signal may be used as a reference signal for input to a second correlation canceler together with either the first or second measured signals for computation of, respectively, either the first or second primary signal portions.
- Physiological monitors can benefit from signal processors of the present invention.
- a first signal comprising a first primary portion and a first secondary portion and a second signal comprising a second primary portion and a second secondary portion are acquired.
- the signals may be acquired by propagating energy through a patient's body (or a material which is derived from the body, such as breath, blood, or tissue, for example) or inside a vessel and measuring an attenuated signal after transmission or reflection.
- the signal may be acquired by measuring energy generated by a patient's body, such as in electrocardiography.
- the signals are processed via the signal processor of the present invention to acquire either a secondary reference or a primary reference which is input to a correlation canceler, such as an adaptive noise canceler.
- One physiological monitoring apparatus which benefits from the present invention is a monitoring system which determines a signal which is representative of the arterial pulse, called a plethysmographic wave.
- This signal can be used in blood pressure calculations, blood constituent measurements, etc.
- a specific example of such a use is in pulse oximetry.
- Pulse oximetry involves determining the saturation of oxygen in the blood.
- the primary portion of the signal is the arterial blood contribution to attenuation of energy as it passes through a portion of the body where blood flows close to the skin.
- the pumping of the heart causes blood flow to increase and decrease in the arteries in a periodic fashion, causing periodic attenuation wherein the periodic waveform is the plethysmographic waveform representative of the arterial pulse.
- the secondary portion is noise.
- the measured signals are modeled such that this secondary portion of the signal is related to the venous blood contribution to attenuation of energy as it passes through the body.
- the secondary portion also includes artifacts due to patient movement which causes the venous blood to flow in an unpredictable manner, causing unpredictable attenuation and corrupting the otherwise periodic plethysmographic waveform. Respiration also causes the secondary or noise portion to vary, although typically at a lower frequency than the patients pulse rate.
- the measured signal which forms a plethysmographic waveform is modeled in accordance with the present invention such that the primary portion of the signal is representative of arterial blood contribution to attenuation and the secondary portion is due to several other parameters.
- a physiological monitor particularly adapted to pulse oximetry oxygen saturation measurement comprises two light emitting diodes (LED's) which emit light at different wavelengths to produce first and second signals.
- a detector registers the attenuation of the two different energy signals after each passes through an absorptive media, for example a digit such as a finger, or an earlobe.
- the attenuated signals generally comprise both primary (arterial attenuator) and secondary (noise) signal portions.
- a static filtering system such as a bandpass filter, removes a portion of the secondary signal which is outside of a known bandwidth of interest, leaving an erratic or random secondary signal portion, often caused by motion and often difficult to remove, along with the primary signal portion.
- a processor in accordance with one embodiment of the present invention removes the primary signal portions from the measured signals yielding a secondary reference which is a combination of the remaining secondary signal portions.
- the secondary reference is correlated to both of the secondary signal portions.
- the secondary reference and at least one of the measured signals are input to a correlation canceler, such as an adaptive noise canceler, which removes the random or erratic portion of the secondary signal. This yields a good approximation to a primary plethysmographic signal as measured at one of the measured signal wavelengths.
- a correlation canceler such as an adaptive noise canceler
- the processor of the present invention may also remove the secondary signal portions from the measured signals yielding a primary reference which is a combination of the remaining primary signal portions.
- the primary reference is correlated to both of the primary signal portions.
- the primary reference and at least one of the measured signals are input to a correlation canceler which removes the primary portions of the measured signals. This yields a good approximation to the secondary signal at one of the measured signal wavelengths. This signal may be useful for removing secondary signals from an auxiliary instrument as well as determining venous blood oxygen saturation.
- the two measured signals each having primary and secondary signal portions can be related by coefficients.
- the coefficients provide information about the arterial oxygen saturation and about the noise (the venous oxygen saturation and other parameters).
- the coefficients can be determined by minimizing the correlation between the primary and secondary signal portions as defined in the model. Accordingly, the signal model of the present invention can be utilized in many ways in order to obtain information about the measured signals as will be further apparent in the detailed description of the preferred embodiments.
- One aspect of the present invention is a method for use in a signal processor in a signal processor for processing at least two measured signals S 1 and S 2 each containing a primary signal portion s and a secondary signal portion n, the signals S 1 and S 2 being in accordance with the following relationship:
- the method comprises a number of steps.
- a value of coefficient r a is determined which minimize correlation between s 1 and n 1 .
- at least one of the first and second signals is processed using the determined value for r a to significantly reduce n from at least one of the first or second measured signal to form a clean signal.
- the clean signal is displayed on a display.
- the method further comprises the step of processing the clean signal to determine a physiological parameter from the first or second measured signals.
- the parameter is arterial oxygen saturation.
- the parameter is an ECG signal.
- the method further comprises the step of calculating the pulse rate.
- the monitor has a first input configured to receive a first measured signal S 1 having a primary portion, s 1 , and a secondary portion n 1 .
- the monitor also has a second input configured to received a second measured signal S 2 having a primary portion s 2 and a secondary portion n 2 .
- the first and the second measured signals S 1 and S 2 are in accordance with the following relationship:
- the monitor further has a scan reference processor, the scan reference processor responds to a plurality of possible values for r a to multiply the second measured signal by each of the possible values for r a and for each of the resulting values, to subtract the resulting values from the first measured signal to provide a plurality of output signals.
- a correlation canceler having a first input configured to receive the first measured signal, and having a second input configured to receive the plurality of output signals from the saturation scan reference processor, provides a plurality of output vectors corresponding to the correlation cancellation between the plurality of output signals and the first measured signal.
- An integrator having an input configured to receive the plurality of output vectors from the correlation canceler is responsive to the plurality of output vectors to determine a corresponding power for each output vector.
- An extremum detector is coupled at its input to the output of the integrator. The extremum detector is responsive to the corresponding power for each output vector to detect a selected power.
- the plurality of possible values correspond to a plurality of possible values for a selected blood constituent.
- the selected blood constituent is arterial blood oxygen saturation.
- the selected blood constituent is venous blood oxygen saturation.
- the selected blood constituent is carbon monoxide.
- the monitor has a first input configured to receive a first measured signal S 1 having a primary portion, s 1 , and a secondary portion, n 1 .
- the monitor also has a second input configured to received a second measured signal S 2 having a primary portion s 2 and a secondary portion n 2 .
- the first and the second measured signals S 1 and S 2 are in accordance with the following relationship:
- a transform module is responsive to the first and the second measured signals and responsive to a plurality of possible values for r a to provide at least one power curve as an output.
- An extremum calculation module is responsive to the at least one power curve to select a value for r a which minimizes the correlation between s and n, and to calculate from the value for r a a corresponding saturation value as an output.
- a display module is responsive to the output of saturation calculation to display the saturation value.
- FIG. 1 illustrates an ideal plethysmographic waveform.
- FIG. 2 schematically illustrates a typical finger.
- FIG. 3 illustrates a plethysmographic waveform which includes a motion-induced erratic signal portion.
- FIG. 4 a illustrates a schematic diagram of a physiological monitor to compute primary physiological signals.
- FIG. 4 b illustrates a schematic diagram of a physiological monitor to compute secondary signals.
- FIG. 5 a illustrates an example of an adaptive noise canceler which could be employed in a physiological monitor, to compute primary physiological signals.
- FIG. 5 b illustrates an example of an adaptive noise canceler which could be employed in a physiological monitor, to compute secondary motion artifact signals.
- FIG. 5 c illustrates the transfer function of a multiple notch filter.
- FIG. 6 a illustrates a schematic of absorbing material comprising N constituents within the absorbing material.
- FIG. 6 b illustrates another schematic of absorbing material comprising N constituents, including one mixed layer, within the absorbing material.
- FIG. 6 c illustrates another schematic of absorbing material comprising N constituents, including two mixed layers, within the absorbing material.
- FIG. 7 a illustrates a schematic diagram of a monitor, to compute primary and secondary signals in accordance with one aspect of the present invention.
- FIG. 7 b illustrates the ideal correlation canceler energy or power output as a function of the signal coefficients r 1 , r 2 , . . . r n .
- FIG. 7 c illustrates the non-ideal correlation canceler energy or power output as a function of the signal coefficients r 1 , r 2 , . . . r n .
- r 3 r a
- r 7 r v .
- FIG. 8 is a schematic model of a joint process estimator comprising a least-squares lattice predictor and a regression filter.
- FIG. 8 a is a schematic model of a joint process estimator comprising a QRD least-squares lattice (LSL) predictor and a regression filter.
- LSL least-squares lattice
- FIG. 9 is a flowchart representing a subroutine for implementing in software a joint process estimator as modeled in FIG. 8.
- FIG. 9 a is a flowchart representing a subroutine for implementing in software a joint process estimator as modeled in FIG. 8 a.
- FIG. 10 is a schematic model of a joint process estimator with a least-squares lattice predictor and two regression filters.
- FIG. 10 a is a schematic model of a joint process estimator with a QRD least-squares lattice predictor and two regression filters.
- FIG. 11 is an example of a physiological monitor in accordance with the teachings of one aspect of the present invention.
- FIG. 11 a illustrates an example of a low noise emitter current driver with accompanying digital to analog converter.
- FIG. 12 illustrates the front end analog signal conditioning circuitry and the analog to digital conversion circuitry of the physiological monitor of FIG. 11.
- FIG. 13 illustrates further detail of the digital signal processing circuitry of FIG. 11.
- FIG. 14 illustrates additional detail of the operations performed by the digital signal processing circuitry of FIG. 11.
- FIG. 15 illustrates additional detail regarding the demodulation module of FIG. 14.
- FIG. 16 illustrates additional detail regarding the decimation module of FIG. 14.
- FIG. 17 represents a more detailed block diagram of the operations of the statistics module of FIG. 14.
- FIG. 18 illustrates a block diagram of the operations of one embodiment of the saturation transform module of FIG. 14.
- FIG. 19 illustrates a block diagram of the operation of the saturation calculation module of FIG. 14.
- FIG. 20 illustrates a block diagram of the operations of the pulse rate calculation module of FIG. 14.
- FIG. 21 illustrates a block diagram of the operations of the motion artifact suppression module of FIG. 20.
- FIG. 21 a illustrates an alternative block diagram for the operations of the motion artifact suppression module of FIG. 20.
- FIG. 22 illustrates a saturation transform curve in accordance with the principles of the present invention.
- FIG. 23 illustrates a block diagram of an alternative embodiment to the saturation transform in order to obtain a saturation value.
- FIG. 24 illustrates a histogram saturation transform in accordance with the alternative embodiment of FIG. 23.
- FIGS. 25 A- 25 C illustrate yet another alternative embodiment in order to obtain the saturation.
- the measured signal comprises a primary portion s ⁇ a (t) and a secondary portion n ⁇ a (t).
- the measured signal comprises a primary portion s ⁇ b (t) and a secondary portion n ⁇ b (t).
- FIG. 28 illustrates the secondary reference n′(t) determined by a processor of the present invention.
- FIG. 31 depicts a set of 3 concentric electrodes, i.e., a tripolar electrode sensor, to derive electrocardiography (ECG) signals, denoted as S 1 , S 2 and S 3 , for use with the present invention.
- ECG electrocardiography
- Each of the ECG signals contains a primary portion and a secondary portion.
- the present invention involves a system which utilizes first and second measured signals that each contain a primary signal portion and a secondary signal portion.
- the output of the system provides a good approximation n′′(t) to the secondary signal portion n(t) or a good approximation s′′(t) to the primary signal portion s(t).
- the system of the present invention is particularly useful where the primary and/or secondary signal portion n(t) may contain one or more of a constant portion, a predictable portion, an erratic portion, a random portion, etc.
- the primary signal approximation s′′(t) or secondary signal approximation n′′(t) is derived by removing as many of the secondary signal portions n(t) or primary signal portions s(t) from the composite signal S(t) as possible. The remaining signal forms either the primary signal approximation s′′(t) or secondary signal approximation n′′(t), respectively.
- the constant portion and predictable portion of the secondary signal n(t) are easily removed with traditional filtering techniques, such as simple subtraction, low pass, band pass, and high pass filtering.
- Traditional filtering techniques such as simple subtraction, low pass, band pass, and high pass filtering.
- the erratic portion is more difficult to remove due to its unpredictable nature. If something is known about the erratic signal, even statistically, it could be removed, at least partially, from the measured signal via traditional filtering techniques. However, often no information is known about the erratic portion of the secondary signal n(t). In this case, traditional filtering techniques are usually insufficient.
- a signal model in accordance with the present invention is defined as follows for the first and second measured signals S 1 and S 2 :
- S 1 s 1 + n 1
- s 1 and n 1 are at least somewhat (preferably substantially) uncorrelated and s 2 and n 2 are at least somewhat (preferably substantially) uncorrelated.
- the first and second measured signals S 1 and S 2 are related by correlation coefficients r a and r v as defined above. The use and selection of these coefficients is described in further detail below.
- this signal model is used in combination with a correlation canceler, such as an adaptive noise canceler, to remove or derive the erratic portion of the measured signals.
- a correlation canceler such as an adaptive noise canceler
- a correlation canceler has two signal inputs and one output.
- One of the inputs is either the secondary reference n′(t) or the primary reference s′(t) which are correlated, respectively, to the secondary signal portions n(t) and the primary signal portions s(t) present in the composite signal S(t).
- the other input is for the composite signal S(t).
- the output of the correlation canceler s′′(t) or n′′(t) corresponds, respectively, to the primary signal s(t) or the secondary signal n(t) portions only.
- a secondary reference n′(t) or a primary reference s′(t) is determined from two composite signals measured simultaneously, or nearly simultaneously, at two different wavelengths, ⁇ a and ⁇ b.
- FIGS. 4 a and 4 b A block diagram of a generic monitor incorporating a signal processor according to the present invention, and a correlation canceler is shown in FIGS. 4 a and 4 b.
- Two measured signals, S ⁇ a (t) and S ⁇ b (t) are acquired by a detector 20 .
- Each signal is conditioned by a signal conditioner 22 a and 22 b. Conditioning includes, but is not limited to, such procedures as filtering the signals to remove constant portions and amplifying the signals for ease of manipulation.
- the signals are then converted to digital data by an analog-to-digital converter 24 a and 24 b.
- the first measured signal S ⁇ a (t) comprises a first primary signal portion, labeled herein s ⁇ a (t), and a first secondary signal portion, labeled herein n ⁇ a (t).
- the second measured signal S ⁇ b (t) is at least partially correlated to the first measured signal S ⁇ a (t) and comprises a second primary signal portion, labeled herein s ⁇ b (t), and a second secondary signal portion, labeled herein n ⁇ b (t).
- the first and second secondary signal portions, n ⁇ a (t) and n ⁇ b (t) are uncorrelated and/or erratic with respect to the primary signal portions s ⁇ a (t) and s ⁇ b (t).
- the secondary signal portions n ⁇ a (t) and n ⁇ b (t) are often caused by motion of a patient in physiological measurements.
- the signals S ⁇ a (t) and S ⁇ b (t) are input to a reference processor 26 .
- the signal coefficient factors r a and r v are determined to cause either the primary signal portions s ⁇ a (t) and s ⁇ b (t) or the secondary signal portions n ⁇ a (t) and n ⁇ b (t) to cancel, respectively, when the two signals S ⁇ a (t) and S ⁇ b (t) are subtracted.
- a reference signal n′(t) or s′(t) is input, along with one of the measured signals S ⁇ a (t) or S ⁇ b (t), to a correlation canceler 27 which uses the reference signal n′(t) or s′(t) to remove either the secondary signal portions n ⁇ a (t) or n ⁇ b (t) or the primary signal portions s ⁇ a (t) or s ⁇ b (t) from the measured signal S ⁇ a (t) or S ⁇ b (t).
- the output of the correlation canceler 27 is a good primary signal approximation s′′(t) or secondary signal approximation n′′(t). In one embodiment, the approximation s′′(t) or n′′(t) is displayed on a display 28 .
- an adaptive noise canceler 30 is employed as the correlation canceler 27 , to remove either one of the erratic, secondary signal portions n ⁇ a (t) and n ⁇ b (t) from the first and second signals S ⁇ a (t) and S ⁇ b (t).
- the adaptive noise canceler 30 in FIG. 5 a has as one input a sample of the secondary reference n′(t) which is correlated to the secondary signal portions n ⁇ a (t) and n ⁇ b (t).
- the secondary reference n′(t) is determined from the two measured signals S ⁇ a (t) and S ⁇ b (t) by the processor 26 of the present invention as described herein.
- the adaptive noise canceler 30 in FIG. 5 b, may also be employed to remove either one of primary signal portions s ⁇ a (t) and s ⁇ b (t) from the first and second measured signals S ⁇ a (t) and S ⁇ b (t).
- the adaptive noise canceler 30 has as one input a sample of the primary reference s′(t) which is correlated to the primary signal portions s ⁇ a (t) and s ⁇ b (t).
- the primary reference s′(t) is determined from the two measured signals S ⁇ a (t) and S ⁇ b (t) by the processor 26 of the present invention as described herein.
- the adaptive noise canceler 30 functions to remove frequencies common to both the reference n′(t) or s′(t) and the measured signal S ⁇ a (t) or S ⁇ b (t). Since the reference signals are correlated to either the secondary signal portions n ⁇ a (t) and n ⁇ b (t) or the primary signal portions s ⁇ a (t) and s ⁇ b (t), the reference signals will be correspondingly erratic or well behaved.
- the adaptive noise canceler 30 acts in a manner which may be analogized to a dynamic multiple notch filter based on the spectral distribution of the reference signal n′(t) or s′(t).
- FIG. 5 c illustrates an exemplary transfer function of a multiple notch filter.
- the notches, or dips in the amplitude of the transfer function, indicate frequencies which are attenuated or removed when a signal passes through the notch filter.
- the output of the notch filter is the composite signal having frequencies at which a notch is present removed.
- the frequencies at which notches are present change continuously based upon the inputs to the adaptive noise canceler 30 .
- the adaptive noise canceler 30 (FIGS. 5 a and 5 b ) produces an output signal, labeled herein as s′′ ⁇ a (t), s′′ ⁇ b (t), n′′ ⁇ a (t) or n′′ ⁇ b (t) which is fed back to an internal processor 32 within the adaptive noise canceler 30 .
- the internal processor 32 automatically adjusts its own transfer function according to a predetermined algorithm such that the output of the internal processor 32 labeled b ⁇ (t) in FIG. 5 a and c ⁇ (t) in FIG.
- the internal processor optimizes s′′ ⁇ a (t) or s′′ ⁇ b (t) such that s′′ ⁇ a (t) or s′′ ⁇ b (t) is approximately equal to the primary signal s ⁇ a (t) or s ⁇ b (t), respectively.
- the internal processor optimizes n′′ ⁇ a (t) or n′′ ⁇ b (t) such that n′′ ⁇ a (t) or n′′ ⁇ b (t) is approximately equal to the secondary signal portion n ⁇ a (t) or n ⁇ b (t), respectively.
- One algorithm which may be used for the adjustment of the transfer function of the internal processor 32 is a least-squares algorithm, as described in Chapter 6 and Chapter 12 of the book Adaptive Signal Processing by Bernard Widrow and Samuel Stearns, published by Prentice Hall, copyright 1985. This entire book, including Chapters 6 and 12, is hereby incorporated herein by reference.
- Adaptive processors 30 in FIGS. 5 a and 5 b have been successfully applied to a number of problems including antenna sidelobe canceling, pattern recognition, the elimination of periodic interference in general, and the elimination of echoes on long distance telephone transmission lines.
- considerable ingenuity is often required to find a suitable reference signal n′(t) or s′(t) since the portions n ⁇ a (t), n ⁇ b (t), s ⁇ a (t) and s ⁇ b (t) cannot easily be separated from the measured composite signals S ⁇ a (t) and S ⁇ b (t). If either the actual secondary portion n ⁇ a (t) or n ⁇ b (t) or the primary signal portion s ⁇ a (t) or s ⁇ b (t) were a priori available, techniques such as correlation cancellation would not be necessary.
- a first signal is measured at, for example, a wavelength ⁇ a, by a detector yielding a signal S ⁇ a (t):
- s ⁇ a (t) is the primary signal portion and n ⁇ a (t) is the secondary signal portion.
- the measured signals S ⁇ a (t) and S ⁇ b (t) are transformed to eliminate, respectively, the primary or secondary signal components.
- one way of doing this is to find proportionality constants, r a and r v , between the primary signal portions s ⁇ a (t) and s ⁇ b (t) and the secondary signal portions n ⁇ a (t) and n ⁇ b (t) such that the signals can be modeled as follows:
- a non-zero signal which is correlated to each secondary signal portion n ⁇ a (t) and n ⁇ b (t) and can be used as the secondary reference n′(t) in a correlation canceler such as an adaptive noise canceler.
- a non-zero signal which is correlated to each of the primary signal portions s ⁇ a (t) and s ⁇ b (t) and can be used as the signal reference s′(t) in a correlation canceler such as an adaptive noise canceler.
- Correlation canceling is particularly useful in a large number of measurements generally described as absorption measurements.
- An example of an absorption type monitor which can advantageously employ correlation canceling, such as adaptive noise canceling, based upon a reference n′(t) or s′(t) determined by a processor of the present invention is one which determines the concentration of an energy absorbing constituent within an absorbing material when the material is subject to change. Such changes can be caused by forces about which information is desired or primary, or alternatively, by random or erratic secondary forces such as a mechanical force on the material. Random or erratic interference, such as motion, generates secondary components in the measured signal. These secondary components can be removed or derived by the correlation canceler if a suitable secondary reference n′(t) or primary reference s′(t) is known.
- FIG. 6 a A schematic N constituent absorbing material comprising a container 42 having N different absorbing constituents, labeled A 1 , A 2 , A 3 , . . . A N , is shown in FIG. 6 a.
- the constituents A 1 through A N in FIG. 6 a are arranged in a generally orderly, layered fashion within the container 42 .
- An example of a particular type of absorptive system is one in which light energy passes through the container 42 and is absorbed according to the generalized Beer-Lambert Law of light absorption.
- I 0 is the incident light energy intensity
- I is the transmitted light energy intensity
- ⁇ i, ⁇ a is the absorption coefficient of the i th constituent at the wavelength ⁇ a
- x i (t) is the optical path length of i th layer, i.e., the thickness of material of the i th layer through which optical energy passes
- c i (t) is the concentration of the i th constituent in the volume associated with the thickness x i (t).
- the absorption coefficients ⁇ 1 through ⁇ N are known values which are constant at each wavelength. Most concentrations c 1 (t) through c N (t) are typically unknown, as are most of the optical path lengths x i (t) of each layer.
- the total optical path length is the sum of each of the individual optical path lengths x i (t) of each layer.
- the optical path length of each layer, x i (t) is generally constant. This results in generally constant attenuation of the optical energy and thus, a generally constant offset in the measured signal. Typically, this offset portion of the signal is of little interest since knowledge about a force which perturbs the material is usually desired. Any signal portion outside of a known bandwidth of interest, including the constant undesired signal portion resulting from the generally constant absorption of the constituents when not subject to change, is removed. This is easily accomplished by traditional band pass filtering techniques. However, when the material is subject to forces, each layer of constituents may be affected by the perturbation differently than other layers.
- Some perturbations of the optical path lengths of each layer x i (t) may result in excursions in the measured signal which represent desired or primary information. Other perturbations of the optical path length of each layer x i (t) cause undesired or secondary excursions which mask primary information in the measured signal. Secondary signal components associated with secondary excursions must also be removed to obtain primary information from the measured signal. Similarly, the ability to compute secondary signal components caused by secondary excursions directly allows one to obtain primary signal components from the measured signal via simple subtraction, or correlation cancellation techniques.
- the correlation canceler may selectively remove from the composite signal, measured after being transmitted through or reflected from the absorbing material, either the secondary or the primary signal components caused by forces which perturb or change the material differently from the forces which perturbed or changed the material to cause respectively, either the primary or secondary signal component.
- the portion of the measured signal which is deemed to be the primary signal s ⁇ a (t) is the attenuation term ⁇ 5 c 5 x 5 (t) associated with a constituent of interest, namely A 5 , and that the layer of constituent A 5 is affected by perturbations different than each of the layers of other constituents A 1 through A 4 and A 6 through A N .
- the secondary signal component n ⁇ a (t) can be either removed or derived via a correlation canceler, such as an adaptive noise canceler, having as one input, respectively, a secondary reference n′(t) or a primary reference s′(t) determined by a processor of the present invention as long as the perturbation on layers other than the layer of constituent A 5 is different than the perturbation on the layer of constituent A 5 .
- the correlation canceler yields a good approximation to either the primary signal s ⁇ a (t) or the secondary signal n ⁇ a (t).
- the concentration of the constituent of interest, c 5 (t) can often be determined since in some physiological measurements, the thickness of the primary signal component, x 5 (t) in this example, is known or can be determined.
- the correlation canceler utilizes either the secondary reference n′(t) or the primary reference s′(t) determined from two substantially simultaneously measured signals S ⁇ a (t) and S ⁇ b (t).
- S ⁇ a (t) is determined as above in equation (7).
- S ⁇ b (t) is determined similarly at a different wavelength ⁇ b.
- S ⁇ a(t) ⁇ 5, ⁇ a c 5 x 5 ( t )+ n ⁇ a ( t ) (9)
- a sample of either the secondary reference n′(t) or the primary reference s′(t), and a sample of either measured signal S ⁇ a (t) or S ⁇ b (t), are input to a correlation canceler 27 , such as an adaptive noise canceler 30 , an example of which is shown in FIGS. 5 a and 5 b and a preferred example of which is discussed herein under the heading PREFERRED CORRELATION CANCELER USING A JOINT PROCESS ESTIMATOR IMPLEMENTATION.
- the correlation canceler 27 removes either the secondary portion n ⁇ a (t) or n ⁇ b (t), or the primary portions, s ⁇ a (t) or s ⁇ b (t), of the measured signal yielding a good approximation to either the primary signals s′′ ⁇ a (t) ⁇ 5, ⁇ a c 5 x 5 (t) or s′′ ⁇ b (t) ⁇ 5, ⁇ b c 5 x 5 (t) or the secondary signals n′′ ⁇ a (t) ⁇ n ⁇ a (t) or n′′ ⁇ b (t) ⁇ n ⁇ b (t).
- the concentration c 5 (t) may then be determined from the approximation to the primary signal s′′ ⁇ a (t) or s′′ ⁇ b (t) according to:
- the absorption coefficients are constant at each wavelength ⁇ a and ⁇ b and the thickness of the primary signal component, x 5 (t) in this example, is often known or can be determined as a function of time, thereby allowing calculation of the concentration c 5 (t) of constituent A 5 .
- FIG. 6 b another material having N different constituents arranged in layers is shown.
- This is analogous to combining the layers of constituents A 5 and A 6 in FIG. 6 a.
- a combination of layers, such as the combination of layers of constituents A 5 and A 6, is feasible when the two layers are under the same total forces which result in the same change of the optical path lengths x 5 (t) and x 6 (t) of the layers.
- concentration or the saturation i.e., a percent concentration
- a determination of the concentration or the saturation of a constituent within a given volume may be made with any number of constituents in the volume subject to the same total forces and therefore under the same perturbation or change.
- To determine the saturation of one constituent in a volume comprising many constituents as many measured signals as there are constituents which absorb incident light energy are necessary. It will be understood that constituents which do not absorb light energy are not consequential in the determination of saturation.
- concentration as many signals as there are constituents which absorb incident light energy are necessary as well as information about the sum of concentrations.
- the measured signals S ⁇ a (t) and S ⁇ b (t) can be written (logarithm converted) as:
- the primary signals s ⁇ a (t) and s ⁇ b (t) again comprise terms related to both A 5 and A 6 and portions of the secondary signals n ⁇ a (t) and n ⁇ b (t) comprise terms related to both A 3 and A 4 .
- the primary and secondary signals according to this model may be written as:
- n ⁇ a ( t ) [ ⁇ 5, ⁇ a c 3 + ⁇ 6, ⁇ a c 4 ]x 3,4 ( t )+ n ⁇ a ( t ) (20c)
- signals n ⁇ a (t) and n ⁇ b (t) are similar to the secondary signals n ⁇ a (t) and n ⁇ b (t) except for the omission of the 3, 4 layer.
- any signal portions whether primary or secondary, outside of a known bandwidth of interest, including the constant undesired secondary signal portion resulting from the generally constant absorption of the constituents when not under perturbation, should be removed to determine an approximation to either the primary signal or the secondary signal within the bandwidth of interest. This is easily accomplished by traditional band pass filtering techniques. As in the previous example, it is often the case that the total perturbation or change affecting the layers associated with the secondary signal components is caused by random or erratic forces, causing the thickness of each layer, or the optical path length of each layer, x i (t), to change erratically, producing a random or erratic secondary signal component n ⁇ a (t).
- the secondary signal component n ⁇ a (t) can be removed or derived via a correlation canceler, such as an adaptive noise canceler, having as one input a secondary reference n′(t) or a primary reference s′(t) determined by a processor of the present invention as long as the perturbation in layers other than the layer of constituents A 5 and A 6 is different than the perturbation in the layer of constituents A 5 and A 6 .
- a correlation canceler such as an adaptive noise canceler
- Either the erratic secondary signal components n ⁇ a (t) and n ⁇ b (t) or the primary components s ⁇ a (t) and s ⁇ b (t) may advantageously be removed from equations (18) and (19), or alternatively equations (20) and (21), by a correlation canceler.
- the correlation canceler again, requires a sample of either the primary reference s′(t) or the secondary reference n′(t) and a sample of either of the composite signals S ⁇ a (t) or S ⁇ b (t) of equations (18) and (19).
- One method for determining reference signals s′(t) or n′(t) from the measured signals S ⁇ a (t) and S ⁇ b (t) in accordance with one aspect of the invention is what will be referred to as the constant saturation approach.
- the saturation of A 5 in the volume containing A 5 and A 6 and the saturation of A 3 in the volume containing A 3 and A 4 remains relatively constant over some period of time, i.e.:
- the approximations to either the primary signals s ⁇ a (t) and s ⁇ b (t) or the secondary signals n ⁇ a (t) and n ⁇ b (t), found by the correlation canceler for a substantially immediately preceding set of samples of the measured signals S ⁇ a (t) and S ⁇ b (t) are used in a processor of the present invention for calculating the proportionality constants, r a and r v , for the next set of samples of the measured signals S ⁇ a (t) and S ⁇ b (t).
- initial proportionality coefficients can be determined as further explained below. It is not necessary for the patient to remain motionless even for an initialization period.
- a correlation canceler may be utilized with a secondary reference n′(t) or a primary reference s′(t).
- r r 1 , r 2 , . . . r n as shown in FIG. 7 a.
- a plurality of signal coefficients are chosen to represent a cross section of possible signal coefficients.
- signal coefficients r a and r v are determined from the plurality of assumed signal coefficients r 1 , r 2 , . . . r n .
- the coefficients r a and r v are selected such that they cause either the primary signal portions s ⁇ a (t) and s ⁇ b (t) or the secondary signal portions n ⁇ a (t) and n ⁇ b (t) to cancel or nearly cancel when they are substituted into the reference function R′(r, t), e. g.
- coefficients r a and r v are selected at values which reflect the minimum of correlation between the primary signal portions and the secondary signal portions.
- One approach to determine the signal coefficients r a and r v from the plurality of coefficients r 1 , r 2 , . . . r n employs the use of a correlation canceler 27 , such as an adaptive noise canceler, which takes a first input which corresponds to one of the measured signals S ⁇ a (t) or S ⁇ b (t) and takes a second input which corresponds to successively each one of the plurality of reference signals R′(r 1 , t), R′(r 2 , t), . . . , R′(r n , t) as shown in FIG. 7 a.
- a correlation canceler 27 such as an adaptive noise canceler
- the corresponding output of the correlation canceler 27 is input to a “squares” operation 28 which squares the output of the correlation canceler 27 .
- the output of the squares operation 28 is provided to an integrator 29 for forming a cumulative output signal (a summation of the squares).
- the cumulative output signal is subsequently input to an extremum detector 31 .
- the purpose of the extremum detector 31 is to chose signal coefficients r a and r v from the set r 1 , r 2 , . . . r n by observing which provide a maximum in the cumulative output signal as in FIGS.
- coefficients which provide a maximum integrated output, such as energy or power, from the correlation canceler 27 correspond to the signal coefficients r a and r v which relate to a minimum correlation between the primary signal portions and the secondary signal portions in accordance with the signal model of the present invention.
- R′ ( r, t ) S ⁇ a ( t ) ⁇ rS ⁇ b ( t )+ n ⁇ a (36)
- the time variable, t, of the correlation canceler output C(S ⁇ a (t), R′(r j , t)) may be eliminated by computing its energy or power.
- the energy of the correlation canceler output is given by
- discrete time measurement signals may be employed as well as continuous time measurement signals.
- a system which performs a discrete transform e.g., a saturation transform in the present example
- integration approximation methods such as the trapezoid rule, midpoint rule, Tick's rule, Simpson's approximation or other techniques may be used to compute the correlation canceler energy or power output.
- t i is the i th discrete time
- t 0 is the initial time
- t n is the final time
- ⁇ t is the time between discrete time measurement samples.
- the energy functions given above, and shown in FIG. 7 b, indicate that the correlation canceler output is usually zero due to correlation between the measured signal S ⁇ a (t) or S ⁇ b (t) and many of the plurality of reference signals R′(r 1 , t), R′(r 2 , t), . . . , R′(r n , t).
- the energy functions are non zero at values of r j which correspond to cancellation of either the primary signal portions s ⁇ a (t) and s ⁇ b (t) or the secondary signal portions n ⁇ a (t) and n ⁇ b (t) in the reference signal R′(r j , t). These values correspond to the signal coefficients r a and r v .
- reference signal techniques together with a correlation cancellation can be employed to decompose a signal into two or more signal components each of which is related by a ratio.
- the correlation canceler can be implemented in either hardware or software.
- the preferred implementation of a correlation canceler is that of an adaptive noise canceler using a joint process estimator.
- the least mean squares (LMS) implementation of the internal processor 32 described above in conjunction with the adaptive noise canceler of FIG. 5 a 5 b is relatively easy to implement, but lacks the speed of adaptation desirable for most physiological monitoring applications of the present invention.
- LMS least mean squares
- a faster approach for adaptive noise canceling called a least-squares lattice joint process estimator model, is used in one embodiment.
- a joint process estimator 60 is shown diagrammatically in FIG. 8 and is described in detail in Chapter 9 of Adaptive Filter Theory by Simon Haykin, published by Prentice-Hall, copyright 1986. This entire book, including Chapter 9, is hereby incorporated herein by reference.
- the function of the joint process estimator is to remove either the secondary signal portions n ⁇ a (t) or n ⁇ b (t) or the primary signal portions s ⁇ a (t) or s ⁇ b (t) from the measured signals S ⁇ a (t) or S ⁇ b (t), yielding either a primary signal approximation s′′ ⁇ a (t) or s′′ ⁇ b (t) or a secondary signal approximation n′′ ⁇ a (t) or n′′ ⁇ b (t).
- the joint process estimator estimates either the value of the primary signals s ⁇ a (t) or s ⁇ b (t) or the secondary signals n ⁇ a (t) or n ⁇ b (t).
- the inputs to the joint process estimator 60 are either the secondary reference n′(t) or the primary reference s′(t) and the composite measured signal S ⁇ a (t) or S ⁇ b (t).
- the output is a good approximation to the signal S ⁇ a (t) or S ⁇ b (t) with either the secondary signal or the primary signal removed, i.e. a good approximation to either s ⁇ a (t), s ⁇ b (t), n ⁇ a (t) or n ⁇ b (t).
- the joint process estimator 60 of FIG. 8 utilizes, in conjunction, a least square lattice predictor 70 and a regression filter 80 .
- Either the secondary reference n′(t) or the primary reference s′(t) is input to the least square lattice predictor 70 while the measured signal S ⁇ a (t) or S ⁇ b (t) is input to the regression filter 80 .
- S ⁇ a (t) will be the measured signal from which either the primary portion s ⁇ a (t) or the secondary portion n ⁇ a (t) will be estimated by the joint process estimator 60 .
- S ⁇ b (t) could also be input to the regression filter 80 and the primary portion s ⁇ b (t) or the secondary portion n ⁇ b (t) of this signal could be estimated.
- the joint process estimator 60 removes all frequencies that are present in both the reference n′(t) or s′(t), and the measured signal S ⁇ a (t).
- the secondary signal portion n ⁇ a (t) usually comprises frequencies unrelated to those of the primary signal portion s ⁇ a (t). It is improbable that the secondary signal portion n ⁇ a (t) would be of exactly the same spectral content as the primary signal portion s ⁇ a (t). However, in the unlikely event that the spectral content of s ⁇ a (t) and n ⁇ a (t) are similar, this approach will not yield accurate results.
- the joint process estimator 60 compares the reference input signal n′(t) or s′(t), which is correlated to either the secondary signal portion n ⁇ a (t) or the primary signal portion s ⁇ a (t), and input signal S ⁇ a (t) and removes all frequencies which are identical.
- the joint process estimator 60 acts as a dynamic multiple notch filter to remove those frequencies in the secondary signal component n ⁇ a (t) as they change erratically with the motion of the patient or those frequencies in the primary signal component s ⁇ a (t) as they change with the arterial pulsation of the patient.
- the output s′′ ⁇ a (t) or n′′ ⁇ a (t) of the joint process estimator 60 is a very good approximation to either the primary signal s ⁇ a (t) or the secondary signal n ⁇ a (t).
- the joint process estimator 60 can be divided into stages, beginning with a zero-stage and terminating in an m th -stage, as shown in FIG. 8. Each stage, except for the zero-stage, is identical to every other stage.
- the zero-stage is an input stage for the joint process estimator 60 .
- the first stage through the m th -stage work on the signal produced in the immediately previous stage, i.e., the (m ⁇ 1) th -stage, such that a good primary signal approximation s′′ ⁇ a (t) or secondary signal approximation n′′ ⁇ a (t) is produced as output from the m th -stage.
- the least-squares lattice predictor 70 comprises registers 90 and 92 , summing elements 100 and 102 , and delay elements 110 .
- the registers 90 and 92 contain multiplicative values of a forward reflection coefficient ⁇ f,m (t) and a backward reflection coefficient ⁇ b,m (t) which multiply the reference signal n′(t) or s′(t) and signals derived from the reference signal n′(t) or s′(t).
- Each stage of the least-squares lattice predictor outputs a forward prediction error f m (t) and a backward prediction error b m (t).
- the subscript m is indicative of the stage.
- the sample of the reference signal n′(t) or s′(t) is input to the least-squares lattice predictor 70 .
- the zero-stage forward prediction error f 0 (t) and the zero-stage backward prediction error b 0 (t) are set equal to the reference signal n′(t) or s′(t).
- the backward prediction error b 0 (t) is delayed by one sample period by the delay element 110 in the first stage of the least-squares lattice predictor 70 .
- the immediately previous value of the reference n′(t) or s′(t) is used in calculations involving the first-stage delay element 110 .
- the zero-stage forward prediction error is added to the negative of the delayed zero-stage backward prediction error b 0 (t ⁇ 1) multiplied by the forward reflection coefficient value ⁇ f,1 (t) register 90 value, to produce a first-stage forward prediction error f 1 (t).
- the zero-stage forward prediction error f 0 (t) is multiplied by the backward reflection coefficient ⁇ b,1 (t) register 92 value and added to the delayed zero-stage backward prediction error b 0 (t ⁇ 1) to produce a first-stage backward prediction error b 1 (t).
- the previous forward and backward prediction error values, f m ⁇ 1 (t) and b m ⁇ 1 (t ⁇ 1), the backward prediction error being delayed by one sample period, are used to produce values of the forward and backward prediction errors for the present stage, f m (t) and b m (t).
- the backward prediction error b m (t) is fed to the concurrent stage, m, of the regression filter 80 . There it is input to a register 96 , which contains a multiplicative regression coefficient value ⁇ m, ⁇ a (t). For example, in the zero-stage of the regression filter 80 , the zero-stage backward prediction error b 0 (t) is multiplied by the zero-stage regression coefficient ⁇ 0, ⁇ a (t) register 96 value and subtracted from the measured value of the signal S ⁇ a (t) at a summing element 106 to produce a first stage estimation error signal e 1, ⁇ a (t).
- the first-stage estimation error signal e 1, ⁇ a (t) is a first approximation to either the primary signal or the secondary signal.
- This first-stage estimation error signal e 1, ⁇ a (t) is input to the first-stage of the regression filter 80 .
- the first-stage backward prediction error b 1 (t), multiplied by the first-stage regression coefficient ⁇ 1, ⁇ a (t) register 96 value is subtracted from the first-stage estimation error signal e 1, ⁇ a (t) to produce the second-stage estimation error e 2, ⁇ a (t).
- the second-stage estimation error signal e 2, ⁇ a (t) is a second, somewhat better approximation to either the primary signal s ⁇ a (t) or the secondary signal n ⁇ a (t).
- a number of intermediate variables are required to calculate the forward reflection coefficient ⁇ f,m (t), the backward reflection coefficient ⁇ b,m (t), and the regression coefficient ⁇ m, ⁇ a (t) register 90 , 92 , and 96 values.
- Intermediate variables include a weighted sum of the forward prediction error squares I m (t), a weighted sum of the backward prediction error squares ⁇ m (t), a scalar parameter ⁇ m (t), a conversion factor ⁇ m (t), and another scalar parameter ⁇ m, ⁇ a (t).
- ⁇ without a wavelength identifier, a or b is a constant multiplicative value unrelated to wavelength and is typically less than or equal to one, i.e., ⁇ 1.
- ⁇ without a wavelength identifier, a or b is a constant multiplicative value unrelated to wavelength and is typically less than or equal to one, i.e., ⁇ 1.
- the operation of the joint process estimator 60 is as follows.
- the initial values of intermediate variables and signals including the parameter ⁇ m ⁇ 1 (t), the weighted sum of the forward prediction error signals I m ⁇ 1 (t), the weighted sum of the backward prediction error signals ⁇ m ⁇ 1 (t), the parameter ⁇ m, ⁇ a (t), and the zero-stage estimation error e 0, ⁇ a (t) are initialized, some to zero and some to a small positive number ⁇ :
- a simultaneous sample of the measured signal S ⁇ a (t) or S ⁇ b (t) and either the secondary reference n′(t) or the primary reference s′(t) are input to the joint process estimator 60 , as shown in FIG. 8.
- the forward and backward prediction error signals f 0 (t) and b 0 (t), and intermediate variables including the weighted sums of the forward and backward error signals I 0 (t) and ⁇ 0 (t), and the conversion factor ⁇ 0 (t) are calculated for the zero-stage according to:
- Forward reflection coefficient ⁇ f,m (t), backward reflection coefficient ⁇ b,m (t), and regression coefficient ⁇ m, ⁇ a (t) register 90 , 92 and 96 values in each stage thereafter are set according to the output of the previous stage.
- the forward reflection coefficient ⁇ f,1 (t), backward reflection coefficient ⁇ b,1 (t), and regression coefficient ⁇ 1, ⁇ a (t) register 90 , 92 and 96 values in the first stage are thus set according to the algorithm using values in the zero-stage of the joint process estimator 60 .
- intermediate values and register values including the parameter ⁇ m ⁇ 1 (t); the forward reflection coefficient ⁇ f,m (t) register 90 value; the backward reflection coefficient ⁇ b,m (t) register 92 value; the forward and backward error signals f m (t) and b m (t); the weighted sum of squared forward prediction errors I f,m (t), as manipulated in ⁇ 9.3 of the Haykin book; the weighted sum of squared backward prediction errors ⁇ b,m (t), as manipulated in ⁇ 9.3 of the Haykin book; the conversion factor ⁇ m (t); the parameter ⁇ m, ⁇ a (t); the regression coefficient ⁇ m, ⁇ a (t) register 96 value; and the estimation error e m+1 ⁇ a (t) value are set according to:
- ⁇ m ⁇ 1 ( t ) ⁇ m ⁇ 1 ( t ⁇ 1)+ ⁇ b m ⁇ 1 ( t ⁇ 1) f* m ⁇ 1 ( t )/ ⁇ m ⁇ 1 ( t ⁇ 1) ⁇ (54)
- ⁇ f,m ( t ) ⁇ m ⁇ 1 ( t )/ ⁇ m ⁇ 1 ( t ⁇ 1) ⁇ (55)
- ⁇ b,m ( t ) ⁇ * m ⁇ 1 ( t )/I m ⁇ 1 ( t ) ⁇ (56)
- I m ( t ) I m ⁇ 1 ( t ) ⁇
- ⁇ m ( t ) ⁇ m ⁇ 1 ( t ⁇ 1) ⁇
- ⁇ m ( t ⁇ 1) ⁇ m ⁇ 1 ( t ⁇ 1) ⁇
- ⁇ m, ⁇ a ( t ) ⁇ m, ⁇ a ( t ⁇ 1)+ ⁇ b m ( t ) ⁇ * m, ⁇ a ( t )/ ⁇ m ( t ) ⁇ (62)
- a next set of samples including a sample of the measured signal S ⁇ a (t) and a sample of either the secondary reference n′(t) or the primary reference s′(t), are input to the joint process estimator 60 .
- the re-initialization process does not re-occur, such that the forward and backward reflection coefficient ⁇ f,m (t) and ⁇ b,m (t) register 90 , 92 values and the regression coefficient ⁇ m, ⁇ a (t) register 96 value reflect the multiplicative values required to estimate either the primary signal portion s ⁇ a (t) or the secondary signal portion n ⁇ a (t) of the sample of S ⁇ a (t) input previously.
- information from previous samples is used to estimate either the primary or secondary signal portion of a present set of samples in each stage.
- a normalized joint process estimator is used. This version of the joint process estimator normalizes several variables of the above-described joint process estimator such that the normalized variables fall between ⁇ 1 and 1.
- ⁇ overscore ( ⁇ ) ⁇ m ⁇ 1 ( t ) ⁇ overscore ( ⁇ ) ⁇ m ⁇ 1 ( t ⁇ 1)[1 ⁇
- ⁇ m ( t ) ⁇ m ⁇ 1 ( t )[1 ⁇
- N(t) be defined as the reference noise input at time index n
- U(t) be defined as combined signal plus noise input at time index t the following equations apply (see Haykin, p. 619):
- a normalized joint process estimator can be used for a more stable system.
- the correlation cancellation is performed with a QRD algorithm as shown diagrammatically in FIG. 8 a and as described in detail in Chapter 18 of Adaptive Filter Theory by Simon Haykin, published by Prentice-Hall, copyright 1986.
- ⁇ m ⁇ 1 ( t ⁇ 1) ⁇ m ⁇ 1 ( t ⁇ 2)+
- ⁇ f,m ⁇ 1 ⁇ ( t ) c b,m ⁇ 1 ( t ⁇ 1) ⁇ 1 ⁇ 2 ⁇ f,m ⁇ 1 ⁇ ( t ⁇ 1)+ s b,m ⁇ 1 ( t ⁇ 1) ⁇ f,m ⁇ 1 ( t )
- ⁇ m 1 ⁇ 2 ( t ⁇ 1) c b,m ⁇ 1 ( t ⁇ 1) ⁇ m ⁇ 1 1 ⁇ 2 ( t ⁇ 1)
- I m ⁇ 1 ( t ) ⁇ I m ⁇ 1 ( t ⁇ 1)+
- ⁇ b,m ⁇ 1 19 ( t ) c f,m ⁇ 1 ( t ) ⁇ 1 ⁇ 2 ⁇ b,m ⁇ 1 ⁇ ( t ⁇ 1)+ s f,m ⁇ 1 ( t ) ⁇ b,m ⁇ 1 ( t ⁇ 1)
- ⁇ m ( t ) ⁇ m ( t ⁇ 1)+
- a joint process estimator 60 type adaptive noise canceler is generally implemented via a software program having an iterative loop.
- One iteration of the loop is analogous to a single stage of the joint process estimator as shown in FIG. 8.
- a loop is iterated m times, it is equivalent to an m stage joint process estimator 60 .
- FIG. 9 A flow chart of a subroutine to estimate the primary signal portion s ⁇ a (t) or the secondary signal portion n ⁇ a (t) of a measured composite signal, S ⁇ a (t) is shown in FIG. 9.
- the flow chart illustrates the function of a reference processor for determining either the secondary reference n′(t) or the primary reference s′(t).
- the flowchart for the joint process estimator is implemented in software.
- a one-time initialization is performed when the physiological monitor is powered-on, as indicated by an “INITIALIZE NOISE CANCELER” action block 120 .
- the initialization sets all registers 90 , 92 , and 96 and delay element variables 110 to the values described above in equations (46) through (50).
- a set of simultaneous samples of the composite measured signals S ⁇ a (t) and S ⁇ b (t) is input to the subroutine represented by the flowchart in FIG. 9.
- a time update of each of the delay element program variables occurs, as indicated in a “TIME UPDATE OF [Z ⁇ 1 ] ELEMENTS” action block 130 .
- the value stored in each of the delay element variables 110 is set to the value at the input of the delay element variable 110 .
- the zero-stage backward prediction error b 0 (t) is stored as the first-stage delay element variable
- the first-stage backward prediction error b 1 (t) is stored as the second-stage delay element variable, and so on.
- the reference signal is obtained using the ratiometric or the constant saturation methods described above. This is indicated by a “CALCULATE REFERENCE [n′(t) or s′(t)] FOR TWO MEASURED SIGNAL SAMPLES” action block 140 .
- a zero-stage order update is performed next as indicated in a “ZERO-STAGE UPDATE” action block 150 .
- the zero-stage backward prediction error b 0 (t), and the zero-stage forward prediction error f 0 (t) are set equal to the value of the reference signal n′(t) or s′(t).
- the weighted sum of the forward prediction errors I m (t) and the weighted sum of backward prediction errors ⁇ m (t) are set equal to the value defined in equations (47) and (48).
- a maximum value of m defining the total number of stages to be used by the subroutine corresponding to the flowchart in FIG. 9, is also defined.
- the loop is constructed such that it stops iterating once a criterion for convergence upon a best approximation to either the primary signal or the secondary signal has been met by the joint process estimator 60 .
- the forward and backward reflection coefficient ⁇ f,m (t) and ⁇ b,m (t) register 90 and 92 values in the least-squares lattice filter are calculated first, as indicated by the “ORDER UPDATE MTH CELL OF LSL-LATTICE” action block 170 in FIG. 9. This requires calculation of intermediate variable and signal values used in determining register 90 , 92 , and 96 values in the present stage, the next stage, and in the regression filter 80 .
- the calculation of regression filter register 96 value ⁇ m, ⁇ a (t) is performed next, indicated by the “ORDER UPDATE MTH STAGE OF REGRESSION FILTER(S)” action block 180 .
- convergence is determined by checking if the weighted sums of the forward and backward prediction errors I m (t) and ⁇ m (t) are less than a small positive number.
- An output is calculated next, as indicated by a “CALCULATE OUTPUT” action block 200 .
- the output is a good approximation to either the primary signal or secondary signal, as determined by the reference processor 26 and joint process estimator 60 subroutine corresponding to the flow chart of FIG. 9. This is displayed (or used in a calculation in another subroutine), as indicated by a “TO DISPLAY” action block 210 .
- a new set of samples of the two measured signals S ⁇ a (t) and S ⁇ b (t) is input to the processor and joint process estimator 60 adaptive noise canceler subroutine corresponding to the flowchart of FIG. 9 and the process reiterates for these samples. Note, however, that the initialization process does not re-occur.
- New sets of measured signal samples S ⁇ a (t) and S ⁇ b (t) are continuously input to the reference processor 26 and joint process estimator adaptive noise canceler subroutine.
- the output forms a chain of samples which is representative of a continuous wave.
- This waveform is a good approximation to either the primary signal waveform s ⁇ a (t) or the secondary waveform n ⁇ a (t) at wavelength ⁇ a.
- the waveform may also be a good approximation to either the primary signal waveform s ⁇ b (t) or the secondary waveform n′′ ⁇ b (t) at wavelength ⁇ b.
- FIG. 9 a A corresponding flowchart for the QRD algorithm of FIG. 8 a is depicted in FIG. 9 a, with reference numeral corresponding in number with an ‘a’ extension
- Physiological monitors may use the approximation of the primary signals s′′ ⁇ a (t) or s′′ ⁇ b (t) or the secondary signals n′′ ⁇ a (t) or n′′ ⁇ b (t) to calculate another quantity, such as the saturation of one constituent in a volume containing that constituent plus one or more other constituents.
- another quantity such as the saturation of one constituent in a volume containing that constituent plus one or more other constituents.
- such calculations require information about either a primary or secondary signal at two wavelengths.
- the constant saturation method requires a good approximation of the primary signal portions s ⁇ a (t) and s ⁇ b (t) of both measured signals S ⁇ a (t) and S ⁇ b (t).
- the arterial saturation is determined from the approximations to both signals, i.e.
- the constant saturation method also requires a good approximation of the secondary signal portions n ⁇ a (t) or n ⁇ b (t).
- An estimate of the venous saturation may be determined from the approximations to these signals i.e. n′′ ⁇ a (t) and n′′ ⁇ b (t).
- a joint process estimator 60 having two regression filters 80 a and 80 b is shown in FIG. 10.
- a first regression filter 80 a accepts a measured signal S ⁇ a (t).
- a second regression filter 80 b accepts a measured signal S ⁇ b (t) for a use of the constant saturation method to determine the reference signal n′(t) or s′(t).
- the first and second regression filters 80 a and 80 b are independent.
- the backward prediction error b m (t) is input to each regression filter 80 a and 80 b, the input for the second regression filter 80 b bypassing the first regression filter 80 a.
- the second regression filter 80 b comprises registers 98 , and summing elements 108 arranged similarly to those in the first regression filter 80 a.
- the second regression filter 80 b operates via an additional intermediate variable in conjunction with those defined by equations (54) through (64), i.e.:
- ⁇ m, ⁇ b ( t ) ⁇ ⁇ m, ⁇ b ( t ⁇ 1)+ ⁇ b m ( t ) e* m, ⁇ b ( t )/ ⁇ m ( t ) ⁇ ; and (65)
- the second regression filter 80 b has an error signal value defined similar to the first regression filter error signal values, e m+1, ⁇ a (t), i.e.:
- the second regression filter has a regression coefficient ⁇ m, ⁇ b (t) register 98 value defined similarly to the first regression filter error signal values, i.e.:
- S ⁇ b (t) is input to the second regression filter 80 b.
- the output is then a good approximation to the primary signal s′′ ⁇ b (t) or secondary signal s′′ ⁇ b (t).
- the addition of the second regression filter 80 b does not substantially change the computer program subroutine represented by the flowchart of FIG. 9. Instead of an order update of the m th stage of only one regression filter, an order update of the m th stage of both regression filters 80 a and 80 b is performed. This is characterized by the plural designation in the “ORDER UPDATE OF m th STAGE OF REGRESSION FILTER(S)” activity block 180 in FIG. 9. Since the regression filters 80 a and 80 b operate independently, independent calculations can be performed in the reference processor and joint process estimator 60 adaptive noise canceler subroutine modeled by the flowchart of FIG. 9.
- FIG. 10 a An alternative diagram for the joint process estimator of FIG. 10, using the QRD algorithm and having two regression filters is shown in FIG. 10 a. This type of joint process estimator would be used for correlation cancellation using the QRD algorithm described in the Haykin book.
- the saturation of A 5 in a volume containing A 5 and A 6 may be calculated according to various known methods.
- the approximations to the primary signals can be written in terms of ⁇ a and ⁇ b, as:
- Equations (70) and (71) are equivalent to two equations having three unknowns, namely c 5 (t), c 6 (t) and x 5,6 (t).
- the saturation can be determined by acquiring approximations to the primary or secondary signal portions at two different, yet proximate times t 1 and t 2 over which the saturation of A 5 in the volume containing A 5 and A 6 and the saturation of A 3 in the volume containing A 3 and A 4 does not change substantially.
- the primary signals estimated at times t 1 and t 2 are estimated at times t 1 and t 2 :
- difference signals may be determined which relate the signals of equations (72) through (75), i.e.:
- ⁇ s ⁇ a s′′ ⁇ a ( t 1 ) ⁇ s′′ ⁇ a ( t 2 ) ⁇ 5, ⁇ a c 5 ⁇ x+ ⁇ 6, ⁇ a c 6 ⁇ x; (76)
- ⁇ s ⁇ b s′′ ⁇ b ( t 1 ) ⁇ s′′ ⁇ b ( t 2 ) ⁇ 5, ⁇ b c 5 ⁇ x+ ⁇ , ⁇ b c 6 ⁇ x; (77)
- a specific example of a physiological monitor utilizing a processor of the present invention to determine a secondary reference n′(t) for input to a correlation canceler that removes erratic motion-induced secondary signal portions is a pulse oximeter.
- Pulse oximetry may also be performed utilizing a processor of the present invention to determine a primary signal reference s′(t) which may be used for display purposes or for input to a correlation canceler to derive information about patient movement and venous blood oxygen saturation.
- a pulse oximeter typically causes energy to propagate through a medium where blood flows close to the surface for example, an ear lobe, or a digit such as a finger, a forehead or a fetus' scalp.
- An attenuated signal is measured after propagation through or reflected from the medium.
- the pulse oximeter estimates the saturation of oxygenated blood.
- Freshly oxygenated blood is pumped at high pressure from the heart into the arteries for use by the body.
- the volume of blood in the arteries varies with the heartbeat, giving rise to a variation in absorption of energy at the rate of the heartbeat, or the pulse.
- Oxygen depleted, or deoxygenated, blood is returned to the heart by the veins along with unused oxygenated blood.
- the volume of blood in the veins varies with the rate of breathing, which is typically much slower than the heartbeat.
- venous blood causes a low frequency variation in absorption of energy.
- the low frequency variation in absorption is coupled with the erratic variation in absorption due to motion artifact.
- two light emitting diodes are positioned on one side of a portion of the body where blood flows close to the surface, such as a finger, and a photodetector is positioned on the opposite side of the finger.
- a visible wavelength preferably red
- the other LED emits an infrared wavelength.
- the finger comprises skin, tissue, muscle, both arterial blood and venous blood, fat, etc., each of which absorbs light energy differently due to different absorption coefficients, different concentrations, different thicknesses, and changing optical pathlengths.
- FIG. 11 depicts a general hardware block diagram of a pulse oximeter 299 .
- a sensor 300 has two light emitters 301 and 302 such as LED's. One LED 301 emitting light of red wavelengths and another LED 302 emitting light of infrared wavelengths are placed adjacent a finger 310 .
- a photodetector 320 which produces an electrical signal corresponding to the attenuated visible and infrared light energy signals is located opposite the LED's 301 and 302 .
- the photodetector 320 is connected to front end analog signal conditioning circuitry 330 .
- the front end analog signal conditioning circuitry 330 has outputs coupled to analog to digital conversion circuit 332 .
- the analog to digital conversion circuitry 332 has outputs coupled to a digital signal processing system 334 .
- the digital signal processing system 334 provides the desired parameters as outputs for a display 336 .
- Outputs for display are, for example, blood oxygen saturation, heart rate, and a clean plethysmographic waveform.
- the signal processing system also provides an emitter current control output 337 to a digital-to-analog converter circuit 338 which provides control information for light emitter drivers 340 .
- the light emitter drivers 340 couple to the light emitters 301 , 302 .
- the digital signal processing system 334 also provides a gain control output 342 for the front end analog signal conditioning circuitry 330 .
- FIG. 11 a illustrates a preferred embodiment for the combination of the emitter drivers 340 and the digital to analog conversion circuit 338 .
- the driver comprises first and second input latches 321 , 322 , a synchronizing latch 323 , a voltage reference 324 , a digital to analog conversion circuit 325 , first and second switch banks 326 , 327 , first and second voltage to current converters 328 , 329 and the LED emitters 301 , 302 corresponding to the LED emitters 301 , 302 of FIG. 11.
- the preferred driver depicted in FIG. 11 a is advantageous in that the present inventors recognized that much of the noise in the oximeter 299 of FIG. 11 is caused by the LED emitters 301 , 302 . Therefore, the emitter driver circuit of FIG. 11 a is designed to minimize the noise from the emitters 301 , 302 .
- the first and second input latches 321 , 324 are connected directly to the DSP bus. Therefore, these latches significantly minimizes the bandwidth (resulting in noise) present on the DSP bus which passes through to the driver circuitry of FIG. 11 a.
- the output of the first and second input latches only changes when these latched detect their address on the DSP bus.
- the first input latch receives the setting for the digital to analog converter circuit 325 .
- the second input latch receives switching control data for the switch banks 326 , 327 .
- the synchronizing latch accepts the synchronizing pulses which maintain synchronization between the activation of emitters 301 , 302 and the analog to digital conversion circuit 332 .
- the voltage reference is also chosen as a low noise DC voltage reference for the digital to analog conversion circuit 325 .
- the voltage reference has an lowpass output filter with a very low corner frequency (e.g., 1 Hz in the present embodiment).
- the digital to analog converter 325 also has a lowpass filter at its output with a very low corner frequency (e.g., 1 Hz).
- the digital to analog converter provides signals for each of the emitters 301 , 302 .
- the output of the voltage to current converters 328 , 329 are switched such that with the emitters 301 , 302 connected in back-to-back configuration, only one emitter is active an any given time.
- the voltage to current converter for the inactive emitter is switched off at its input as well, such that it is completely deactivated. This reduces noise from the switching and voltage to current conversion circuitry.
- low noise voltage to current converters are selected (e.g., Op 27 Op Amps), and the feedback loop is configured to have a low pass filter to reduce noise.
- the low pass filtering function of the voltage to current converter 328 , 329 has a corner frequency of just above 625 Hz, which is the switching speed for the emitters, as further discussed below. Accordingly, the preferred driver circuit of FIG. 11 a, minimizes the noise of the emitters 301 , 302 .
- the red and infrared light emitters 301 , 302 each emits energy which is absorbed by the finger 310 and received by the photodetector 320 .
- the photodetector 320 produces an electrical signal which corresponds to the intensity of the light energy striking the photodetector 320 .
- the front end analog signal conditioning circuitry 330 receives the intensity signals and filters and conditions these signals as further described below for further processing.
- the resultant signals are provided to the analog-to-digital conversion circuitry 332 which converts the analog signals to digital signals for further processing by the digital signal processing system 334 .
- the digital signal processing system 334 utilizes the two signals in order to provide a what will be called herein a “saturation transform.” It should be understood, that for parameters other than blood saturation monitoring, the saturation transform could be better termed as a concentration transform, in-vivo transform, or the like, depending on the desired parameter.
- saturation transform is used to describe an operation which converts the sample data from time domain to saturation domain values as will be apparent from the discussion below.
- the output of the digital signal processing system 334 provides clean plethysmographic waveforms of the detected signals and provides values for oxygen saturation and pulse rate to the display 336 .
- the digital signal processing system 334 also provides control for driving the light emitters 301 , 302 with an emitter current control signal on the emitter current control output 337 .
- This value is a digital value which is converted by the digital-to-analog conversion circuit 338 which provides a control signal to the emitter current drivers 340 .
- the emitter current drivers 340 provide the appropriate current drive for the red emitter 301 and the infrared emitter 302 . Further detail of the operation of the physiological monitor for pulse oximetry is explained below.
- the light emitters are driven via the emitter current driver 340 to provide light transmission with digital modulation at 625 Hz.
- the light emitters 301 , 302 are driven at a power level which provides an acceptable intensity for detection by the detector and for conditioning by the front end analog signal conditioning circuitry 330 .
- this energy level is determined for a given patient by the digital signal processing system 334 , the current level for the red and infrared emitters is maintained constant. It should be understood, however, that the current could be adjusted for changes in the ambient room light and other changes which would effect the voltage input to the front end analog signal conditioning circuitry 330 .
- the red and infrared light emitters are modulated as follows: for one complete 625 Hz red cycle, the red emitter 301 is activated for the first quarter cycle, and off for the remaining three-quarters cycle; for one complete 625 Hz infrared cycle, the infrared light emitter 302 is activated for one quarter cycle and is off for the remaining three-quarters cycle.
- the emitters are cycled on and off alternatively, in sequence, with each only active for a quarter cycle per 625 Hz cycle and a quarter cycle separating the active times.
- the light signal is attenuated (amplitude modulated) by the pumping of blood through the finger 310 (or other sample medium).
- the attenuated (amplitude modulated) signal is detected by the photodetector 320 at the 625 Hz carrier frequency for the red and infrared light. Because only a single photodetector is used, the photodetector 320 receives both the red and infrared signals to form a composite time division signal.
- the composite time division signal is provided to the front analog signal conditioning circuitry 330 . Additional detail regarding the front end analog signal conditioning circuitry 330 and the analog to digital converter circuit 332 is illustrated in FIG. 12. As depicted in FIG. 12, the front end circuitry 302 has a preamplifier 342 , a high pass filter 344 , an amplifier 346 , a programmable gain amplifier 348 , and a low pass filter 350 .
- the preamplifier 342 is a transimpedance amplifier that converts the composite current signal from the photodetector 320 to a corresponding voltage signal, and amplifies the signal. In the present embodiment, the preamplifier has a predetermined gain to boost the signal amplitude for ease of processing.
- the source voltages for the preamplifier 342 are ⁇ 15 VDC and +15 VDC.
- the attenuated signal contains a component representing ambient light as well as the component representing the infrared or the red light as the case may be in time. If there is light in the vicinity of the sensor 300 other than the red and infrared light, this ambient light is detected by the photodetector 320 . Accordingly, the gain of the preamplifier is selected in order to prevent the ambient light in the signal from saturating the preamplifier under normal and reasonable operating conditions.
- the preamplifier 342 comprises an Analog Devices AD743JR OpAmp.
- This transimpedance amplifier is particularly advantageous in that it exhibits several desired features for the system described, such as: low equivalent input voltage noise, low equivalent input current noise, low input bias current, high gain bandwidth product, low total harmonic distortion, high common mode rejection, high open loop gain, and a high power supply rejection ratio.
- the output of the preamplifier 342 couples as an input to the high pass filter 344 .
- the output of the preamplifier also provides a first input 346 to the analog to digital conversion circuit 332 .
- the high pass filter is a single-pole filter with a corner frequency of about 1 ⁇ 2-1 Hz. However, the corner frequency is readily raised to about 90 Hz in one embodiment. As will be understood, the 625 Hz carrier frequency of the red and infrared signals is well above a 90 Hz corner frequency.
- the high-pass filter 344 has an output coupled as an input to an amplifier 346 .
- the amplifier 346 comprises a unity gain amplifier. However, the gain of the amplifier 346 is adjustable by the variation of a single resistor. The gain of the amplifier 346 would be increased if the gain of the preamplifier 342 is decreased to compensate for the effects of ambient light.
- the output of the amplifier 346 provides an input to a programmable gain amplifier 348 .
- the programmable gain amplifier 348 also accepts a programming input from the digital signal processing system 334 on a gain control signal line 343 .
- the gain of the programmable gain amplifier 348 is digitally programmable. The gain is adjusted dynamically at initialization or sensor placement for changes in the medium under test from patient to patient. For example, the signal from different fingers differs somewhat. Therefore, a dynamically adjustable amplifier is provided by the programmable gain amplifier 348 in order to obtain a signal suitable for processing.
- the programmable gain amplifier is also advantageous in an alternative embodiment in which the emitter drive current is held constant.
- the emitter drive current is adjusted for each patient in order to obtain the proper dynamic range at the input of the analog to digital conversion circuit 332 .
- changing the emitter drive current can alter the emitter wavelength, which in turn affects the end result in oximetry calculations. Accordingly, it would be advantageous to fix the emitter drive current for all patients.
- the programmable gain amplifier can be adjusted by the DSP in order to obtain a signal at the input to the analog to digital conversion circuit which is properly within the dynamic range (+3 v to ⁇ 3 v in the present embodiment) of the analog to digital conversion circuit 332 . In this manner, the emitter drive current could be fixed for all patients, eliminating the wavelength shift due to emitter current drive changes.
- the output of the programmable gain amplifier 348 couples as an input to a low-pass filter 350 .
- the low pass filter 350 is a single-pole filter with a corner frequency of approximately 10 Khz in the present embodiment. This low pass filter provides anti-aliasing in the present embodiment.
- the output of the low-pass filter 350 provides a second input 352 to the analog-to-digital conversion circuit 332 .
- FIG. 12 also depicts additional defect of the analog-to-digital conversion circuit.
- the analog-to-digital conversion circuit 332 comprises a first analog-to-digital converter 354 and a second analog-to-digital converter 356 .
- the first analog-to-digital converter 354 accepts input from the first input 346 to the analog-to-digital conversion circuit 332
- the second analog to digital converter 356 accepts input on the second input 352 to the analog-to-digital conversion circuitry 332 .
- the first analog-to-digital converter 354 is a diagnostic analog-to-digital converter.
- the diagnostic task (performed by the digital signal processing system) is to read the output of the detector as amplified by the preamplifier 342 in order to determine if the signal is saturating the input to the high-pass filter 344 .
- the front end analog signal conditioning circuits 330 provides a ‘0’ output.
- the first analog-to-digital converter 354 remains unused.
- the second analog-to-digital converter 352 accepts the conditioned composite analog signal from the front end signal conditioning circuitry 330 and converts the signal to digital form.
- the second analog to digital converter 356 comprises a single-channel, delta-sigma converter.
- a Crystal Semiconductor CS5317-KS delta-sigma analog to digital converter is used.
- Such a converter is advantageous in that it is low cost, and exhibits low noise characteristics.
- a delta-sigma converter consists of two major portions, a noise modulator and a decimation filter. The selected converter uses a second order analog delta-sigma modulator to provide noise shaping.
- Noise shaping refers to changing the noise spectrum from a flat response to a response where noise at the lower frequencies has been reduced by increasing noise at higher frequencies.
- the decimation filter then cuts out the reshaped, higher frequency noise to provide 16-bit performance at a lower frequency.
- the present converter samples the data 128 times for every 16 bit data word that it produces. In this manner, the converter provides excellent noise rejection, dynamic range and low harmonic distortion, that help in critical measurement situations like low perfusion and electrocautery.
- the delta-sigma converter is also advantageous in that it exhibits noise shaping, for improved noise control.
- An exemplary analog to digital converter is a Crystal Semiconductor CS5317.
- the second analog to digital converter 356 samples the signal at a 20 Khz sample rate.
- the output of the second analog to digital converter 356 provides data samples at 20 Khz to the digital signal processing system 334 (FIG. 11).
- the digital signal processing system 334 is illustrated in additional detail in FIG. 13.
- the digital signal processing system comprises a microcontroller 360 , a digital signal processor 362 , a program memory 364 , a sample buffer 366 , a data memory 368 , a read only memory 370 and communication registers 372 .
- the digital signal processor 362 is an Analog Devices AD 21020.
- the microcontroller 360 comprises a Motorola 68HC05, with built in program memory.
- the sample buffer 366 is a buffer which accepts the 20 Khz sample data from the analog to digital conversion circuit 332 for storage in the data memory 368 .
- the data memory 368 comprises 32 KWords (words being 40 bits in the present embodiment) of static random access memory.
- the microcontroller 360 is connected to the DSP 362 via a conventional JTAG Tap line.
- the microcontroller 360 transmits the boot loader for the DSP 362 to the program memory 364 via the Tap line, and then allows the DSP 362 to boot from the program memory 364 .
- the boot loader in program memory 364 then causes the transfer of the operating instructions for the DSP 362 from the read only memory 370 to the program memory 364 .
- the program memory 364 is a very high speed memory for the DSP 362 .
- the microcontroller 360 provides the emitter current control and gain control signals via the communications register 372 .
- FIGS. 14 - 20 depict functional block diagrams of the operations of the pulse oximeter 299 carried out by the digital signal processing system 334 .
- the signal processing functions described below are carried out by the DSP 362 in the present embodiment with the microcontroller 360 providing system management. In the present embodiment, the operation is software/firmware controlled.
- FIG. 14 depicts a generalized functional block diagram for the operations performed on the 20 Khz sample data entering the digital signal processing system 334 . As illustrated in FIG. 14, a demodulation, as represented in a demodulation module 400 , is first performed. Decimation, as represented in a decimation module 402 is then performed on the resulting data.
- Certain statistics are calculated, as represented in a statistics module 404 and a saturation transform is performed, as represented in a saturation transform module 406 , on the data resulting from the decimation operation.
- the data subjected to the statistics operations and the data subjected to the saturation transform operations are forwarded to saturation operations, as represented by a saturation calculation module 408 and pulse rate operations, as represented in a pulse rate calculation module 410 .
- the demodulation operation separates the red and infrared signals from the composite signal and removes the 625 Hz carrier frequency, leaving raw data points.
- the raw data points are provided at 625 Hz intervals to the decimation operation which reduces the samples by an order of 10 to samples at 62.5 Hz.
- the decimation operation also provides some filtering on the samples.
- the resulting data is subjected to statistics and to the saturation transform operations in order to calculate a saturation value which is very tolerant to motion artifacts and other noise in the signal.
- the saturation value is ascertained in the saturation calculation module 408 , and a pulse rate and a clean plethysmographic waveform is obtained through the pulse rate module 410 . Additional detail regarding the various operations is provided in connection with FIGS. 15 - 21 .
- FIG. 15 illustrates the operation of the demodulation module 400 .
- the modulated signal format is depicted in FIG. 15.
- One full 625 Hz cycle of the composite signal is depicted in FIG. 15 with the first quarter cycle being the active red light plus ambient light signal, the second quarter cycle being an ambient light signal, the third quarter cycle being the active infrared plus ambient light signal, and the fourth quarter cycle being an ambient light signal.
- the single full cycle at 625 Hz described above comprises 32 samples of 20 KHz data, eight samples relating to red plus ambient light, eight samples relating to ambient light, eight samples relating to infrared plus ambient light, and finally eight samples related to ambient light.
- the signal processing system 334 controls the activation of the light emitters 300 , 302 , the entire system is synchronous.
- the data is synchronously divided (and thereby demodulated) into four 8-sample packets, with a time division demultiplexing operation as represented in a demultiplexing module 421 .
- One eight-sample packet 422 represents the red plus ambient light signal;
- a second eight-sample packet 424 represents an ambient light signal;
- a third eight-sample packet 426 represents the attenuated infrared light plus ambient light signal;
- a fourth eight-sample packet 428 represents the ambient light signal.
- a select signal synchronously controls the demultiplexing operation so as to divide the time-division multiplexed composite signal at the input of the demultiplexer 421 into its four subparts.
- a sum of the last four samples from each packet is then calculated, as represented in the summing operations 430 , 432 , 434 , 436 of FIG. 15.
- the last four samples are used because a low pass filter in the analog to digital converter 356 of the present embodiment has a settling time.
- This summing operation provides an integration operation which enhances noise immunity.
- the sum of the respective ambient light samples is then subtracted from the sum of the red and infrared samples, as represented in the subtraction modules 438 , 440 .
- the subtraction operation provides some attenuation of the ambient light signal present in the data.
- the 625 Hz carrier frequency has been removed by the demodulation operation 400 .
- the 625 Hz sample data at the output of the demodulation operation 400 is sample data without the carrier frequency.
- less than 20 Hz is needed (understanding that the human pulse is about 25 to 250 beats per minute, or about 0.4 Hz-4 Hz). Accordingly, the 625 Hz resolution is reduced to 62.5 Hz in the decimation operation.
- FIG. 16 illustrates the operations of the decimation module 402 .
- the red and infrared sample data is provided at 625 Hz to respective red and infrared buffer/filters 450 , 452 .
- the red and infrared buffer/filters are 519 samples deep.
- the buffer filters 450 , 452 function as continuous first-in, first-out buffers.
- the 519 samples are subjected to low-pass filtering.
- the low-pass filtering has a cutoff frequency of approximately 7.5 Hz with attenuation of approximately ⁇ 110 dB.
- the buffer/filters 450 , 452 form a Finite Impulse Response (FIR) filter with coefficients for 519 taps.
- FIR Finite Impulse Response
- the low-pass filter calculation is performed every ten samples, as represented in respective red and infrared decimation by 10 modules 454 , 456 .
- a new low pass filter calculation is performed by multiplying the impulse response (coefficients) by the 519 filter taps.
- Each filter calculation provides one output sample for respective red and infrared output buffers 458 , 460 .
- the red and infrared output buffers 458 , 460 are also continuous FIFO buffers that hold 570 samples of data.
- the 570 samples provide respective infrared and red samples or packets (also denoted “snapshot” herein) of samples.
- the output buffers provide sample data for the statistics operation module 404 , saturation transform module 406 , and the pulse rate module 410 .
- FIG. 17 illustrates additional functional operation details of the statistics module 404 .
- the statistics module 404 provides first order oximetry calculations and RMS signal values for the red and infrared channels.
- the statistics module also provides a cross-correlation output which indicates a cross-correlation between the red and infrared signals.
- the statistics operation accepts two packets of samples (e.g., 570 samples at 62.5 Hz in the present embodiment) representing the attenuated infrared and red signals, with the carrier frequency removed.
- the respective packets for infrared and red signals are normalized with a log function, as represented in the Log modules 480 , 482 .
- the normalization is followed by removal of the DC portion of the signals, as represented in the DC Removal modules 484 , 486 .
- the DC removal involves ascertaining the DC value of the first one of the samples (or the mean of the first several or the mean of an entire snapshot) from each of the respective red and infrared snapshots, and removing this DC value from all samples in the respective packets.
- the signals are subjected to bandpass filtering, as represented in red and infrared Bandpass Filter modules 488 , 490 .
- the bandpass filters are configured with 301 taps to provide a FIR filter with a linear phase response and little or no distortion.
- the bandpass filter has a pass band from 34 beats/minute to 250 beats/minute.
- the 301 taps slide over the 570 samples in order to obtain 270 filtered samples representing the filtered red signal and 270 filtered samples representing the filtered infrared signal.
- the bandpass filters 488 , 490 remove the DC in the signal.
- the DC removal operations 484 , 486 assist in DC removal in the present embodiment.
- the last 120 samples from each packet are selected for further processing as represented in Select Last 120 Samples modules 492 , 494 .
- the last 120 samples are selected because, in the present embodiment, the first 150 samples fall within the settling time for the Saturation Transfer module 406 , which processes the same data packets, as further discussed below.
- the saturation equation module 502 represents a conventional look-up table or the like which, for predetermined ratios, provides known saturation values at its output 504 .
- the red and infrared RMS values are also provided as outputs of the statistics operations module 404 .
- the 120-sample packets are subjected to a cross-correlation operation as represented in a first cross-correlation module 506 .
- the first cross-correlation module 506 determines if good correlation exists between the infrared and red signals. This cross correlation is advantageous for detecting defective or otherwise malfunctioning detectors.
- the cross correlation is also advantageous in detecting when the signal model (i.e., the model of Equations (1)-(3)) is satisfied. If correlation becomes too low between the two channels, the signal model is not met.
- the normalized cross correlation can be computed by the cross-correlation module 506 for each snapshot of data.
- One such correlation function is as follows: ⁇ s 1 ⁇ s 2 ⁇ s 1 2 ⁇ s 2 2
- the oximeter 299 provides a warning (e.g., audible, visual, etc.) to the operator.
- a warning e.g., audible, visual, etc.
- the snapshot does not qualify. Signals which satisfy the signal model will have a correlation greater than the threshold.
- the red and infrared 120-sample packets are also subjected to a second saturation operation and cross correlation in the same manner as described above, except the 120 samples are divided into 5 equal bins of samples (i.e., 5 bins of 24 samples each).
- the RMS, ratio, saturation, and cross correlation operations are performed on a bin-by-bin basis. These operations are represented in the Divide Into Five Equal Bins modules 510 , 512 , the second red and infrared RMS modules 514 , 516 , the second RED-RMS/IR-RMS ratio module 518 , the second saturation equation module 520 and the second cross correlation module 522 in FIG. 17.
- FIG. 18 illustrates additional detail regarding the saturation transform module 406 depicted in FIG. 14.
- the saturation transform module 406 comprises a reference processor 530 , a correlation canceler 531 , a master power curve module 554 , and a bin power curve module 533 .
- the saturation transform module 406 can be correlated to FIG. 7 a which has a reference processor 26 and a correlation canceler 27 and an integrator 29 to provide a power curve for separate signal coefficients as depicted in FIG. 7 c.
- the saturation transform module 406 obtains a saturation spectrum from the snapshots of data. In other words, the saturation transform 406 provides information of the saturation values present in the snapshots.
- the reference processor 530 for the saturation transform module 406 has a saturation equation module 532 , a reference generator module 534 , a DC removal module 536 and a bandpass filter module 538 .
- the red and infrared 570-sample packets from the decimation operation are provided to the reference processor 530 .
- a plurality of possible saturation values are provided as input to the saturation reference processor 530 .
- 117 saturation values are provided as the saturation axis scan.
- the 117 saturation values range uniformly from a blood oxygen saturation of 34.8 to 105.0.
- the 117 saturation values provide an axis scan for the reference processor 530 which generates a reference signal for use by the correlation canceler 531 .
- the reference processor is provided with each of the saturation values, and a resultant reference signal is generated corresponding to the saturation value.
- the correlation canceler is formed by a joint process estimator 550 and a low pass filter 552 in the present embodiment.
- the scan values could be chosen to provide higher or lower resolution than 117 scan values.
- the scan values could also be non-uniformly spaced.
- the saturation equation module 532 accepts the saturation axis scan values as an input and provides a ratio “r n ” as an output.
- this ratio “r n ” corresponds to the plurality of scan value discussed above in general.
- the saturation equation simply provides a known ratio “r” (red/infrared) corresponding to the saturation value received as an input.
- the ratio “r n ” is provided as an input to the reference generator 534 , as are the red and infrared sample packets.
- the reference generator 534 multiplies either the red or infrared samples by the ratio “r n ” and subtracts the value from the infrared or red samples, respectively. For instance, in the present embodiment, the reference generator 534 multiplies the red samples by the ratio “r n ” and subtracts this value from the infrared samples.
- the resulting values become the output of the reference generator 534 .
- This operation is completed for each of the saturation scan values (e.g., 117 possible values in the present embodiment). Accordingly, the resultant data can be described as 117 reference signal vectors of 570 data points each, hereinafter referred to as the reference signal vectors. This data can be stored in an array or the like.
- the output of the reference generator becomes the secondary reference signal n′(t), which complies with the signal model defined above, as follows:
- n′ ( t ) s ir ( t ) ⁇ r n s red ( t )
- the reference signal vectors and the infrared signal are provided as input to the DC removal module 536 of the reference processor 530 .
- the DC removal module 536 like the DC removal modules 484 , 486 in the statistics module 404 , ascertains the DC value of the first of the samples for the respective inputs (or mean of the first several or all samples in a packet) and subtracts the respective DC baseline from the sample values. The resulting sample values are subjected to a bandpass filter 538 .
- the bandpass filter 538 of the reference processor 530 performs the same type of filtering as the bandpass filters 488 , 490 of the statistics module 404 . Accordingly, each set of 570 samples subjected to bandpass filtering results in 270 remaining samples.
- the resulting data at a first output 542 of the bandpass filter 538 is one vector of 270 samples (representing the filtered infrared signal in the present embodiment).
- the resulting data at a second output 540 of the bandpass filter 538 therefore, is 117 reference signal vectors of 270 data points each, corresponding to each of the saturation axis scan values provided to the saturation reference processor 530 .
- red and infrared sample packets may be switched in their use in the reference processor 530 .
- the DC removal module 536 and the bandpass filter module 538 can be executed prior to input of the data to the reference processor 530 because the calculations performed in the reference processor are linear. This results in a significant processing economy.
- the outputs of the reference processor 530 provide first and second inputs to a joint process estimator 550 of the type described above with reference to FIG. 8.
- the first input to the joint process estimator 550 is the 270-sample packet representing the infrared signal in the present embodiment. This signal contains primary and secondary signal portions.
- the second input to the joint process estimator is the 117 reference signal vectors of 270 samples each.
- the joint process estimator also receives a lambda input 543 , a minimum error input 544 and a number of cells configuration input 545 . These parameters are well understood in the art.
- the lambda parameter is often called the “forgetting parameter” for a joint process estimator.
- the lambda input 543 provides control for the rate of cancellation for the joint process estimator. In the present embodiment, lambda is set to a low value such as 0.8. Because statistics of the signal are non-stationary, a low value improves tracking.
- the minimum error input 544 provides an initialization parameter (conventionally known as the “initialization value”) for the joint process estimator 550 . In the present embodiment, the minimum error value is 10 ⁇ 6 .
- This initialization parameter prevents the joint process estimator 500 from dividing by zero upon initial calculations.
- the number of cells input 545 to the joint process estimator 550 configures the number of cells for the joint process estimator.
- the number of cells for the saturation transform operation 406 is six.
- the joint process estimator requires two cells. If there are two sine waves in the 35-250 beats/minute range, six cells allows for the two heart beat sine waves and one noise sine wave.
- the joint process estimator 550 subjects the first input vector on the first input 542 to a correlation cancellation based upon each of the plurality of reference signal vectors provided in the second input 540 to the correlation canceler 531 (all 117 reference vectors in sequence in the present embodiment).
- the correlation cancellation results in a single output vector for each of the 117 reference vectors.
- Each output vector represents the information that the first input vector and the corresponding reference signal vector do not have in common.
- the resulting output vectors are provided as an output to the joint process estimator, and subjected to the low pass filter module 552 .
- the low pass filter 552 comprises a FIR filter with 25 taps and with a corner frequency of 10 Hz with the sampling frequency of 62.5 Hz (i.e., at the decimation frequency).
- the joint process estimator 550 of the present embodiment has a settling time of 150 data points. Therefore, the last 120 data points from each 270 point output vector are used for further processing.
- the output vectors are further processed together as a whole, and are divided into a plurality of bins of equal number of data points. As depicted in FIG. 18, the output vectors are provided to a master power curve module 554 and to a Divide into five Equal Bins module 556 .
- the Divide into Five Equal Bins module 556 divides each of the output vectors into five bins of equal number of data points (e.g., with 120 data points per vector, each bin has 24 data points). Each bin is then provided to the Bin Power Curves module 558 .
- the Master Power Curve module 554 performs a saturation transform as follows: for each output vector, the sum of the squares of the data points is ascertained. This provides a sum of squares value corresponding to each output vector (each output vector corresponding to one of the saturation scan values). These values provide the basis for a master power curve 555 , as further represented in FIG. 22.
- the horizontal axis of the power curve represents the saturation axis scan values and the vertical axis represents the sum of squares value (or output energy) for each output vector. In other words, as depicted in FIG.
- each of the sum of squares could be plotted with the magnitude of the sum of squares value plotted on the vertical “output” axis at the point on the horizontal axis of the corresponding saturation scan value which generated that output vector.
- the sum of squares for the corresponding output vector of the correlation canceler 531 will be very low.
- the correlation between the first and second inputs to the correlation canceler 531 are not significantly correlated, the sum of squares of the output vector will be high. Accordingly, where the spectral content of the reference signal and the first input to the correlation canceler are made up mostly of physiological (e.g., movement of venous blood due to respiration) and non-physiological (e.g., motion induced) noise, the output energy will be low. Where the spectral content of the reference signal and the first input to the correlation canceler are not correlated, the output energy will be much higher.
- a corresponding transform is completed by the Bin Power Curves module 558 , except a saturation transform power curve is generated for each bin.
- the resulting power curves are provided as the outputs of the saturation transform module 406 .
- the peak corresponding to the highest saturation value corresponds to the proportionality coefficient r a .
- the proportionality coefficient r a corresponds to the red/infrared ratio which will be measured for the arterial saturation.
- peak that corresponds to the lowest saturation value (not necessarily the peak with the lowest magnitude) will generally correspond to the venous oxygen saturation, which corresponds to the proportionality coefficient r v in the signal model of the present invention. Therefore, the proportionality coefficient r v will be a red/infrared ratio corresponding to the venous oxygen saturation.
- FIG. 19 illustrates the operation of the saturation calculation module 408 based upon the output of the saturation transform module 406 and the output of the statistics module 404 .
- the bin power curves and the bin statistics are provided to the saturation calculation module 408 .
- the master power curves are not provided to the saturation module 408 but can be displayed for a visual check on system operation.
- the bin statistics contain the red and infrared RMS values, the seed saturation value, and a value representing the cross-correlation between the red and infrared signals from the statistics module 404 .
- the saturation calculation module 408 first determines a plurality of bin attributes as represented by the Compute Bin Attributes module 560 .
- the Compute Bin Attributes module 560 collects a data bin from the information from the bin power curves and the information from the bin statistics. In the present embodiment, this operation involves placing the saturation value of the peak from each power curve corresponding to the highest saturation value in the data bin.
- the selection of the highest peak is performed by first computing the first derivative of the power curve in question by convolving the power curve with a smoothing differentiator filter function.
- the smoothing differentiator filter function (using a FIR filter) has the following coefficients:
- This filter performs the differentiation and smoothing.
- each point in the original power curve in question is evaluated and determined to be a possible peak if the following conditions are met: (1) the point is at least 2% of the maximum value in the power curve; (2) the value of the first derivative changes from greater than zero to less than or equal to zero.
- the neighboring points are examined and the largest of the three points is considered to be the true peak.
- the peak width for these selected peaks is also calculated.
- the peak width of a power curve in question is computed by summing all the points in the power curve and subtracting the product of the minimum value in the power curve and the number of points in the power curve.
- the peak width calculation is applied to each of the bin power curves. The maximum value is selected as the peak width.
- the infrared RMS value from the entire snapshot, the red RMS value, the seed saturation value for each bin, and the cross correlation between the red and infrared signals from the statistics module 404 are also placed in the data bin.
- the attributes are then used to determine whether the data bin consists of acceptable data, as represented in a Bin Qualifying Logic module 562 .
- the bin is discarded. If the saturation value of the selected peak for a given bin is lower than the seed saturation for the same bin, the peak is replaced with the seed saturation value. If either red or infrared RMS value is below a very small threshold, the bins are all discarded, and no saturation value is provided, because the measured signals are considered to be too small to obtain meaningful data. If no bins contain acceptable data, the exception handling module 563 provides a message to the display 336 that the data is erroneous.
- bins qualify, those bins that qualify as having acceptable data are selected, and those that do not qualify are replaced with the average of the bins that are accepted.
- Each bin is given a time stamp in order to maintain the time sequence.
- a voter operation 565 examines each of the bins and selects the three highest saturation values. These values are forwarded to a clip and smooth operation 566 .
- the clip and smooth operation 566 basically performs averaging with a low pass filter.
- the low pass filter provides adjustable smoothing as selected by a Select Smoothing Filter module 568 .
- the Select Smoothing Filter module 568 performs its operation based upon a confidence determination performed by a High Confidence Test module 570 .
- the high confidence test is an examination of the peak width for the bin power curves. The width of the peaks provides some indication of motion by the patient—wider peaks indicating motion. Therefore, if the peaks are wide, the smoothing filter is slowed down. If peaks are narrow, the smoothing filter speed is increased. Accordingly, the smoothing filter 566 is adjusted based on the confidence level.
- the output of the clip and smooth module 566 provides the oxygen saturation values in accordance with the present invention.
- the clip and smooth filter 566 takes each new saturation value and compares it to the current saturation value. If the magnitude of the difference is less than 16 (percent oxygen saturation) then the value is pass. Otherwise, if the new saturation value is less than the filtered saturation value, the new saturation value is changed to 16 less than the filtered saturation value. If the new saturation value is greater than the filtered saturation value, then the new saturation value is changed to 16 more than the filtered saturation value.
- the smoothing filter is a simple one-pole or exponential smoothing filter which is computed as follows:
- a three-pole IIR (infinite impulse response) filter is used. Its characteristics are controlled by three time constants t a , t b , and t c with values of 0.985, 0.900, and 0.94 respectively.
- the coefficients for a direct form I, IIR filter are computed from these time constants using the following relationships:
- FIGS. 20 and 21 illustrate the pulse rate module 410 (FIG. 14) in greater detail.
- the heart rate module 410 has a transient removal and bandpass filter module 578 , a motion artifact suppression module 580 , a saturation equation module 582 , a motion status module 584 , first and second spectral estimation modules 586 , 588 , a spectrum analysis module 590 , a slew rate limiting module 592 , an output filter 594 , and an output filter coefficient module 596 .
- the heart rate module 410 accepts the infrared and red 570-sample snapshots from the output of the decimation module 402 .
- the heart rate module 410 further accepts the saturation value which is output from the saturation calculation module 408 .
- the maximum peak width as calculated by the confidence test module 570 is also provided as an input to the heart rate module 410 .
- the infrared and red sample packets, the saturation value and the output of the motion status module 584 are provided to the motion artifact suppression module 580 .
- the average peak width value provides an input to a motion status module 584 .
- this is taken as an indication of motion. If motion is not detected, spectral estimation on the signals is carried out directly without motion artifact suppression.
- the motion artifact suppression module 580 is nearly identical to the saturation transform module 406 .
- the motion artifact suppression module 580 provides an output which connects as an input to the second spectral estimation module 588 .
- the first and second spectral estimation modules 586 , 588 have outputs which provide inputs to the spectrum analysis module 590 .
- the spectrum analysis module 590 also receives an input which is the output of the motion status module 584 .
- the output of the spectrum analysis module 590 is the initial heart rate determination of the heart rate module 410 and is provided as input to the slew rate limiting module 592 .
- the slew rate limiting module 592 connects to the output filter 594 .
- the output filter 594 also receives an input from the output filter coefficient module 596 .
- the output filter 594 provides the filtered heart rate for the display 336 (FIG. 11).
- one of the signals (the infrared signal in the present embodiment) is subjected to DC removal and bandpass filtering as represented in the DC removal and bandpass filter module 578 .
- the DC removal and bandpass filter module 578 provide the same filtering as the DC removal and bandpass filter modules 536 , 538 .
- the filtered infrared signal is provided to the first spectral estimation module 586 .
- the spectral estimation comprises a Chirp Z transform that provides a frequency spectrum of heart rate information.
- the Chirp Z transform is used rather than a conventional Fourier Transform because a frequency range for the desired output can be designated in a Chirp Z transform.
- a frequency spectrum of the heart rate is provided between 30 and 250 beats/minute.
- the frequency spectrum is provided to a spectrum analysis module 590 which selects the first harmonic from the spectrum as the pulse rate.
- the first harmonic is the peak in the frequency spectrum that has the greatest magnitude and represents the pulse rate.
- the second or third harmonic can exhibit the greater magnitude.
- the first peak that has an amplitude of at least ⁇ fraction (1/20) ⁇ th of the largest peak in the spectrum is selected). This minimizes the possibility of selecting as the heart rate a peak in the Chirp Z transform caused by noise.
- a motion artifact suppression is completed on the snapshot with the motion artifact suppression module 580 .
- the motion artifact suppression module 580 is depicted in greater detail in FIG. 21. As can be seen in FIG. 21, the motion artifact suppression module 580 is nearly identical to the saturation transform module 406 (FIG. 18). Accordingly, the motion artifact suppression module has a motion artifact reference processor 570 and a motion artifact correlation canceler 571 .
- the motion artifact reference processor 570 is the same as the reference processor 530 of the saturation transform module 406 . However, the reference processor 570 utilizes the saturation value from the saturation module 408 , rather than completing an entire saturation transform with the 117 saturation scan values.
- the reference processor 570 therefore, has a saturation equation module 581 , a reference generator 582 , a DC removal module 583 , and a bandpass filter module 585 . These modules are the same as corresponding modules in the saturation transform reference processor 530 .
- the saturation equation module 581 receives the arterial saturation value from the saturation calculation module 408 rather than doing a saturation axis scan as in the saturation transform module 406 .
- the output of the saturation equation module 581 corresponds to the proportionality constant r a (i.e., the expected red to infrared ratio for the arterial saturation value).
- the reference processor 570 performs the same function as the reference processor 530 of the saturation transform module 406 .
- the motion artifact correlation canceler 571 is also similar to the saturation transform correlation canceler 531 (FIG. 18). However, the motion artifact suppression correlation canceler 571 uses a slightly different motion artifact joint process estimator 572 . Accordingly, the motion artifact suppression correlation canceler 571 has a joint process estimator 572 and a low-pass filter 573 .
- the motion artifact joint process estimator 572 differs from the saturation transform joint process estimator 550 in that there are a different number of cells (between 6 and 10 in the present embodiment), as selected by the Number of Cells input 574 , in that the forgetting parameter differs (0.98 in the present embodiment), and in that the time delay due to adaptation differs.
- the low-pass filter 573 is the same as the low pass filter 552 of the saturation transform correlation canceler 531 .
- the output of the correlation canceler 571 is a clean waveform because the actual saturation value of the patient is known which allows the reference processor 570 to generate a noise reference for inputting to the correlation canceler 571 as the reference signal.
- the clean waveform at the output of the motion artifact suppression module 580 is a clean plethysmograph waveform which can be forwarded to the display 336 .
- an alternative joint process estimator uses the QRD least squares lattice approach (FIGS. 8 a, 9 a and 10 a ). Accordingly, the joint process estimator 573 (as well as the joint process estimator 550 ) could be replaced with a joint process estimator executing the QRD least squares lattice operation.
- FIG. 21 a depicts an alternative embodiment of the motion artifact suppression module with a joint process estimator 572 a replacing the joint process estimator 572 .
- the joint process estimator 572 a comprises a QRD least squares lattice system as in FIG. 10 a.
- different initialization parameters are used as necessary for the QRD algorithm.
- the initialization parameters are referenced in FIG. 21 a as “Number of Cells,” “Lambda,” “MinSumErr,” “GamsInit,” and “SumErrInit.” Number of Cells and Lambda correspond to like parameters in the joint process estimator 572 .
- GamsInit corresponds to the ⁇ initialization variable for all stages except the zero order stage, which as set forth in the QRD equations above is initialized to ‘1’.
- SumErrInit provides the ⁇ initialization parameter referenced above in the QRD equations. In order to avoid overflow, the larger of the actual calculated denominator in each division in the QRD equations and MinSumErr is used.
- the preferred initialization parameters are as follows:
- the clean waveform output from the motion artifact suppression module 580 also provides an input to the second spectral estimation module 588 .
- the second spectral estimation module 588 performs the same Chirp Z transform as the first spectral estimation module 586 .
- the output from the first spectral estimation module 586 is provided to the spectrum analysis module 586 ; in the case of motion, the output from the second spectral estimation module 588 is provided to a spectrum analysis module 590 .
- the spectrum analysis module 590 examines the frequency spectrum from the appropriate spectral estimation module to determine the pulse rate.
- the spectrum analysis module 590 selects the peak in the spectrum with the highest amplitude, because the motion artifact suppression module 580 attenuates all other frequencies to a value below the actual heart rate peak. In the case of no motion, the spectrum analysis module selects the first harmonic in the spectrum as the heart rate as described above.
- the output of the spectrum analysis module 590 provides the raw heart rate as an input to the slew rate limiting module 592 , which provides an input to an output filter 594 .
- the slew rate limiting module 592 prevents changes greater that 20 beats/minute per 2 second interval.
- the output filter 594 comprises an exponential smoothing filter similar to the exponential smoothing filter described above with respect to the clip and smooth filter 566 .
- the output filter is controlled via an output filter coefficient module 596 . If motion is large, this filter is slowed down, if there is little or no motion, this filter can sample much faster and still maintain a clean value.
- the output from the output filter 594 is the pulse of the patient, which is advantageously provided to the display 336 .
- FIG. 23 An alternative to the saturation transform of the saturation transform module 406 can be implemented with a bank of filters as depicted in FIG. 23.
- a first filter bank 600 receives a first measured signal S ⁇ b (t) (the infrared signal samples in the present embodiment) on a corresponding first filter bank input 604
- the second filter bank 602 receives a second measured signal S ⁇ a (t) (the red samples in the present embodiment) on a corresponding second filter bank input 606 .
- the first and second filter banks utilize static recursive polyphase bandpass filters with fixed center frequencies and corner frequencies.
- Recursive polyphase filters are described in an article Harris, et. al. “Digital Signal Processing With Efficient Polyphase Recursive All-Pass filters” attached hereto as Appendix A. However, adaptive implementations are also possible. In the present implementation, the recursive polyphase bandpass filter elements are each designed to include a specific center frequency and bandwidth.
- Each of the filter elements in the first filter bank 600 have a matching (i.e., same center frequency and bandwidth) filter element in the second filter bank 602 .
- the center frequencies and the corner frequencies of N elements are each designed to occupy N frequency ranges, 0 to F 1 , F 1 -F 2 , F 2 -F 3 , F 3 -F 4 . . . F N ⁇ 1 -F N as shown in FIG. 23.
- the number of filter elements can range from 1 to infinity. However, in the present embodiment, there are approximately 120 separate filter elements with center frequencies spread evenly across a frequency range of 25 beats/minute-250 beats/minute.
- the outputs of the filters contain information about the primary and secondary signals for the first and second measured signals (red and infrared in the present example) at the specified frequencies.
- the outputs for each pair of matching filters are provided to saturation determination modules 610 .
- FIG. 23 depicts only one saturation determination module 610 for ease of illustration. However, a saturation determination module can be provided for each matched pair of filter elements for parallel processing.
- Each saturation determination module has a ratio module 616 and a saturation equation module 618 .
- the ratio module 616 forms a ratio of the second output to the first output. For instance, in the present example, a ratio of each red RMS value to each corresponding infrared RMS value (Red/IR) is completed in the ratio module 616 .
- the output of the ratio module 616 provides an input to the saturation equation module 618 which references a corresponding saturation value for the input ratio.
- the output of the saturation equation modules 618 are collected (as represented in the histogram module 620 ) for each of the matched filter pairs. However, the data collected is initially a function of frequency and saturation. In order to form a saturation transform curve similar to the curve depicted in FIG. 22, a histogram or the like is generated as in FIG. 24. The horizontal axis represents the saturation value, and the vertical axis represents a summation of the number of points (outputs from the saturation equation modules 618 ) collected at each saturation value. In other words, if the output of the saturation equation module 618 for ten different matched filter pairs indicates a saturation value of 98%, then a point in the histogram of FIG. 24 would reflect a value of 10 at 98% saturation. This results in a curve similar to the saturation transform curve of FIG. 22. This operation is completed in the histogram module 620 .
- the results of the histogram provide a power curve similar to the power curve of FIG. 22. Accordingly, the arterial saturation can be calculated from the histogram by selecting the peak (greatest number of occurrences in the area of interest) corresponding to the highest saturation value (e.g., the peak ‘c’ in Figure peaks corresponding to the highest saturation value peak. Similarly, the venous or background saturation can be determined from the histogram by selecting the peak corresponding to the lowest saturation value (e.g., the peak ‘d’ in FIG. 24), in a manner similar to the processing in the saturation calculation module 408 .
- the output saturation (not necessarily a peak in the histogram) corresponding to the highest saturation value could be selected as the arterial saturation with the corresponding ratio representing r a .
- the output saturation corresponding to the lowest saturation value could be selected as the venous or background saturation with the corresponding ratio representing r v .
- the entry ‘a’ in the histogram of FIG. 24 would be chosen as the arterial saturation and the entry in the histogram ‘b’ with the lowest saturation value would be chosen as the venous or background saturation.
- primary and secondary signal portions can be modeled as follows:
- Equation (89) [0359] Substituting Equation (91) into Equation (89) provides the following:
- determining r a and r v can be accomplished using the saturation transform described above doing a scan of many possible coefficients.
- the correlation of equation (93) is enhanced with a user specified window function as follows:
- ⁇ i 1 N ⁇ w i ⁇ s 2 ⁇ ( s r ⁇ ⁇ e ⁇ ⁇ d i , s I ⁇ ⁇ R i , r a , r v ) ⁇ n 2 ⁇ ( s r ⁇ ⁇ e ⁇ ⁇ d i , s I ⁇ ⁇ R i , r a , r v )
- the correlation equation becomes an equation in terms of two variables, r a and r v .
- an exhaustive scan is executed for a good cross-section of possible values for r a and r v (e.g., 20-50 values each corresponding to saturation values ranging from 30-105).
- the minimum of the correlation function is then selected and the values of r a and r v which resulted in the minimum are chosen as r a and r v .
- arterial oxygen saturation and venous oxygen saturation can be determined by provided r a and r v to a saturation equation, such as the saturation equation 502 of the statistics module 404 which provides an oxygen saturation value corresponding to the ratios r a and r v .
- r a and r v the same signal model set forth above is again used.
- the energy in the signal s 2 is maximized under the constraint that s 2 is uncorrelated with n 2 .
- this implementation is based upon minimizing the correlation between s and n and on the signal model of the present invention where the signal s relates to the arterial pulse and the signal n is the noise (containing information on the venous blood, as well as motion artifacts and other noise); r a is the ratio (RED/IR) related to arterial saturation and r v is the ratio (RED/IR) related to venous saturation.
- r a and r v are determined such that the energy of the signal s 2 is maximized where s 2 and n 2 are uncorrelated.
- R 1 is the energy of the red signal
- R 2 is the energy of the infrared signal
- R 1,2 is the correlation between the red and infrared signals.
- ⁇ is the Lagrange multiplier. Finding the value of r a , r v and ⁇ that solve the cost function can be accomplished using a constrained optimization method such as described in Luenberger, Linear & Nonlinear Programming, Addison-Wesley, 2d Ed., 1984.
- Solving equation (104) results in two values for r v .
- the r v value that results in x 2 ⁇ r v y>0 is selected. If both values of r v result in x 2 ⁇ r v y>0, the r v that maximizes the energy of s 2 (Energy(s 2 )) at t 2 is selected. r v is then substituted into the equations above to obtain r a . Alternatively r a can be found directly in the same manner r v was determined.
- the blood oxygen saturation, pulse rate and a clean plethysmographic waveform of a patient can also be obtained using the signal model of the present invention using a complex FFT, as explained further with reference to FIGS. 25 A- 25 C.
- a complex FFT a complex FFT
- FIG. 25A corresponds generally to FIG. 14, with the fast saturation transform replacing the previously described saturation transform.
- the operations of FIG. 25A can replace the operations of FIG. 14.
- the fast saturation transform is represented in a fast saturation transform/pulse rate calculation module 630 .
- the outputs are arterial oxygen saturation, a clean plethysmographic waveform, and pulse rate.
- FIG. 25B and 25C illustrate additional detail regarding the fast saturation transform/pulse rate calculation module 630 . As depicted in FIG.
- the fast saturation transform module 630 has infrared log and red log modules 640 , 642 to perform a log normalization as in the infrared and red log modules 480 , 482 of FIG. 17. Similarly, there are infrared DC removal and red DC removal modules 644 , 646 . In addition, there are infrared and red high-pass filter modules 645 , 647 , window function modules 648 , 640 , complex FFT modules 652 , 654 , select modules 653 , 655 , magnitude modules 656 , 658 , threshold modules 660 , 662 , a point-by-point ratio module 670 , a saturation equation module 672 , and a select saturation module 680 . There are also phase modules 690 , 692 , a phase difference module 694 , and a phase threshold module 696 . The output of the select saturation module 680 provides the arterial saturation on an arterial saturation output line 682 .
- the snapshot for red and infrared signals is 562 samples from the decimation module 402 .
- the infrared DC removal module 644 and the red DC removal module 646 are slightly different from the infrared and red DC removal modules 484 , 486 of FIG. 17.
- the mean of all 563 sample points for each respective channel is calculated. This mean is then removed from each individual sample point in the respective snapshot in order to remove the baseline DC from each sample.
- the outputs of the infrared and red DC removal modules 644 , 646 provide inputs to respective infrared high-pass filter module 645 and red high-pass filter module 647 .
- the high-pass filter modules 645 , 647 comprise FIR filters with 51 taps for coefficients.
- the high-pass filters comprise Chebychev filters with a side-lobe level parameter of 30 and a corner frequency of 0.5 Hz (i.e., 30 beats/minute). It will be understood that this filter could be varied for performance. With 562 sample points entering the high-pass filters, and with 51 taps for coefficients, there are 512 samples provided from these respective infrared and red snapshots at the output of the high-pass filter modules.
- the output of the high-pass filter modules provides an input to the window function modules 648 , 650 for each respective channel.
- the window function modules 648 , 650 perform a conventional windowing function.
- a Kaiser windowing function is used in the present embodiment.
- the functions throughout FIG. 25B maintain a point-by-point analysis.
- the time bandwidth product for the Kaiser window function is 7.
- the output of the window function modules provides an input to the respective complex Fast Fourier Transform (FFT) modules 652 , 654 .
- FFT Fast Fourier Transform
- the complex FFT modules 652 , 654 perform complex FFTs on respective infrared and red channels on the data snapshots.
- the data from the complex FFTs is then analyzed in two paths, once which examines the magnitude and one which examines the phase from the complex FFT data points.
- the data is provided to respective infrared and red select modules 653 , 655 because the output of the FFT operation will provide repetitive information from 0-1 ⁇ 2 the sampling rate and from 1 ⁇ 2 the sampling rate to the sampling rate.
- the select modules select only samples from 0-1 ⁇ 2 the sampling rate (e.g., 0-31.25 Hz in the present embodiment) and then select from those samples to cover a frequency range of the heart rate and one or more harmonics of the heart rate.
- samples which fall in the frequency range of 20 beats per minute to 500 beats per minute are selected. This value can be varied in order to obtain harmonics of the heart rate as desired. Accordingly, the output of the select modules results in less than 256 samples.
- the sample points 2-68 of the outputs of the FFTs are utilized for further processing.
- the output from the select modules 653 , 655 are provided to respective infrared and red magnitude modules 656 , 658 .
- the magnitude modules 656 , 658 perform a magnitude function wherein the magnitude on a point-by-point basis of the complex FFT points is selected for each of the respective channels.
- the outputs of the magnitude modules 656 , 658 provide an input to infrared and red threshold modules 660 , 662 .
- the threshold modules 660 , 662 examine the sample points, on a point-by-point basis, to select those points where the magnitude of an individual point is above a particular threshold which is set at a percentage of the maximum magnitude detected among all the remaining points in the snapshots. In the present embodiment, the percentage for the threshold operation is selected as 1% of the maximum magnitude.
- the data points are forwarded to a point-by-point ratio module 670 .
- the point-by-point ratio module takes the red over infrared ratio of the values on a point-by-point basis. However, a further test is performed to qualify the points for which a ratio is taken.
- the sample points output from the select modules 653 , 655 are also provided to infrared and red phase modules 690 , 692 .
- the phase modules 690 , 692 select the phase value from the complex FFT points.
- the output of the phase modules 690 , 692 is then presented to a phase difference module 694 .
- the phase difference module 694 calculates the difference in phase between the corresponding data points from the phase modules 690 , 692 . If the magnitude of the phase difference between any two corresponding points is less than a particular threshold (e.g., 0.1 radians) in the present embodiment), then the sample points qualify. If the phase of two corresponding sample points is too far apart, then the sample points are not used.
- the output of the phase threshold module 696 provides an enable input to the RED/IR rate module 670 . Accordingly, in order for the ratio of a particular pair of sample points to be taken, the three tests are executed:
- the red sample must pass the red threshold 660 ;
- the infrared sample must pass the infrared threshold 662 ;
- phase between the two points must be less than the predefined threshold as determined in the phase threshold 696 .
- the resulting ratios are provided to a saturation equation module which is the same as the saturation equation modules 502 , 520 in the statistics module 504 .
- the saturation equation module 672 accepts the ratio on a point-by-point basis and provides as an output a corresponding saturation value corresponding to the discrete ratio points.
- the saturation points output from the saturation equation module 672 provide a series of saturation points which could be plotted as saturation with respect to frequency. The frequency reference was entered into the points at the complex FFT stage.
- the arterial (and the venous) saturation can then be selected, as represented in the select arterial saturation module 680 , in one of two methods according to the present invention.
- the arterial saturation value can be selected simply as the point corresponding to the largest saturation value for all points output from the saturation equation module 672 for a packet.
- a histogram similar to the histogram of FIG. 22 can be generated in which the number of saturation values at different frequencies (points) are summed to form a histogram of the number of occurrences for each particular saturation value.
- the arterial saturation can be obtained and provided as an output to the select arterial saturation module on the arterial saturation output line 682 .
- the minimum arterial saturation value, of points that exhibit non-zero value is selected rather than the maximum arterial saturation value.
- the saturation can be provided to the display 336 .
- the fast saturation transform information can also be used to provide the pulse rate and the clean plethysmographic wave form as further illustrated in FIG. 25C.
- the pulse rate and clean plethysmographic wave form are determined using a window function module 700 , a spectrum analysis module 702 and an inverse window function module 704 .
- the input to the window function module 700 is obtained from the output of the complex FFT modules 652 or 654 . In the present embodiment, only one measured signal is necessary.
- Another input to the window function module 700 is the arterial saturation obtained from the output of the select arterial saturation module 680 .
- the window function module performs a windowing function selected to pass those frequencies that significantly correlate to the frequencies which exhibited saturation values very close to the arterial saturation value.
- the following windowing function is selected: 1 - [ SAT art - SAT n 100 ] 15 ( 105 )
- SAT n equals the saturation value corresponding to each particular frequency for the sample points and SAT art represents the arterial saturation as chosen at the output of the select arterial saturation module 680 .
- This window function is applied to the window function input representing the complex FFT of either the red or the infrared signal.
- the output of the window function module 700 is a red or infrared signal represented with a frequency spectrum as determined by the FFT, with motion artifacts removed by the windowing function. It should be understood that many possible window functions can be provided. In addition, with the window function described above, it should be understood that using a higher power will provide more noise suppression.
- the output points from the window function module 700 are provided to a spectrum analysis module 702 .
- the spectrum analysis module 702 is the same as the spectrum analysis module 590 of FIG. 20. In other words, the spectrum analysis module 702 determines the pulse rate by determining the first harmonic in the frequency spectrum represented by the output points of the windowing function 700 .
- the output of spectrum analysis module 702 is the pulse rate.
- the output of the windowing function 700 is applied to an inverse window function module 704 .
- the inverse window function module 704 completes an inverse of the Kaiser window function of the window function module 648 or 650 of FIG. 25B.
- the inverse window function 704 does a point-by-point inverse of the Kaiser function for points that are still defined.
- the output is a clean plethysmographic waveform.
- the noise can be suppressed from the plethysmographic waveform in order to obtain the arterial saturation, the pulse rate, and a clean plethysmographic waveform. It should be understood that although the above description relates to operations primarily in the frequency domain, operations that obtain similar results could also be accomplished in the time domain.
- c V HbO2 represents the concentration of oxygenated hemoglobin in the venous blood
- c V Hb represents the concentration of deoxygenated hemoglobin in the venous blood
- x V represents the thickness of the venous blood (e.g., the thickness the layer containing A3 and A 4 ).
- c A HbO 2 represents the concentration of oxygenated hemoglobin in the arterial blood
- c A Hb represents the concentration of deoxygenated hemoglobin in the arterial blood
- x A represents the thickness of the arterial blood (e.g., the thickness of the layer containing A 5 and A 6 ).
- the oxygen saturation of arterial and venous blood changes slowly, if at all, with respect to the sample rate, making this a valid assumption.
- n ⁇ a ( t ) r v ( t ) n ⁇ b ( t ) (108b)
- n′ ( t ) S ⁇ a ( t ) ⁇ r a ( t ) S ⁇ b ( t ) (110a)
- a first segment 26 a and 27 a of each of the signals is relatively undisturbed by motion artifact, i.e., the patient did not move substantially during the time period in which these segments were measured.
- These segments 26 a and 27 a are thus generally representative of the primary plethysmographic waveform at each of the measured wavelengths.
- a second segment 26 b and 27 b of each of the signals is affected by motion artifact, i.e., the patient did move during the time period in which these segments were measured. Each of these segments 26 b and 27 b shows large motion induced excursions in the measured signal.
- a third segment 26 c and 27 c of each of the signals is again relatively unaffected by motion artifact and is thus generally representative of the primary plethysmographic waveform at each of the measured wavelengths.
- the secondary reference signal n′(t) is correlated to the secondary signal portions n ⁇ a and n ⁇ b .
- a first segment 28 a of the secondary reference signal n′(t) is generally flat, corresponding to the fact that there is very little motion induced noise in the first segments 26 a and 27 a of each signal.
- a second segment 28 b of the secondary reference signal n′(t) exhibits large excursions, corresponding to the large motion induced excursions in each of the measured signals.
- a third segment 28 c of the noise reference signal n′(t) is generally flat, again corresponding to the lack of motion artifact in the third segments 26 c and 27 c of each measured signal.
- the primary reference signal s′(t) would be generally indicative of the plethysmograph waveform.
- FIGS. 29 and 30 show the approximations s′′ ⁇ a (t) and s′′ ⁇ b (t) to the primary signals s ⁇ a (t) and s ⁇ b (t) as estimated by a correlation canceler using a secondary reference signal n′(t). Note that the scale of FIGS. 26 through 30 is not the same for each figure to better illustrate changes in each signal. FIGS. 29 and 30 illustrate the effect of correlation cancellation using the secondary reference signal n′(t) as determined by the reference processor. Segments 29 b and 30 b are not dominated by motion induced noise as were segments 26 b and 27 b of the measured signals. Additionally, segments 29 a, 30 a, 29 c, and 30 c have not been substantially changed from the measured signal segments 26 a, 27 a, 26 c, and 27 c where there was no motion induced noise.
- n′′ ⁇ a (t) and n′′ ⁇ b (t) to the secondary signals n ⁇ a (t) and n ⁇ b (t) as estimated by a correlation canceler using a primary reference signal s′(t) can also be determined in accordance with the present invention.
- This subroutine is, another way to implement the steps illustrated in the flowchart of FIG. 9 for a monitor particularly adapted for pulse oximetry.
- the two signals are measured at two different wavelengths ⁇ a and ⁇ b, where ⁇ a is typically in the visible region and ⁇ b is typically in the infrared region.
- ⁇ a is typically in the visible region
- ⁇ b is typically in the infrared region.
- a first portion of the program performs the initialization of the registers 90 , 92 , 96 , and 98 and intermediate variable values as in the “INITIALIZED CORRELATION CANCELER” action block 120 .
- a second portion of the program performs the time updates of the delay element variables 110 with the value at the input of each delay element variable 110 is stored in the delay element variable 110 as in the “TIME UPDATE OF LEFT [Z ⁇ 1 ] ELEMENTS” action block 130 .
- the calculation of saturation is performed in a separate module.
- Various methods for calculation of the oxygen saturation are known to those skilled in the art. One such calculation is described in the articles by G. A. Mook, et al, and Michael R. Neuman cited above.
- the value of the saturation is determined similarly to equations (72) through (79) wherein measurements at times t 1 and t 2 are made at different, yet proximate times over which the saturation is relatively constant.
- a third portion of the subroutine calculates the primary reference or secondary reference, as in the “CALCULATE PRIMARY OR SECONDARY REFERENCE (s′(t) or n′(t)) FOR TWO MEASURED SIGNAL SAMPLES” action block 140 for the signals S ⁇ a (t) and S ⁇ b (t) using the proportionality constants r a (t) and r v (t) determined by the constant saturation method as in equation (3).
- the saturation is calculated in a separate subroutine and a value of r a (t) or r v (t) is imported to the present subroutine for estimating either the primary portions s ⁇ a (t) and s ⁇ b (t) or the secondary portions n ⁇ a (t) and n ⁇ b (t) of the composite measured signals S ⁇ a (t) and S ⁇ b (t).
- a fourth portion of the program performs Z-stage update as in the “ZERO STAGE UPDATE” action block 150 where the Z-stage forward prediction error F o (t) and Z-stage backward prediction error b o (t) are set equal to the value of the reference signal n′(t) or s′(t) just calculated. Additionally zero-stage values of intermediate variables I o and ⁇ 0 (t)(nc[m].Fswsqr and nc[m].Bswsqr in the program) are calculated for use in setting registers 90 , 92 , 96 , and 98 values in the least-squares lattice predictor 70 in the regression filters 80 a and 80 b.
- NC_CELLS is a predetermined maximum value of iterations for the loop.
- a typical value for NC_CELLS is between 6 and 10, for example.
- the conditions of the loop are set such that the loop iterates a minimum of five times and continues to iterate until a test for conversion is met or m-NC_CELLS.
- the test for conversion is whether or not the sum of the weighted sum of four prediction errors plus the weighted sum of backward prediction errors is less than a small number, typically 0.00001 (i.e., I m (t)+ ⁇ m(t) ⁇ 0.00001).
- a sixth portion of the program calculates the forward and backward reflection coefficient ⁇ m,f (t) and ⁇ m,b (t) register 90 and 92 values (nc[m].fref and nc[m].bref in the program) as in the “ORDER UPDATE m th -STAGE OF LSL-PREDICTOR” action block 170 . Then forward and backward prediction errors f m (t) and b m (t) (nc[m].ferr and nc[m].berr in the program) are calculated.
- intermediate variables I m (t), ⁇ m(t), and ⁇ (t) (nc[m].Fswsqr, nc[m].Bswsqr, nc[m]. gamma in the program) are calculated.
- the first cycle of the loop uses the value for nc[0].Fswsqr and nc[0].Bswsqr calculated in the ZERO STAGE UPDATE portion of the program.
- a seventh portion of the program calculates the regression coefficient register 96 and 98 values ⁇ m, ⁇ a (t) and ⁇ m, ⁇ b (t) (nc[m].K_a and nc[m].K_b in the program) in both regression filters, as in the “ORDER UPDATE m th STAGE OF REGRESSION FILTER(S)” action block 180 .
- the loop iterates until the test for convergence is passed.
- the test for convergence of the joint process estimator is performed each time the loop iterates analogously to the “DONE” action block 190 . If the sum of the weighted sums of the forward and backward prediction errors I m (t)+ ⁇ m(t) is less than or equal to 0.00001, the loop terminates. Otherwise, sixth and seventh portions of the program repeat.
- the output of the present subroutine is a good approximation to the primary signals s′′ ⁇ a (t) and s′′ ⁇ b (t) or the secondary signals n′′ ⁇ a (t) and n′′ ⁇ b (t) for the set of samples S ⁇ a (t) and S ⁇ b (t) input to the program.
- a compilation of the outputs provides waves which are good approximations to the plethysmographic wave or motion artifact at each wavelength, ⁇ a and ⁇ b.
- a physiological monitor incorporating a processor of the present invention for determining a reference signal for use in a correlation canceler, such as an adaptive noise canceler, to remove or derive primary and secondary components from a physiological measurement
- a correlation canceler such as an adaptive noise canceler
- the signal processing techniques described in the present invention may be used to compute the arterial and venous blood oxygen saturations of a physiological system on a continuous or nearly continuous time basis. These calculations may be performed, regardless of whether or not the physiological system undergoes voluntary motion.
- correlation cancellation techniques other than joint process estimation may be used together with the reference signals of the present invention. These may include but are not limited to least mean square algorithms, wavelet transforms, spectral estimation techniques, neural networks, Weiner and Kalman filters among others.
- physiological monitors may employ the teachings of the present invention.
- Other types of physiological monitors include, but are in not limited to, electro cardiographs, blood pressure monitors, blood constituent monitors (other than oxygen saturation) monitors, capnographs, heart rate monitors, respiration monitors, or depth of anesthesia monitors.
- monitors which measure the pressure and quantity of a substance within the body such as a breathalizer, a drug monitor, a cholesterol monitor, a glucose monitor, a carbon dioxide monitor, a glucose monitor, or a carbon monoxide monitor may also employ the above described techniques.
- ECG electrocardiography
- the present invention has been described in terms of a physiological monitor, one skilled in the art will realize that the signal processing techniques of the present invention can be applied in many areas, including but not limited to the processing of a physiological signal.
- the present invention may be applied in any situation where a signal processor comprising a detector receives a first signal which includes a first primary signal portion and a first secondary signal portion and a second signal which includes a second primary signal portion and a second secondary signal portion.
- the signal processor of the present invention is readily applicable to numerous signal processing areas.
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US20070078354A1 (en) * | 2005-10-04 | 2007-04-05 | Welch Allyn, Inc. | Method and apparatus for removing baseline wander from an ECG signal |
US20070083094A1 (en) * | 2005-10-11 | 2007-04-12 | Colburn Joel C | Medical sensor and technique for using the same |
US7254433B2 (en) | 1991-03-07 | 2007-08-07 | Masimo Corporation | Signal processing apparatus |
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US20080058621A1 (en) * | 2004-08-11 | 2008-03-06 | Melker Richard J | Methods and Devices for Countering Grativity Induced Loss of Consciousness and Novel Pulse Oximeter Probes |
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US8366613B2 (en) | 2007-12-26 | 2013-02-05 | Covidien Lp | LED drive circuit for pulse oximetry and method for using same |
US8386002B2 (en) | 2005-09-30 | 2013-02-26 | Covidien Lp | Optically aligned pulse oximetry sensor and technique for using the same |
US8391941B2 (en) | 2009-07-17 | 2013-03-05 | Covidien Lp | System and method for memory switching for multiple configuration medical sensor |
US8396527B2 (en) | 2006-09-22 | 2013-03-12 | Covidien Lp | Medical sensor for reducing signal artifacts and technique for using the same |
US8417309B2 (en) | 2008-09-30 | 2013-04-09 | Covidien Lp | Medical sensor |
US8417310B2 (en) | 2009-08-10 | 2013-04-09 | Covidien Lp | Digital switching in multi-site sensor |
US8423112B2 (en) | 2008-09-30 | 2013-04-16 | Covidien Lp | Medical sensor and technique for using the same |
US8428675B2 (en) | 2009-08-19 | 2013-04-23 | Covidien Lp | Nanofiber adhesives used in medical devices |
US8433383B2 (en) | 2001-10-12 | 2013-04-30 | Covidien Lp | Stacked adhesive optical sensor |
US8437822B2 (en) | 2008-03-28 | 2013-05-07 | Covidien Lp | System and method for estimating blood analyte concentration |
US8442608B2 (en) | 2007-12-28 | 2013-05-14 | Covidien Lp | System and method for estimating physiological parameters by deconvolving artifacts |
US8452366B2 (en) | 2009-03-16 | 2013-05-28 | Covidien Lp | Medical monitoring device with flexible circuitry |
US8452364B2 (en) | 2007-12-28 | 2013-05-28 | Covidien LLP | System and method for attaching a sensor to a patient's skin |
US8483790B2 (en) | 2002-10-18 | 2013-07-09 | Covidien Lp | Non-adhesive oximeter sensor for sensitive skin |
US8509869B2 (en) | 2009-05-15 | 2013-08-13 | Covidien Lp | Method and apparatus for detecting and analyzing variations in a physiologic parameter |
US8505821B2 (en) | 2009-06-30 | 2013-08-13 | Covidien Lp | System and method for providing sensor quality assurance |
US8560034B1 (en) | 1993-10-06 | 2013-10-15 | Masimo Corporation | Signal processing apparatus |
US8577434B2 (en) | 2007-12-27 | 2013-11-05 | Covidien Lp | Coaxial LED light sources |
US8634891B2 (en) | 2009-05-20 | 2014-01-21 | Covidien Lp | Method and system for self regulation of sensor component contact pressure |
US8636667B2 (en) | 2009-07-06 | 2014-01-28 | Nellcor Puritan Bennett Ireland | Systems and methods for processing physiological signals in wavelet space |
US8649838B2 (en) | 2010-09-22 | 2014-02-11 | Covidien Lp | Wavelength switching for pulse oximetry |
US8666467B2 (en) | 2001-05-17 | 2014-03-04 | Lawrence A. Lynn | System and method for SPO2 instability detection and quantification |
US8862196B2 (en) | 2001-05-17 | 2014-10-14 | Lawrence A. Lynn | System and method for automatic detection of a plurality of SP02 time series pattern types |
US8897850B2 (en) | 2007-12-31 | 2014-11-25 | Covidien Lp | Sensor with integrated living hinge and spring |
US8914088B2 (en) | 2008-09-30 | 2014-12-16 | Covidien Lp | Medical sensor and technique for using the same |
US8983800B2 (en) | 2003-01-13 | 2015-03-17 | Covidien Lp | Selection of preset filter parameters based on signal quality |
US9010634B2 (en) | 2009-06-30 | 2015-04-21 | Covidien Lp | System and method for linking patient data to a patient and providing sensor quality assurance |
US9031793B2 (en) | 2001-05-17 | 2015-05-12 | Lawrence A. Lynn | Centralized hospital monitoring system for automatically detecting upper airway instability and for preventing and aborting adverse drug reactions |
US9042952B2 (en) | 1997-01-27 | 2015-05-26 | Lawrence A. Lynn | System and method for automatic detection of a plurality of SPO2 time series pattern types |
US9053222B2 (en) | 2002-05-17 | 2015-06-09 | Lawrence A. Lynn | Patient safety processor |
US9198586B2 (en) | 2002-06-20 | 2015-12-01 | University Of Florida Research Foundation, Inc. | Methods of monitoring oxygenation by positive end expiratory pressure using photoplethysmography |
US9468378B2 (en) | 1997-01-27 | 2016-10-18 | Lawrence A. Lynn | Airway instability detection system and method |
US9521971B2 (en) | 1997-07-14 | 2016-12-20 | Lawrence A. Lynn | System and method for automatic detection of a plurality of SPO2 time series pattern types |
US9950112B2 (en) | 2010-08-17 | 2018-04-24 | University Of Florida Research Foundation, Incorporated | Intelligent drug and/or fluid delivery system to optimizing medical treatment or therapy using pharmacodynamic and/or pharamacokinetic data |
US10159412B2 (en) | 2010-12-01 | 2018-12-25 | Cercacor Laboratories, Inc. | Handheld processing device including medical applications for minimally and non invasive glucose measurements |
US10354753B2 (en) | 2001-05-17 | 2019-07-16 | Lawrence A. Lynn | Medical failure pattern search engine |
US11273283B2 (en) | 2017-12-31 | 2022-03-15 | Neuroenhancement Lab, LLC | Method and apparatus for neuroenhancement to enhance emotional response |
US11364361B2 (en) | 2018-04-20 | 2022-06-21 | Neuroenhancement Lab, LLC | System and method for inducing sleep by transplanting mental states |
US11452839B2 (en) | 2018-09-14 | 2022-09-27 | Neuroenhancement Lab, LLC | System and method of improving sleep |
US20220330842A1 (en) * | 2010-07-22 | 2022-10-20 | Masimo Corporation | Non-invasive blood pressure measurement system |
US11717686B2 (en) | 2017-12-04 | 2023-08-08 | Neuroenhancement Lab, LLC | Method and apparatus for neuroenhancement to facilitate learning and performance |
US11723579B2 (en) | 2017-09-19 | 2023-08-15 | Neuroenhancement Lab, LLC | Method and apparatus for neuroenhancement |
US11786694B2 (en) | 2019-05-24 | 2023-10-17 | NeuroLight, Inc. | Device, method, and app for facilitating sleep |
Families Citing this family (653)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5638818A (en) * | 1991-03-21 | 1997-06-17 | Masimo Corporation | Low noise optical probe |
US6371921B1 (en) * | 1994-04-15 | 2002-04-16 | Masimo Corporation | System and method of determining whether to recalibrate a blood pressure monitor |
US5758644A (en) * | 1995-06-07 | 1998-06-02 | Masimo Corporation | Manual and automatic probe calibration |
US6931268B1 (en) | 1995-06-07 | 2005-08-16 | Masimo Laboratories, Inc. | Active pulse blood constituent monitoring |
US6027452A (en) * | 1996-06-26 | 2000-02-22 | Vital Insite, Inc. | Rapid non-invasive blood pressure measuring device |
US6229856B1 (en) | 1997-04-14 | 2001-05-08 | Masimo Corporation | Method and apparatus for demodulating signals in a pulse oximetry system |
US6525386B1 (en) * | 1998-03-10 | 2003-02-25 | Masimo Corporation | Non-protruding optoelectronic lens |
US6334065B1 (en) * | 1998-06-03 | 2001-12-25 | Masimo Corporation | Stereo pulse oximeter |
WO2000001294A1 (en) * | 1998-07-04 | 2000-01-13 | Whitland Research Limited | Non-invasive measurement of blood analytes |
US7400918B2 (en) * | 1998-07-04 | 2008-07-15 | Edwards Lifesciences | Measurement of blood oxygen saturation |
US7548787B2 (en) | 2005-08-03 | 2009-06-16 | Kamilo Feher | Medical diagnostic and communication system |
AU5180499A (en) | 1998-08-13 | 2000-03-06 | Whitland Research Limited | Optical device |
USRE41912E1 (en) | 1998-10-15 | 2010-11-02 | Masimo Corporation | Reusable pulse oximeter probe and disposable bandage apparatus |
US7245953B1 (en) | 1999-04-12 | 2007-07-17 | Masimo Corporation | Reusable pulse oximeter probe and disposable bandage apparatii |
US6721585B1 (en) | 1998-10-15 | 2004-04-13 | Sensidyne, Inc. | Universal modular pulse oximeter probe for use with reusable and disposable patient attachment devices |
US7364577B2 (en) | 2002-02-11 | 2008-04-29 | Sherwood Services Ag | Vessel sealing system |
US6463311B1 (en) * | 1998-12-30 | 2002-10-08 | Masimo Corporation | Plethysmograph pulse recognition processor |
US6684090B2 (en) | 1999-01-07 | 2004-01-27 | Masimo Corporation | Pulse oximetry data confidence indicator |
US20020140675A1 (en) * | 1999-01-25 | 2002-10-03 | Ali Ammar Al | System and method for altering a display mode based on a gravity-responsive sensor |
US6770028B1 (en) * | 1999-01-25 | 2004-08-03 | Masimo Corporation | Dual-mode pulse oximeter |
US6360114B1 (en) | 1999-03-25 | 2002-03-19 | Masimo Corporation | Pulse oximeter probe-off detector |
US7260369B2 (en) | 2005-08-03 | 2007-08-21 | Kamilo Feher | Location finder, tracker, communication and remote control system |
US9307407B1 (en) | 1999-08-09 | 2016-04-05 | Kamilo Feher | DNA and fingerprint authentication of mobile devices |
US9813270B2 (en) | 1999-08-09 | 2017-11-07 | Kamilo Feher | Heart rate sensor and medical diagnostics wireless devices |
US9373251B2 (en) | 1999-08-09 | 2016-06-21 | Kamilo Feher | Base station devices and automobile wireless communication systems |
US6515273B2 (en) * | 1999-08-26 | 2003-02-04 | Masimo Corporation | System for indicating the expiration of the useful operating life of a pulse oximetry sensor |
US6377829B1 (en) | 1999-12-09 | 2002-04-23 | Masimo Corporation | Resposable pulse oximetry sensor |
US6950687B2 (en) | 1999-12-09 | 2005-09-27 | Masimo Corporation | Isolation and communication element for a resposable pulse oximetry sensor |
EP2324761A3 (en) | 2000-04-17 | 2014-06-18 | Adidas AG | Systems and methods for ambulatory monitoring of physiological signals |
US6430525B1 (en) | 2000-06-05 | 2002-08-06 | Masimo Corporation | Variable mode averager |
EP2064989B1 (en) | 2000-08-18 | 2012-03-21 | Masimo Corporation | Dual-mode pulse oximeter |
US6640116B2 (en) * | 2000-08-18 | 2003-10-28 | Masimo Corporation | Optical spectroscopy pathlength measurement system |
US20020083461A1 (en) | 2000-11-22 | 2002-06-27 | Hutcheson Stewart Douglas | Method and system for providing interactive services over a wireless communications network |
EP1372507B1 (en) | 2001-04-06 | 2006-06-28 | Sherwood Services AG | Vessel sealer and divider with non-conductive stop members |
US7056967B2 (en) * | 2001-04-10 | 2006-06-06 | Ciba Specialty Chemicals Corporation | Stabilized medium and high voltage cable insulation composition |
US6985764B2 (en) | 2001-05-03 | 2006-01-10 | Masimo Corporation | Flex circuit shielded optical sensor |
US6850787B2 (en) * | 2001-06-29 | 2005-02-01 | Masimo Laboratories, Inc. | Signal component processor |
US6697658B2 (en) | 2001-07-02 | 2004-02-24 | Masimo Corporation | Low power pulse oximeter |
US7628760B2 (en) * | 2007-02-28 | 2009-12-08 | Semler Scientific, Inc. | Circulation monitoring system and method |
US6780158B2 (en) * | 2001-12-14 | 2004-08-24 | Nihon Kohden Corporation | Signal processing method and pulse wave signal processing method |
US20030212312A1 (en) * | 2002-01-07 | 2003-11-13 | Coffin James P. | Low noise patient cable |
US6934570B2 (en) * | 2002-01-08 | 2005-08-23 | Masimo Corporation | Physiological sensor combination |
US7355512B1 (en) * | 2002-01-24 | 2008-04-08 | Masimo Corporation | Parallel alarm processor |
US6822564B2 (en) * | 2002-01-24 | 2004-11-23 | Masimo Corporation | Parallel measurement alarm processor |
WO2003065557A2 (en) * | 2002-01-25 | 2003-08-07 | Masimo Corporation | Power supply rail controller |
US6961598B2 (en) * | 2002-02-22 | 2005-11-01 | Masimo Corporation | Pulse and active pulse spectraphotometry |
US7509494B2 (en) * | 2002-03-01 | 2009-03-24 | Masimo Corporation | Interface cable |
US7505877B2 (en) * | 2002-03-08 | 2009-03-17 | Johnson Controls Technology Company | System and method for characterizing a system |
US6850788B2 (en) | 2002-03-25 | 2005-02-01 | Masimo Corporation | Physiological measurement communications adapter |
US7280944B1 (en) * | 2002-06-20 | 2007-10-09 | United States Of America As Represented By The Secretary Of The Navy | Multichannel adaptive filter for noise and/or interference cancellation |
US7738935B1 (en) * | 2002-07-09 | 2010-06-15 | Pacesetter, Inc. | Methods and devices for reduction of motion-induced noise in pulse oximetry |
US7096054B2 (en) * | 2002-08-01 | 2006-08-22 | Masimo Corporation | Low noise optical housing |
US7366564B2 (en) * | 2002-08-23 | 2008-04-29 | The United States Of America As Represented By The Secretary Of The Navy | Nonlinear blind demixing of single pixel underlying radiation sources and digital spectrum local thermometer |
US7274955B2 (en) * | 2002-09-25 | 2007-09-25 | Masimo Corporation | Parameter compensated pulse oximeter |
US7142901B2 (en) * | 2002-09-25 | 2006-11-28 | Masimo Corporation | Parameter compensated physiological monitor |
US7096052B2 (en) * | 2002-10-04 | 2006-08-22 | Masimo Corporation | Optical probe including predetermined emission wavelength based on patient type |
US7027849B2 (en) * | 2002-11-22 | 2006-04-11 | Masimo Laboratories, Inc. | Blood parameter measurement system |
US6970792B1 (en) * | 2002-12-04 | 2005-11-29 | Masimo Laboratories, Inc. | Systems and methods for determining blood oxygen saturation values using complex number encoding |
US7919713B2 (en) * | 2007-04-16 | 2011-04-05 | Masimo Corporation | Low noise oximetry cable including conductive cords |
US7006856B2 (en) | 2003-01-10 | 2006-02-28 | Nellcor Puritan Bennett Incorporated | Signal quality metrics design for qualifying data for a physiological monitor |
US7225006B2 (en) | 2003-01-23 | 2007-05-29 | Masimo Corporation | Attachment and optical probe |
US6920345B2 (en) | 2003-01-24 | 2005-07-19 | Masimo Corporation | Optical sensor including disposable and reusable elements |
US7809433B2 (en) * | 2005-08-09 | 2010-10-05 | Adidas Ag | Method and system for limiting interference in electroencephalographic signals |
US20050055276A1 (en) * | 2003-06-26 | 2005-03-10 | Kiani Massi E. | Sensor incentive method |
US7455643B1 (en) | 2003-07-07 | 2008-11-25 | Nellcor Puritan Bennett Ireland | Continuous non-invasive blood pressure measurement apparatus and methods providing automatic recalibration |
US7003338B2 (en) | 2003-07-08 | 2006-02-21 | Masimo Corporation | Method and apparatus for reducing coupling between signals |
US7500950B2 (en) * | 2003-07-25 | 2009-03-10 | Masimo Corporation | Multipurpose sensor port |
US7254431B2 (en) * | 2003-08-28 | 2007-08-07 | Masimo Corporation | Physiological parameter tracking system |
US20050049468A1 (en) * | 2003-09-03 | 2005-03-03 | Sven-Erik Carlson | Increasing the performance of an optical pulsoximeter |
US20070110229A1 (en) * | 2004-02-25 | 2007-05-17 | Ternarylogic, Llc | Ternary and Multi-Value Digital Signal Scramblers, Descramblers and Sequence of Generators |
US20110064214A1 (en) * | 2003-09-09 | 2011-03-17 | Ternarylogic Llc | Methods and Apparatus in Alternate Finite Field Based Coders and Decoders |
US7643632B2 (en) * | 2004-02-25 | 2010-01-05 | Ternarylogic Llc | Ternary and multi-value digital signal scramblers, descramblers and sequence generators |
US7505589B2 (en) * | 2003-09-09 | 2009-03-17 | Temarylogic, Llc | Ternary and higher multi-value digital scramblers/descramblers |
US8577026B2 (en) | 2010-12-29 | 2013-11-05 | Ternarylogic Llc | Methods and apparatus in alternate finite field based coders and decoders |
US7254434B2 (en) * | 2003-10-14 | 2007-08-07 | Masimo Corporation | Variable pressure reusable sensor |
US7483729B2 (en) | 2003-11-05 | 2009-01-27 | Masimo Corporation | Pulse oximeter access apparatus and method |
US7373193B2 (en) * | 2003-11-07 | 2008-05-13 | Masimo Corporation | Pulse oximetry data capture system |
US7367976B2 (en) | 2003-11-17 | 2008-05-06 | Sherwood Services Ag | Bipolar forceps having monopolar extension |
US7740591B1 (en) | 2003-12-01 | 2010-06-22 | Ric Investments, Llc | Apparatus and method for monitoring pressure related changes in the extra-thoracic arterial circulatory system |
US7280858B2 (en) * | 2004-01-05 | 2007-10-09 | Masimo Corporation | Pulse oximetry sensor |
US8491492B2 (en) | 2004-02-05 | 2013-07-23 | Earlysense Ltd. | Monitoring a condition of a subject |
US8942779B2 (en) | 2004-02-05 | 2015-01-27 | Early Sense Ltd. | Monitoring a condition of a subject |
US8403865B2 (en) * | 2004-02-05 | 2013-03-26 | Earlysense Ltd. | Prediction and monitoring of clinical episodes |
US7077810B2 (en) | 2004-02-05 | 2006-07-18 | Earlysense Ltd. | Techniques for prediction and monitoring of respiration-manifested clinical episodes |
US9176121B2 (en) * | 2004-02-13 | 2015-11-03 | Roche Diagnostics Hematology, Inc. | Identification of blood elements using inverted microscopy |
US7371981B2 (en) * | 2004-02-20 | 2008-05-13 | Masimo Corporation | Connector switch |
US7580472B2 (en) * | 2004-02-25 | 2009-08-25 | Ternarylogic Llc | Generation and detection of non-binary digital sequences |
US8374289B2 (en) | 2004-02-25 | 2013-02-12 | Ternarylogic Llc | Generation and detection of non-binary digital sequences |
US7696785B2 (en) * | 2004-02-25 | 2010-04-13 | Ternarylogic Llc | Implementing logic functions with non-magnitude based physical phenomena |
US7218144B2 (en) * | 2004-02-25 | 2007-05-15 | Ternarylogic Llc | Single and composite binary and multi-valued logic functions from gates and inverters |
US7212847B2 (en) * | 2004-02-25 | 2007-05-01 | Nellcor Puritan Bennett Llc | Delta-sigma modulator for outputting analog representation of physiological signal |
US7548092B2 (en) | 2004-02-25 | 2009-06-16 | Ternarylogic Llc | Implementing logic functions with non-magnitude based physical phenomena |
US7438683B2 (en) | 2004-03-04 | 2008-10-21 | Masimo Corporation | Application identification sensor |
US7194293B2 (en) | 2004-03-08 | 2007-03-20 | Nellcor Puritan Bennett Incorporated | Selection of ensemble averaging weights for a pulse oximeter based on signal quality metrics |
US8611977B2 (en) * | 2004-03-08 | 2013-12-17 | Covidien Lp | Method and apparatus for optical detection of mixed venous and arterial blood pulsation in tissue |
EP1722676B1 (en) * | 2004-03-08 | 2012-12-19 | Masimo Corporation | Physiological parameter system |
US7277741B2 (en) * | 2004-03-09 | 2007-10-02 | Nellcor Puritan Bennett Incorporated | Pulse oximetry motion artifact rejection using near infrared absorption by water |
US20050234317A1 (en) * | 2004-03-19 | 2005-10-20 | Kiani Massi E | Low power and personal pulse oximetry systems |
US7292883B2 (en) * | 2004-03-31 | 2007-11-06 | Masimo Corporation | Physiological assessment system |
CA2464029A1 (en) * | 2004-04-08 | 2005-10-08 | Valery Telfort | Non-invasive ventilation monitor |
JP4444008B2 (ja) * | 2004-06-02 | 2010-03-31 | パナソニック株式会社 | 超音波診断装置 |
US9492084B2 (en) | 2004-06-18 | 2016-11-15 | Adidas Ag | Systems and methods for monitoring subjects in potential physiological distress |
US20060021003A1 (en) * | 2004-06-23 | 2006-01-26 | Janus Software, Inc | Biometric authentication system |
FR2872518B1 (fr) * | 2004-07-02 | 2007-07-27 | Usinor Sa | Procede de controle du bullage en poche et installation de mise en oeuvre |
US9341565B2 (en) * | 2004-07-07 | 2016-05-17 | Masimo Corporation | Multiple-wavelength physiological monitor |
US7343186B2 (en) * | 2004-07-07 | 2008-03-11 | Masimo Laboratories, Inc. | Multi-wavelength physiological monitor |
US7937128B2 (en) * | 2004-07-09 | 2011-05-03 | Masimo Corporation | Cyanotic infant sensor |
US7562106B2 (en) * | 2004-08-07 | 2009-07-14 | Ternarylogic Llc | Multi-value digital calculating circuits, including multipliers |
US8036727B2 (en) | 2004-08-11 | 2011-10-11 | Glt Acquisition Corp. | Methods for noninvasively measuring analyte levels in a subject |
US7254429B2 (en) * | 2004-08-11 | 2007-08-07 | Glucolight Corporation | Method and apparatus for monitoring glucose levels in a biological tissue |
US7976472B2 (en) * | 2004-09-07 | 2011-07-12 | Masimo Corporation | Noninvasive hypovolemia monitor |
US20100164548A1 (en) * | 2004-09-08 | 2010-07-01 | Ternarylogic Llc | Implementing Logic Functions With Non-Magnitude Based Physical Phenomena |
CA2481631A1 (en) * | 2004-09-15 | 2006-03-15 | Dspfactory Ltd. | Method and system for physiological signal processing |
US9504410B2 (en) | 2005-09-21 | 2016-11-29 | Adidas Ag | Band-like garment for physiological monitoring |
US20060073719A1 (en) * | 2004-09-29 | 2006-04-06 | Kiani Massi E | Multiple key position plug |
CN103083768B (zh) | 2004-10-06 | 2016-07-06 | 瑞思迈有限公司 | 用于非侵入性监测睡眠紊乱呼吸中呼吸参数的方法和设备 |
US20060111621A1 (en) * | 2004-11-03 | 2006-05-25 | Andreas Coppi | Musical personal trainer |
US20060189871A1 (en) * | 2005-02-18 | 2006-08-24 | Ammar Al-Ali | Portable patient monitor |
US8116839B1 (en) | 2005-02-25 | 2012-02-14 | General Electric Company | System for detecting potential probe malfunction conditions in a pulse oximeter |
WO2006094171A1 (en) | 2005-03-01 | 2006-09-08 | Masimo Laboratories, Inc. | Multiple wavelength sensor drivers |
US7392075B2 (en) | 2005-03-03 | 2008-06-24 | Nellcor Puritan Bennett Incorporated | Method for enhancing pulse oximetry calculations in the presence of correlated artifacts |
CN100450437C (zh) * | 2005-03-10 | 2009-01-14 | 深圳迈瑞生物医疗电子股份有限公司 | 低灌注下测量血氧的方法 |
US7937129B2 (en) * | 2005-03-21 | 2011-05-03 | Masimo Corporation | Variable aperture sensor |
US7678057B2 (en) * | 2005-03-24 | 2010-03-16 | Intelomed, Inc. | Device and system that identifies cardiovascular insufficiency |
US7635337B2 (en) * | 2005-03-24 | 2009-12-22 | Ge Healthcare Finland Oy | Determination of clinical stress of a subject in pulse oximetry |
US8423108B2 (en) * | 2005-03-24 | 2013-04-16 | Intelomed, Inc. | Device and system that identifies cardiovascular insufficiency |
WO2006110859A2 (en) | 2005-04-13 | 2006-10-19 | Glucolight Corporation | Method for data reduction and calibration of an oct-based blood glucose monitor |
ES2425779T3 (es) * | 2005-05-06 | 2013-10-17 | Vasonova, Inc. | Aparato para el guiado y posicionamiento de un dispositivo endovascular |
AU2006251609B2 (en) | 2005-05-20 | 2012-09-20 | Adidas Ag | Methods and systems for determining dynamic hyperinflation |
US20070038060A1 (en) * | 2005-06-09 | 2007-02-15 | Cerwin Stephen A | Identifying and treating bodily tissues using electromagnetically induced, time-reversed, acoustic signals |
US7734466B2 (en) * | 2005-06-20 | 2010-06-08 | Motorola, Inc. | Reduced complexity recursive least square lattice structure adaptive filter by means of limited recursion of the backward and forward error prediction squares |
CN101496001B (zh) * | 2005-06-20 | 2011-08-03 | 摩托罗拉移动公司 | 通过后向和前向误差预测平方的有限递归的减少复杂度的递归最小二乘格型结构自适应滤波器 |
US7403806B2 (en) | 2005-06-28 | 2008-07-22 | General Electric Company | System for prefiltering a plethysmographic signal |
US7515949B2 (en) * | 2005-06-29 | 2009-04-07 | General Electric Company | Wavelet transform of a plethysmographic signal |
US7627357B2 (en) * | 2005-06-30 | 2009-12-01 | General Electric Company | System and method for non-invasive glucose monitoring |
US8033996B2 (en) | 2005-07-26 | 2011-10-11 | Adidas Ag | Computer interfaces including physiologically guided avatars |
US10009956B1 (en) | 2017-09-02 | 2018-06-26 | Kamilo Feher | OFDM, 3G and 4G cellular multimode systems and wireless mobile networks |
US7280810B2 (en) | 2005-08-03 | 2007-10-09 | Kamilo Feher | Multimode communication system |
US20070073116A1 (en) * | 2005-08-17 | 2007-03-29 | Kiani Massi E | Patient identification using physiological sensor |
JP4830693B2 (ja) * | 2005-08-24 | 2011-12-07 | 日本光電工業株式会社 | 酸素飽和度測定装置及び測定方法 |
CN100496388C (zh) * | 2005-08-31 | 2009-06-10 | 深圳迈瑞生物医疗电子股份有限公司 | 利用信号变换计算血压的装置 |
US7725146B2 (en) | 2005-09-29 | 2010-05-25 | Nellcor Puritan Bennett Llc | System and method for pre-processing waveforms |
US7725147B2 (en) * | 2005-09-29 | 2010-05-25 | Nellcor Puritan Bennett Llc | System and method for removing artifacts from waveforms |
WO2007041766A1 (en) * | 2005-10-10 | 2007-04-19 | Compumedics Limited | Adaptive real-time line noise suppression for electrical or magnetic physiological signals |
US7962188B2 (en) | 2005-10-14 | 2011-06-14 | Masimo Corporation | Robust alarm system |
US20070100220A1 (en) * | 2005-10-28 | 2007-05-03 | Baker Clark R Jr | Adjusting parameters used in pulse oximetry analysis |
US7184809B1 (en) | 2005-11-08 | 2007-02-27 | Woolsthorpe Technologies, Llc | Pulse amplitude indexing method and apparatus |
US7879355B2 (en) * | 2005-11-08 | 2011-02-01 | Plensat Llc | Method and system for treatment of eating disorders |
US7215987B1 (en) | 2005-11-08 | 2007-05-08 | Woolsthorpe Technologies | Method and apparatus for processing signals reflecting physiological characteristics |
EP1956968B1 (en) | 2005-11-29 | 2020-04-15 | Masimo Corporation | Optical sensor including disposable and reusable elements |
EP1962671A2 (en) * | 2005-12-03 | 2008-09-03 | Masimo Corporation | Physiological alarm notification system |
US8050730B2 (en) * | 2005-12-23 | 2011-11-01 | Shenzhen Mindray Bio-Medical Electrics Co., Ltd. | Method and apparatus for eliminating interference in pulse oxygen measurement |
US7990382B2 (en) | 2006-01-03 | 2011-08-02 | Masimo Corporation | Virtual display |
US8182443B1 (en) | 2006-01-17 | 2012-05-22 | Masimo Corporation | Drug administration controller |
US8762733B2 (en) | 2006-01-30 | 2014-06-24 | Adidas Ag | System and method for identity confirmation using physiologic biometrics to determine a physiologic fingerprint |
US20070244377A1 (en) * | 2006-03-14 | 2007-10-18 | Cozad Jenny L | Pulse oximeter sleeve |
US8219172B2 (en) | 2006-03-17 | 2012-07-10 | Glt Acquisition Corp. | System and method for creating a stable optical interface |
US7752533B2 (en) * | 2006-03-28 | 2010-07-06 | Sony Corporation | Systems and methods for improving radio frequency signal reception |
US7941199B2 (en) * | 2006-05-15 | 2011-05-10 | Masimo Laboratories, Inc. | Sepsis monitor |
US8998809B2 (en) * | 2006-05-15 | 2015-04-07 | Cercacor Laboratories, Inc. | Systems and methods for calibrating minimally invasive and non-invasive physiological sensor devices |
US9176141B2 (en) | 2006-05-15 | 2015-11-03 | Cercacor Laboratories, Inc. | Physiological monitor calibration system |
FI119542B (fi) * | 2006-05-18 | 2008-12-31 | Polar Electro Oy | Kannettava elektroninen laite verenpainepulssin optiseksi mittaamiseksi |
US8028701B2 (en) | 2006-05-31 | 2011-10-04 | Masimo Corporation | Respiratory monitoring |
US10188348B2 (en) | 2006-06-05 | 2019-01-29 | Masimo Corporation | Parameter upgrade system |
US20080039735A1 (en) * | 2006-06-06 | 2008-02-14 | Hickerson Barry L | Respiratory monitor display |
CA2655782A1 (en) * | 2006-06-13 | 2007-12-21 | Elfi-Tech Ltd. | System and method for measurement of biological parameters of a subject |
US8380271B2 (en) | 2006-06-15 | 2013-02-19 | Covidien Lp | System and method for generating customizable audible beep tones and alarms |
US8475387B2 (en) | 2006-06-20 | 2013-07-02 | Adidas Ag | Automatic and ambulatory monitoring of congestive heart failure patients |
US20080064965A1 (en) * | 2006-09-08 | 2008-03-13 | Jay Gregory D | Devices and methods for measuring pulsus paradoxus |
US7610085B2 (en) * | 2006-09-12 | 2009-10-27 | Allgeyer Dean O | Simplified ECG monitoring system |
USD609193S1 (en) | 2007-10-12 | 2010-02-02 | Masimo Corporation | Connector assembly |
USD614305S1 (en) | 2008-02-29 | 2010-04-20 | Masimo Corporation | Connector assembly |
US8315683B2 (en) * | 2006-09-20 | 2012-11-20 | Masimo Corporation | Duo connector patient cable |
US8457707B2 (en) | 2006-09-20 | 2013-06-04 | Masimo Corporation | Congenital heart disease monitor |
US8298154B2 (en) * | 2007-01-10 | 2012-10-30 | Starr Life Sciences Corporation | Techniques for accurately deriving physiologic parameters of a subject from photoplethysmographic measurements |
US9161696B2 (en) | 2006-09-22 | 2015-10-20 | Masimo Corporation | Modular patient monitor |
US20080103375A1 (en) * | 2006-09-22 | 2008-05-01 | Kiani Massi E | Patient monitor user interface |
US8840549B2 (en) | 2006-09-22 | 2014-09-23 | Masimo Corporation | Modular patient monitor |
US20080076977A1 (en) * | 2006-09-26 | 2008-03-27 | Nellcor Puritan Bennett Inc. | Patient monitoring device snapshot feature system and method |
US8123695B2 (en) | 2006-09-27 | 2012-02-28 | Nellcor Puritan Bennett Llc | Method and apparatus for detection of venous pulsation |
US20080094228A1 (en) * | 2006-10-12 | 2008-04-24 | Welch James P | Patient monitor using radio frequency identification tags |
US7880626B2 (en) | 2006-10-12 | 2011-02-01 | Masimo Corporation | System and method for monitoring the life of a physiological sensor |
US8265723B1 (en) | 2006-10-12 | 2012-09-11 | Cercacor Laboratories, Inc. | Oximeter probe off indicator defining probe off space |
US9861305B1 (en) | 2006-10-12 | 2018-01-09 | Masimo Corporation | Method and apparatus for calibration to reduce coupling between signals in a measurement system |
US8255026B1 (en) | 2006-10-12 | 2012-08-28 | Masimo Corporation, Inc. | Patient monitor capable of monitoring the quality of attached probes and accessories |
US8280473B2 (en) * | 2006-10-12 | 2012-10-02 | Masino Corporation, Inc. | Perfusion index smoother |
US9192329B2 (en) | 2006-10-12 | 2015-11-24 | Masimo Corporation | Variable mode pulse indicator |
WO2008055078A2 (en) | 2006-10-27 | 2008-05-08 | Vivometrics, Inc. | Identification of emotional states using physiological responses |
DE602006015328D1 (de) * | 2006-11-03 | 2010-08-19 | Psytechnics Ltd | Abtastfehlerkompensation |
WO2008058328A1 (en) * | 2006-11-13 | 2008-05-22 | Resmed Ltd | Systems, methods, and/or apparatuses for non-invasive monitoring of respiratory parameters in sleep disordered breathing |
US8600467B2 (en) | 2006-11-29 | 2013-12-03 | Cercacor Laboratories, Inc. | Optical sensor including disposable and reusable elements |
EP2096994B1 (en) | 2006-12-09 | 2018-10-03 | Masimo Corporation | Plethysmograph variability determination |
US7791155B2 (en) * | 2006-12-22 | 2010-09-07 | Masimo Laboratories, Inc. | Detector shield |
US8852094B2 (en) | 2006-12-22 | 2014-10-07 | Masimo Corporation | Physiological parameter system |
US20130085354A1 (en) * | 2007-01-10 | 2013-04-04 | Starr Life Sciences Corp. | Techniques for accurately deriving physiologic parameters of a subject from photoplethysmographic measurements |
US8652060B2 (en) * | 2007-01-20 | 2014-02-18 | Masimo Corporation | Perfusion trend indicator |
US7846104B2 (en) * | 2007-02-08 | 2010-12-07 | Heart Force Medical Inc. | Monitoring physiological condition and detecting abnormalities |
US20090093687A1 (en) * | 2007-03-08 | 2009-04-09 | Telfort Valery G | Systems and methods for determining a physiological condition using an acoustic monitor |
US8229530B2 (en) | 2007-03-09 | 2012-07-24 | Nellcor Puritan Bennett Llc | System and method for detection of venous pulsation |
US20080221416A1 (en) * | 2007-03-09 | 2008-09-11 | Nellcor Puritan Bennett Llc | System and method for detection of macular degeneration using spectrophotometry |
US20080221418A1 (en) * | 2007-03-09 | 2008-09-11 | Masimo Corporation | Noninvasive multi-parameter patient monitor |
US8221326B2 (en) | 2007-03-09 | 2012-07-17 | Nellcor Puritan Bennett Llc | Detection of oximetry sensor sites based on waveform characteristics |
US8109882B2 (en) | 2007-03-09 | 2012-02-07 | Nellcor Puritan Bennett Llc | System and method for venous pulsation detection using near infrared wavelengths |
US8175665B2 (en) * | 2007-03-09 | 2012-05-08 | Nellcor Puritan Bennett Llc | Method and apparatus for spectroscopic tissue analyte measurement |
EP2476369B1 (en) | 2007-03-27 | 2014-10-01 | Masimo Laboratories, Inc. | Multiple wavelength optical sensor |
US8374665B2 (en) | 2007-04-21 | 2013-02-12 | Cercacor Laboratories, Inc. | Tissue profile wellness monitor |
US8585607B2 (en) | 2007-05-02 | 2013-11-19 | Earlysense Ltd. | Monitoring, predicting and treating clinical episodes |
US8602997B2 (en) | 2007-06-12 | 2013-12-10 | Sotera Wireless, Inc. | Body-worn system for measuring continuous non-invasive blood pressure (cNIBP) |
EP2162059B1 (en) | 2007-06-12 | 2021-01-13 | Sotera Wireless, Inc. | Vital sign monitor and method for measuring blood pressure using optical, electrical, and pressure waveforms |
US11330988B2 (en) | 2007-06-12 | 2022-05-17 | Sotera Wireless, Inc. | Body-worn system for measuring continuous non-invasive blood pressure (cNIBP) |
US11607152B2 (en) | 2007-06-12 | 2023-03-21 | Sotera Wireless, Inc. | Optical sensors for use in vital sign monitoring |
CN101688834A (zh) * | 2007-06-21 | 2010-03-31 | 皇家飞利浦电子股份有限公司 | 具有光源和光检测器的微电子传感器设备 |
US8764671B2 (en) * | 2007-06-28 | 2014-07-01 | Masimo Corporation | Disposable active pulse sensor |
US8918290B2 (en) * | 2007-08-27 | 2014-12-23 | Key To Metals Ag | Method and system to identify metal alloys |
US8057398B2 (en) * | 2007-08-31 | 2011-11-15 | Apdm, Inc. | Method, system, and apparatus for cardiovascular signal analysis, modeling, and monitoring |
US8376952B2 (en) * | 2007-09-07 | 2013-02-19 | The Nielsen Company (Us), Llc. | Method and apparatus for sensing blood oxygen |
US8048040B2 (en) | 2007-09-13 | 2011-11-01 | Masimo Corporation | Fluid titration system |
US9186089B2 (en) | 2007-09-14 | 2015-11-17 | Medtronic Monitoring, Inc. | Injectable physiological monitoring system |
US8460189B2 (en) | 2007-09-14 | 2013-06-11 | Corventis, Inc. | Adherent cardiac monitor with advanced sensing capabilities |
EP2194858B1 (en) | 2007-09-14 | 2017-11-22 | Corventis, Inc. | Medical device automatic start-up upon contact to patient tissue |
EP2194847A1 (en) | 2007-09-14 | 2010-06-16 | Corventis, Inc. | Adherent device with multiple physiological sensors |
WO2009036333A1 (en) | 2007-09-14 | 2009-03-19 | Corventis, Inc. | Dynamic pairing of patients to data collection gateways |
EP2200512A1 (en) | 2007-09-14 | 2010-06-30 | Corventis, Inc. | Adherent device for respiratory monitoring and sleep disordered breathing |
EP2200499B1 (en) | 2007-09-14 | 2019-05-01 | Medtronic Monitoring, Inc. | Multi-sensor patient monitor to detect impending cardiac decompensation |
US8355766B2 (en) * | 2007-10-12 | 2013-01-15 | Masimo Corporation | Ceramic emitter substrate |
WO2009049254A2 (en) | 2007-10-12 | 2009-04-16 | Masimo Corporation | Systems and methods for storing, analyzing, and retrieving medical data |
WO2009049101A1 (en) * | 2007-10-12 | 2009-04-16 | Masimo Corporation | Connector assembly |
US8310336B2 (en) | 2008-10-10 | 2012-11-13 | Masimo Corporation | Systems and methods for storing, analyzing, retrieving and displaying streaming medical data |
US7925347B1 (en) * | 2007-11-26 | 2011-04-12 | Pacesetter, Inc. | Assessment of cardiac output by implantable medical device |
US8128569B1 (en) | 2007-12-06 | 2012-03-06 | Los Angeles Biomedical Research Institute At Harbor-Ucla Medical Center | Method and system for detection of respiratory variation in plethysmographic oximetry |
US9757043B2 (en) | 2007-12-06 | 2017-09-12 | Los Angeles Biomedical Research Institute At Harbor-Ucla Medical Center | Method and system for detection of respiratory variation in plethysmographic oximetry |
US20090171173A1 (en) * | 2007-12-31 | 2009-07-02 | Nellcor Puritan Bennett Llc | System and method for reducing motion artifacts in a sensor |
US20090171226A1 (en) * | 2007-12-31 | 2009-07-02 | Nellcor Puritan Bennett Llc | System and method for evaluating variation in the timing of physiological events |
US8768423B2 (en) | 2008-03-04 | 2014-07-01 | Glt Acquisition Corp. | Multispot monitoring for use in optical coherence tomography |
WO2009114548A1 (en) | 2008-03-12 | 2009-09-17 | Corventis, Inc. | Heart failure decompensation prediction based on cardiac rhythm |
EP2103915B1 (en) * | 2008-03-17 | 2016-11-16 | Siemens Aktiengesellschaft | Apparatus and method for determining a resonant frequency of a wind turbine tower |
WO2009117102A2 (en) * | 2008-03-17 | 2009-09-24 | Npe Systems, Inc. | Background light detection system for a flow cytometer |
US9560994B2 (en) * | 2008-03-26 | 2017-02-07 | Covidien Lp | Pulse oximeter with adaptive power conservation |
US8412317B2 (en) | 2008-04-18 | 2013-04-02 | Corventis, Inc. | Method and apparatus to measure bioelectric impedance of patient tissue |
US20090275809A1 (en) * | 2008-05-01 | 2009-11-05 | Starr Life Sciences Corp. | Portable Modular Kiosk Based Physiologic Sensor System with Display and Data Storage for Clinical and Research Applications including Cross Calculating and Cross Checked Physiologic Parameters Based Upon Combined Sensor Input |
US8882684B2 (en) | 2008-05-12 | 2014-11-11 | Earlysense Ltd. | Monitoring, predicting and treating clinical episodes |
US20090275810A1 (en) * | 2008-05-01 | 2009-11-05 | Starr Life Sciences Corp. | Portable modular pc based system for continuous monitoring of blood oxygenation and respiratory parameters |
US9883809B2 (en) | 2008-05-01 | 2018-02-06 | Earlysense Ltd. | Monitoring, predicting and treating clinical episodes |
US20090275844A1 (en) | 2008-05-02 | 2009-11-05 | Masimo Corporation | Monitor configuration system |
EP2312995B1 (en) | 2008-05-05 | 2017-06-28 | Masimo Corporation | Pulse oximetry system with electrical decoupling circuitry |
US20090281435A1 (en) * | 2008-05-07 | 2009-11-12 | Motorola, Inc. | Method and apparatus for robust heart rate sensing |
US8677437B2 (en) * | 2008-05-07 | 2014-03-18 | Evertz Microsystems Ltd. | Systems and methods for calculating the delay between media signals |
US8754947B2 (en) * | 2008-05-07 | 2014-06-17 | Evertz Microsystems Ltd. | Systems and methods for comparing media signals |
US8780209B2 (en) * | 2008-05-07 | 2014-07-15 | Evertz Microsystems Ltd. | Systems and methods for comparing media signals |
JP2012502671A (ja) | 2008-05-12 | 2012-02-02 | アーリーセンス エルティディ | 臨床症状のモニタリング、予測及び治療 |
BRPI0912721B8 (pt) * | 2008-05-14 | 2021-06-22 | Espenusa Holding Llc | unidade de coleta de dados de atividade física com dois ou mais sensores infravermelhos, pelo menos um sensor de temperatura e pelo menos um acelerômetro |
WO2009153700A1 (en) * | 2008-06-16 | 2009-12-23 | Koninklijke Philips Electronics N.V. | Monitoring a vital parameter of a patient with "in-situ" modulation scheme to avoid interference |
US20090326386A1 (en) * | 2008-06-30 | 2009-12-31 | Nellcor Puritan Bennett Ireland | Systems and Methods for Non-Invasive Blood Pressure Monitoring |
US8398556B2 (en) * | 2008-06-30 | 2013-03-19 | Covidien Lp | Systems and methods for non-invasive continuous blood pressure determination |
US8660799B2 (en) | 2008-06-30 | 2014-02-25 | Nellcor Puritan Bennett Ireland | Processing and detecting baseline changes in signals |
US8862194B2 (en) * | 2008-06-30 | 2014-10-14 | Covidien Lp | Method for improved oxygen saturation estimation in the presence of noise |
USD621516S1 (en) | 2008-08-25 | 2010-08-10 | Masimo Laboratories, Inc. | Patient monitoring sensor |
US20100004518A1 (en) | 2008-07-03 | 2010-01-07 | Masimo Laboratories, Inc. | Heat sink for noninvasive medical sensor |
US8370080B2 (en) * | 2008-07-15 | 2013-02-05 | Nellcor Puritan Bennett Ireland | Methods and systems for determining whether to trigger an alarm |
US8506498B2 (en) | 2008-07-15 | 2013-08-13 | Nellcor Puritan Bennett Ireland | Systems and methods using induced perturbation to determine physiological parameters |
US20100016692A1 (en) * | 2008-07-15 | 2010-01-21 | Nellcor Puritan Bennett Ireland | Systems and methods for computing a physiological parameter using continuous wavelet transforms |
US8285352B2 (en) | 2008-07-15 | 2012-10-09 | Nellcor Puritan Bennett Llc | Systems and methods for identifying pulse rates |
US8761855B2 (en) | 2008-07-15 | 2014-06-24 | Nellcor Puritan Bennett Ireland | Systems and methods for determining oxygen saturation |
GB2462081A (en) * | 2008-07-21 | 2010-01-27 | Eigenlabs Ltd | A programmable sound creation interface |
US8203438B2 (en) | 2008-07-29 | 2012-06-19 | Masimo Corporation | Alarm suspend system |
JP5245618B2 (ja) * | 2008-07-30 | 2013-07-24 | 富士通株式会社 | 生体情報測定装置および生体情報測定方法 |
US8630691B2 (en) | 2008-08-04 | 2014-01-14 | Cercacor Laboratories, Inc. | Multi-stream sensor front ends for noninvasive measurement of blood constituents |
US8208986B2 (en) * | 2008-09-10 | 2012-06-26 | Duerk Jeffrey L | Steady state dark blood magnetic resonance imaging |
WO2010031070A2 (en) * | 2008-09-15 | 2010-03-18 | Masimo Corporation | Patient monitor including multi-parameter graphical display |
US20100076323A1 (en) * | 2008-09-19 | 2010-03-25 | Maneesh Shrivastav | Method and apparatus for determining a respiration parameter in a medical device |
US20100076319A1 (en) * | 2008-09-25 | 2010-03-25 | Nellcor Puritan Bennett Llc | Pathlength-Corrected Medical Spectroscopy |
US9314168B2 (en) * | 2008-09-30 | 2016-04-19 | Nellcor Puritan Bennett Ireland | Detecting sleep events using localized blood pressure changes |
US8532751B2 (en) * | 2008-09-30 | 2013-09-10 | Covidien Lp | Laser self-mixing sensors for biological sensing |
US20100081891A1 (en) * | 2008-09-30 | 2010-04-01 | Nellcor Puritan Bennett Llc | System And Method For Displaying Detailed Information For A Data Point |
US9301697B2 (en) * | 2008-09-30 | 2016-04-05 | Nellcor Puritan Bennett Ireland | Systems and methods for recalibrating a non-invasive blood pressure monitor |
US9687161B2 (en) * | 2008-09-30 | 2017-06-27 | Nellcor Puritan Bennett Ireland | Systems and methods for maintaining blood pressure monitor calibration |
US8410951B2 (en) | 2008-09-30 | 2013-04-02 | Covidien Lp | Detecting a signal quality decrease in a measurement system |
US9078609B2 (en) * | 2008-10-02 | 2015-07-14 | Nellcor Puritan Bennett Ireland | Extraction of physiological measurements from a photoplethysmograph (PPG) signal |
US8142473B2 (en) | 2008-10-03 | 2012-03-27 | Tyco Healthcare Group Lp | Method of transferring rotational motion in an articulating surgical instrument |
US8346330B2 (en) | 2008-10-13 | 2013-01-01 | Masimo Corporation | Reflection-detector sensor position indicator |
US8401602B2 (en) | 2008-10-13 | 2013-03-19 | Masimo Corporation | Secondary-emitter sensor position indicator |
US20100106030A1 (en) * | 2008-10-23 | 2010-04-29 | Mason Gregory R | Method and system for automated measurement of pulsus paradoxus |
US11857293B2 (en) | 2008-10-29 | 2024-01-02 | Flashback Technologies, Inc. | Rapid detection of bleeding before, during, and after fluid resuscitation |
US11478190B2 (en) | 2008-10-29 | 2022-10-25 | Flashback Technologies, Inc. | Noninvasive hydration monitoring |
US11382571B2 (en) | 2008-10-29 | 2022-07-12 | Flashback Technologies, Inc. | Noninvasive predictive and/or estimative blood pressure monitoring |
US11395594B2 (en) | 2008-10-29 | 2022-07-26 | Flashback Technologies, Inc. | Noninvasive monitoring for fluid resuscitation |
US8512260B2 (en) * | 2008-10-29 | 2013-08-20 | The Regents Of The University Of Colorado, A Body Corporate | Statistical, noninvasive measurement of intracranial pressure |
US20110172545A1 (en) * | 2008-10-29 | 2011-07-14 | Gregory Zlatko Grudic | Active Physical Perturbations to Enhance Intelligent Medical Monitoring |
US11395634B2 (en) | 2008-10-29 | 2022-07-26 | Flashback Technologies, Inc. | Estimating physiological states based on changes in CRI |
US11406269B2 (en) | 2008-10-29 | 2022-08-09 | Flashback Technologies, Inc. | Rapid detection of bleeding following injury |
WO2010053845A1 (en) * | 2008-11-05 | 2010-05-14 | Nellcor Puritan Bennett Llc | System and method for facilitating observation of monitored physiologic data |
US20100145171A1 (en) * | 2008-12-05 | 2010-06-10 | Electronics And Telecommunications Research Institute | Apparatus for measuring motion noise robust pulse wave and method thereof |
US8771204B2 (en) | 2008-12-30 | 2014-07-08 | Masimo Corporation | Acoustic sensor assembly |
US8114122B2 (en) | 2009-01-13 | 2012-02-14 | Tyco Healthcare Group Lp | Apparatus, system, and method for performing an electrosurgical procedure |
US8588880B2 (en) | 2009-02-16 | 2013-11-19 | Masimo Corporation | Ear sensor |
US10032002B2 (en) | 2009-03-04 | 2018-07-24 | Masimo Corporation | Medical monitoring system |
WO2010102069A2 (en) | 2009-03-04 | 2010-09-10 | Masimo Corporation | Medical monitoring system |
US10007758B2 (en) | 2009-03-04 | 2018-06-26 | Masimo Corporation | Medical monitoring system |
US9323894B2 (en) | 2011-08-19 | 2016-04-26 | Masimo Corporation | Health care sanitation monitoring system |
US8216136B2 (en) | 2009-03-05 | 2012-07-10 | Nellcor Puritan Bennett Llc | Systems and methods for monitoring heart rate and blood pressure correlation |
US20100224192A1 (en) * | 2009-03-06 | 2010-09-09 | Cardinal Health 207, Inc. | Automated Oxygen Delivery Method |
US20100224191A1 (en) * | 2009-03-06 | 2010-09-09 | Cardinal Health 207, Inc. | Automated Oxygen Delivery System |
US8388353B2 (en) | 2009-03-11 | 2013-03-05 | Cercacor Laboratories, Inc. | Magnetic connector |
US20100234718A1 (en) * | 2009-03-12 | 2010-09-16 | Anand Sampath | Open architecture medical communication system |
US8897847B2 (en) | 2009-03-23 | 2014-11-25 | Masimo Corporation | Digit gauge for noninvasive optical sensor |
CN101843489A (zh) * | 2009-03-26 | 2010-09-29 | 深圳市理邦精密仪器有限公司 | 一种呼吸信号处理方法 |
US8320985B2 (en) * | 2009-04-02 | 2012-11-27 | Empire Technology Development Llc | Touch screen interfaces with pulse oximetry |
US20100274102A1 (en) * | 2009-04-22 | 2010-10-28 | Streamline Automation, Llc | Processing Physiological Sensor Data Using a Physiological Model Combined with a Probabilistic Processor |
US8187273B2 (en) | 2009-05-07 | 2012-05-29 | Tyco Healthcare Group Lp | Apparatus, system, and method for performing an electrosurgical procedure |
US8786575B2 (en) * | 2009-05-18 | 2014-07-22 | Empire Technology Development LLP | Touch-sensitive device and method |
US8989831B2 (en) * | 2009-05-19 | 2015-03-24 | Masimo Corporation | Disposable components for reusable physiological sensor |
US8738118B2 (en) | 2009-05-20 | 2014-05-27 | Sotera Wireless, Inc. | Cable system for generating signals for detecting motion and measuring vital signs |
US8571619B2 (en) | 2009-05-20 | 2013-10-29 | Masimo Corporation | Hemoglobin display and patient treatment |
US11896350B2 (en) | 2009-05-20 | 2024-02-13 | Sotera Wireless, Inc. | Cable system for generating signals for detecting motion and measuring vital signs |
US8909330B2 (en) | 2009-05-20 | 2014-12-09 | Sotera Wireless, Inc. | Body-worn device and associated system for alarms/alerts based on vital signs and motion |
US20100298728A1 (en) * | 2009-05-20 | 2010-11-25 | Nellcor Puritan Bennett Ireland | Signal Processing Techniques For Determining Signal Quality Using A Wavelet Transform Ratio Surface |
US20120271554A1 (en) * | 2009-05-29 | 2012-10-25 | Yale University | Systems and Methods Utilizing Plethysmographic Data |
US8418524B2 (en) | 2009-06-12 | 2013-04-16 | Masimo Corporation | Non-invasive sensor calibration device |
US8437824B2 (en) | 2009-06-17 | 2013-05-07 | Sotera Wireless, Inc. | Body-worn pulse oximeter |
US20100324431A1 (en) * | 2009-06-18 | 2010-12-23 | Nellcor Puritan Bennett Ireland | Determining Disease State Using An Induced Load |
US8670811B2 (en) * | 2009-06-30 | 2014-03-11 | Masimo Corporation | Pulse oximetry system for adjusting medical ventilation |
US9198582B2 (en) * | 2009-06-30 | 2015-12-01 | Nellcor Puritan Bennett Ireland | Determining a characteristic physiological parameter |
US8246618B2 (en) | 2009-07-08 | 2012-08-21 | Tyco Healthcare Group Lp | Electrosurgical jaws with offset knife |
US20110208015A1 (en) * | 2009-07-20 | 2011-08-25 | Masimo Corporation | Wireless patient monitoring system |
US20110040197A1 (en) * | 2009-07-20 | 2011-02-17 | Masimo Corporation | Wireless patient monitoring system |
US8471713B2 (en) * | 2009-07-24 | 2013-06-25 | Cercacor Laboratories, Inc. | Interference detector for patient monitor |
US20110021929A1 (en) * | 2009-07-27 | 2011-01-27 | Nellcor Puritan Bennett Ireland | Systems and methods for continuous non-invasive blood pressure monitoring |
US20110028806A1 (en) * | 2009-07-29 | 2011-02-03 | Sean Merritt | Reflectance calibration of fluorescence-based glucose measurements |
US20110028809A1 (en) * | 2009-07-29 | 2011-02-03 | Masimo Corporation | Patient monitor ambient display device |
US8473020B2 (en) | 2009-07-29 | 2013-06-25 | Cercacor Laboratories, Inc. | Non-invasive physiological sensor cover |
US8628477B2 (en) | 2009-07-31 | 2014-01-14 | Nellcor Puritan Bennett Ireland | Systems and methods for non-invasive determination of blood pressure |
US20110087081A1 (en) * | 2009-08-03 | 2011-04-14 | Kiani Massi Joe E | Personalized physiological monitor |
US8688183B2 (en) | 2009-09-03 | 2014-04-01 | Ceracor Laboratories, Inc. | Emitter driver for noninvasive patient monitor |
US11253169B2 (en) | 2009-09-14 | 2022-02-22 | Sotera Wireless, Inc. | Body-worn monitor for measuring respiration rate |
US20110066043A1 (en) * | 2009-09-14 | 2011-03-17 | Matt Banet | System for measuring vital signs during hemodialysis |
US20110172498A1 (en) * | 2009-09-14 | 2011-07-14 | Olsen Gregory A | Spot check monitor credit system |
US10123722B2 (en) | 2009-09-14 | 2018-11-13 | Sotera Wireless, Inc. | Body-worn monitor for measuring respiration rate |
US10420476B2 (en) | 2009-09-15 | 2019-09-24 | Sotera Wireless, Inc. | Body-worn vital sign monitor |
US20110066044A1 (en) | 2009-09-15 | 2011-03-17 | Jim Moon | Body-worn vital sign monitor |
US8364250B2 (en) | 2009-09-15 | 2013-01-29 | Sotera Wireless, Inc. | Body-worn vital sign monitor |
US8321004B2 (en) | 2009-09-15 | 2012-11-27 | Sotera Wireless, Inc. | Body-worn vital sign monitor |
US10806351B2 (en) | 2009-09-15 | 2020-10-20 | Sotera Wireless, Inc. | Body-worn vital sign monitor |
US8527038B2 (en) | 2009-09-15 | 2013-09-03 | Sotera Wireless, Inc. | Body-worn vital sign monitor |
US9579039B2 (en) | 2011-01-10 | 2017-02-28 | Masimo Corporation | Non-invasive intravascular volume index monitor |
DE112010003689T5 (de) * | 2009-09-17 | 2013-01-17 | Marcelo Lamego | Verbesserte Analytüberwachung unter Verwendung eines oder mehrerer Beschleunigungsmesser |
US20110137297A1 (en) | 2009-09-17 | 2011-06-09 | Kiani Massi Joe E | Pharmacological management system |
US8133254B2 (en) | 2009-09-18 | 2012-03-13 | Tyco Healthcare Group Lp | In vivo attachable and detachable end effector assembly and laparoscopic surgical instrument and methods therefor |
US9220440B2 (en) * | 2009-09-21 | 2015-12-29 | Nellcor Puritan Bennett Ireland | Determining a characteristic respiration rate |
US8377054B2 (en) * | 2009-09-24 | 2013-02-19 | Covidien Lp | Automatic control circuit for use in an electrosurgical generator |
US8923945B2 (en) * | 2009-09-24 | 2014-12-30 | Covidien Lp | Determination of a physiological parameter |
US8840562B2 (en) * | 2009-09-24 | 2014-09-23 | Covidien Lp | Signal processing warping technique |
WO2011037699A2 (en) * | 2009-09-24 | 2011-03-31 | Nellcor Puritan Bennett Llc | Determination of a physiological parameter |
US8112871B2 (en) | 2009-09-28 | 2012-02-14 | Tyco Healthcare Group Lp | Method for manufacturing electrosurgical seal plates |
US8571618B1 (en) | 2009-09-28 | 2013-10-29 | Cercacor Laboratories, Inc. | Adaptive calibration system for spectrophotometric measurements |
US9066660B2 (en) * | 2009-09-29 | 2015-06-30 | Nellcor Puritan Bennett Ireland | Systems and methods for high-pass filtering a photoplethysmograph signal |
US8463347B2 (en) * | 2009-09-30 | 2013-06-11 | Nellcor Puritan Bennett Ireland | Systems and methods for normalizing a plethysmograph signal for improved feature analysis |
US20110082711A1 (en) | 2009-10-06 | 2011-04-07 | Masimo Laboratories, Inc. | Personal digital assistant or organizer for monitoring glucose levels |
US9106038B2 (en) | 2009-10-15 | 2015-08-11 | Masimo Corporation | Pulse oximetry system with low noise cable hub |
US9066680B1 (en) | 2009-10-15 | 2015-06-30 | Masimo Corporation | System for determining confidence in respiratory rate measurements |
US8821415B2 (en) * | 2009-10-15 | 2014-09-02 | Masimo Corporation | Physiological acoustic monitoring system |
US8790268B2 (en) | 2009-10-15 | 2014-07-29 | Masimo Corporation | Bidirectional physiological information display |
WO2011047216A2 (en) | 2009-10-15 | 2011-04-21 | Masimo Corporation | Physiological acoustic monitoring system |
US8755535B2 (en) | 2009-10-15 | 2014-06-17 | Masimo Corporation | Acoustic respiratory monitoring sensor having multiple sensing elements |
US8870792B2 (en) | 2009-10-15 | 2014-10-28 | Masimo Corporation | Physiological acoustic monitoring system |
US9848800B1 (en) | 2009-10-16 | 2017-12-26 | Masimo Corporation | Respiratory pause detector |
US8790259B2 (en) | 2009-10-22 | 2014-07-29 | Corventis, Inc. | Method and apparatus for remote detection and monitoring of functional chronotropic incompetence |
JP5476922B2 (ja) * | 2009-10-27 | 2014-04-23 | セイコーエプソン株式会社 | 拍動検出装置及び拍動検出方法 |
US9314181B2 (en) | 2009-11-03 | 2016-04-19 | Vivaquant Llc | Method and apparatus for detection of heartbeat characteristics |
WO2011063106A1 (en) * | 2009-11-18 | 2011-05-26 | Nellcor Puritan Bennett Llc | Intelligent user interface for medical monitors |
US9839381B1 (en) | 2009-11-24 | 2017-12-12 | Cercacor Laboratories, Inc. | Physiological measurement system with automatic wavelength adjustment |
WO2011069122A1 (en) | 2009-12-04 | 2011-06-09 | Masimo Corporation | Calibration for multi-stage physiological monitors |
US9451897B2 (en) | 2009-12-14 | 2016-09-27 | Medtronic Monitoring, Inc. | Body adherent patch with electronics for physiologic monitoring |
US20120088989A1 (en) * | 2009-12-21 | 2012-04-12 | Roche Diagnostic Operations, Inc. | Management Method And System For Implementation, Execution, Data Collection, and Data Analysis of A Structured Collection Procedure Which Runs On A Collection Device |
US9153112B1 (en) | 2009-12-21 | 2015-10-06 | Masimo Corporation | Modular patient monitor |
WO2011080185A1 (en) * | 2009-12-28 | 2011-07-07 | Gambro Lundia Ab | Apparatus and method for prediction of rapid symptomatic blood pressure decrease |
US20110190613A1 (en) * | 2010-01-11 | 2011-08-04 | O2 Medtech, Inc., | Hybrid spectrophotometric monitoring of biological constituents |
WO2011086644A1 (ja) * | 2010-01-18 | 2011-07-21 | コニカミノルタセンシング株式会社 | 生体情報測定装置および該方法 |
WO2011091059A1 (en) | 2010-01-19 | 2011-07-28 | Masimo Corporation | Wellness analysis system |
DE102010006956B4 (de) | 2010-02-02 | 2012-03-29 | Technische Universität Berlin | Verfahren und Messgerät zum Messen der Sauerstoffsättigung im Blut |
US9585709B2 (en) * | 2010-02-05 | 2017-03-07 | Covidien Lp | Square wave for vessel sealing |
US9724024B2 (en) * | 2010-03-01 | 2017-08-08 | Masimo Corporation | Adaptive alarm system |
US8584345B2 (en) | 2010-03-08 | 2013-11-19 | Masimo Corporation | Reprocessing of a physiological sensor |
US20110224498A1 (en) | 2010-03-10 | 2011-09-15 | Sotera Wireless, Inc. | Body-worn vital sign monitor |
KR101674580B1 (ko) * | 2010-03-26 | 2016-11-09 | 삼성전자주식회사 | 생체 신호를 측정하는 장치 및 방법 |
US9307928B1 (en) | 2010-03-30 | 2016-04-12 | Masimo Corporation | Plethysmographic respiration processor |
US9451887B2 (en) | 2010-03-31 | 2016-09-27 | Nellcor Puritan Bennett Ireland | Systems and methods for measuring electromechanical delay of the heart |
US8965498B2 (en) | 2010-04-05 | 2015-02-24 | Corventis, Inc. | Method and apparatus for personalized physiologic parameters |
US8888700B2 (en) | 2010-04-19 | 2014-11-18 | Sotera Wireless, Inc. | Body-worn monitor for measuring respiratory rate |
US8979765B2 (en) | 2010-04-19 | 2015-03-17 | Sotera Wireless, Inc. | Body-worn monitor for measuring respiratory rate |
US8747330B2 (en) | 2010-04-19 | 2014-06-10 | Sotera Wireless, Inc. | Body-worn monitor for measuring respiratory rate |
US9339209B2 (en) | 2010-04-19 | 2016-05-17 | Sotera Wireless, Inc. | Body-worn monitor for measuring respiratory rate |
US9173593B2 (en) | 2010-04-19 | 2015-11-03 | Sotera Wireless, Inc. | Body-worn monitor for measuring respiratory rate |
US9173594B2 (en) | 2010-04-19 | 2015-11-03 | Sotera Wireless, Inc. | Body-worn monitor for measuring respiratory rate |
US8898037B2 (en) | 2010-04-28 | 2014-11-25 | Nellcor Puritan Bennett Ireland | Systems and methods for signal monitoring using Lissajous figures |
US8712494B1 (en) | 2010-05-03 | 2014-04-29 | Masimo Corporation | Reflective non-invasive sensor |
US9138180B1 (en) | 2010-05-03 | 2015-09-22 | Masimo Corporation | Sensor adapter cable |
US7884933B1 (en) | 2010-05-05 | 2011-02-08 | Revolutionary Business Concepts, Inc. | Apparatus and method for determining analyte concentrations |
US8666468B1 (en) | 2010-05-06 | 2014-03-04 | Masimo Corporation | Patient monitor for determining microcirculation state |
US9326712B1 (en) | 2010-06-02 | 2016-05-03 | Masimo Corporation | Opticoustic sensor |
US9002440B2 (en) | 2010-07-08 | 2015-04-07 | Intelomed, Inc. | System and method for characterizing circulatory blood flow |
WO2012006520A1 (en) | 2010-07-08 | 2012-01-12 | Intelomed, Inc. | System and method for characterizing circulatory blood flow |
US8740792B1 (en) | 2010-07-12 | 2014-06-03 | Masimo Corporation | Patient monitor capable of accounting for environmental conditions |
US9649054B2 (en) | 2010-08-26 | 2017-05-16 | Cercacor Laboratories, Inc. | Blood pressure measurement method |
CN101940476B (zh) * | 2010-09-03 | 2016-02-03 | 深圳市索莱瑞医疗技术有限公司 | 一种血氧饱和度检测方法及系统 |
US9775545B2 (en) | 2010-09-28 | 2017-10-03 | Masimo Corporation | Magnetic electrical connector for patient monitors |
EP2621333B1 (en) | 2010-09-28 | 2015-07-29 | Masimo Corporation | Depth of consciousness monitor including oximeter |
US10216893B2 (en) | 2010-09-30 | 2019-02-26 | Fitbit, Inc. | Multimode sensor devices |
US9211095B1 (en) | 2010-10-13 | 2015-12-15 | Masimo Corporation | Physiological measurement logic engine |
US8723677B1 (en) | 2010-10-20 | 2014-05-13 | Masimo Corporation | Patient safety system with automatically adjusting bed |
US20120136226A1 (en) * | 2010-11-29 | 2012-05-31 | Nellcor Puritan Bennett Llc | Pulse Oximetry For Determining Heart Rate Variability As A Measure Of Susceptibility To Stress |
US8825428B2 (en) | 2010-11-30 | 2014-09-02 | Neilcor Puritan Bennett Ireland | Methods and systems for recalibrating a blood pressure monitor with memory |
US9357934B2 (en) | 2010-12-01 | 2016-06-07 | Nellcor Puritan Bennett Ireland | Systems and methods for physiological event marking |
US9259160B2 (en) | 2010-12-01 | 2016-02-16 | Nellcor Puritan Bennett Ireland | Systems and methods for determining when to measure a physiological parameter |
JP5604275B2 (ja) * | 2010-12-02 | 2014-10-08 | 富士通テン株式会社 | 相関低減方法、音声信号変換装置および音響再生装置 |
US10292625B2 (en) | 2010-12-07 | 2019-05-21 | Earlysense Ltd. | Monitoring a sleeping subject |
US10722130B2 (en) | 2010-12-28 | 2020-07-28 | Sotera Wireless, Inc. | Body-worn system for continuous, noninvasive measurement of cardiac output, stroke volume, cardiac power, and blood pressure |
US9414758B1 (en) * | 2011-01-12 | 2016-08-16 | Vivaquant Llc | Apparatus, system and methods for sensing and processing physiological signals |
US9113940B2 (en) | 2011-01-14 | 2015-08-25 | Covidien Lp | Trigger lockout and kickback mechanism for surgical instruments |
US8595164B2 (en) | 2011-01-27 | 2013-11-26 | Ming-Chui DONG | Wavelet modeling paradigms for cardiovascular physiological signal interpretation |
EP2673721A1 (en) | 2011-02-13 | 2013-12-18 | Masimo Corporation | Medical characterization system |
CN103582449B (zh) | 2011-02-18 | 2017-06-09 | 索泰拉无线公司 | 用于患者监护的模块化手腕佩戴式处理器 |
SG192835A1 (en) | 2011-02-18 | 2013-09-30 | Sotera Wireless Inc | Optical sensor for measuring physiological properties |
US9066666B2 (en) | 2011-02-25 | 2015-06-30 | Cercacor Laboratories, Inc. | Patient monitor for monitoring microcirculation |
KR101800706B1 (ko) * | 2011-03-08 | 2017-11-24 | 삼성전자 주식회사 | 잡음이 제거된 생체 신호를 측정하는 장치, 단위 측정기 및 방법 |
EP2502555A1 (en) | 2011-03-22 | 2012-09-26 | Bmeye B.V. | Non-invasive oxygen delivery measurement system and method |
US8830449B1 (en) | 2011-04-18 | 2014-09-09 | Cercacor Laboratories, Inc. | Blood analysis system |
US9095316B2 (en) | 2011-04-20 | 2015-08-04 | Masimo Corporation | System for generating alarms based on alarm patterns |
US9622692B2 (en) | 2011-05-16 | 2017-04-18 | Masimo Corporation | Personal health device |
US8909312B2 (en) * | 2011-05-17 | 2014-12-09 | Microsemi Corporation | Signal acquisition circuit for detecting a wanted signal in the presence of an unwanted signal |
KR102052123B1 (ko) * | 2011-06-10 | 2019-12-17 | 삼성전자주식회사 | 신호 간섭을 저감하고 및 분실 신호를 복원하는 초음파 진단 방법 및 장치 |
US9532722B2 (en) | 2011-06-21 | 2017-01-03 | Masimo Corporation | Patient monitoring system |
US9986919B2 (en) | 2011-06-21 | 2018-06-05 | Masimo Corporation | Patient monitoring system |
US9245668B1 (en) | 2011-06-29 | 2016-01-26 | Cercacor Laboratories, Inc. | Low noise cable providing communication between electronic sensor components and patient monitor |
US9059786B2 (en) * | 2011-07-07 | 2015-06-16 | Vecima Networks Inc. | Ingress suppression for communication systems |
US11439329B2 (en) | 2011-07-13 | 2022-09-13 | Masimo Corporation | Multiple measurement mode in a physiological sensor |
US9192351B1 (en) | 2011-07-22 | 2015-11-24 | Masimo Corporation | Acoustic respiratory monitoring sensor with probe-off detection |
EP2734103B1 (en) | 2011-07-22 | 2020-12-23 | Flashback Technologies, Inc. | Hemodynamic reserve monitor and hemodialysis control |
US8755872B1 (en) | 2011-07-28 | 2014-06-17 | Masimo Corporation | Patient monitoring system for indicating an abnormal condition |
US9782077B2 (en) | 2011-08-17 | 2017-10-10 | Masimo Corporation | Modulated physiological sensor |
JP5837785B2 (ja) * | 2011-09-13 | 2015-12-24 | 日本光電工業株式会社 | 生体信号測定装置 |
US9675274B2 (en) | 2011-09-23 | 2017-06-13 | Nellcor Puritan Bennett Ireland | Systems and methods for determining respiration information from a photoplethysmograph |
US8880576B2 (en) | 2011-09-23 | 2014-11-04 | Nellcor Puritan Bennett Ireland | Systems and methods for determining respiration information from a photoplethysmograph |
US9402554B2 (en) | 2011-09-23 | 2016-08-02 | Nellcor Puritan Bennett Ireland | Systems and methods for determining respiration information from a photoplethysmograph |
US9119597B2 (en) | 2011-09-23 | 2015-09-01 | Nellcor Puritan Bennett Ireland | Systems and methods for determining respiration information from a photoplethysmograph |
US9693709B2 (en) | 2011-09-23 | 2017-07-04 | Nellcot Puritan Bennett Ireland | Systems and methods for determining respiration information from a photoplethysmograph |
US9770210B2 (en) | 2011-09-23 | 2017-09-26 | Nellcor Puritan Bennett Ireland | Systems and methods for analyzing a physiological sensor signal |
EP3584799B1 (en) | 2011-10-13 | 2022-11-09 | Masimo Corporation | Medical monitoring hub |
US9808188B1 (en) | 2011-10-13 | 2017-11-07 | Masimo Corporation | Robust fractional saturation determination |
US9943269B2 (en) | 2011-10-13 | 2018-04-17 | Masimo Corporation | System for displaying medical monitoring data |
US9778079B1 (en) | 2011-10-27 | 2017-10-03 | Masimo Corporation | Physiological monitor gauge panel |
US9693736B2 (en) | 2011-11-30 | 2017-07-04 | Nellcor Puritan Bennett Ireland | Systems and methods for determining respiration information using historical distribution |
US8755871B2 (en) | 2011-11-30 | 2014-06-17 | Covidien Lp | Systems and methods for detecting arrhythmia from a physiological signal |
US9060695B2 (en) | 2011-11-30 | 2015-06-23 | Covidien Lp | Systems and methods for determining differential pulse transit time from the phase difference of two analog plethysmographs |
US9445759B1 (en) | 2011-12-22 | 2016-09-20 | Cercacor Laboratories, Inc. | Blood glucose calibration system |
US9247896B2 (en) | 2012-01-04 | 2016-02-02 | Nellcor Puritan Bennett Ireland | Systems and methods for determining respiration information using phase locked loop |
US9392945B2 (en) | 2012-01-04 | 2016-07-19 | Masimo Corporation | Automated CCHD screening and detection |
US11172890B2 (en) | 2012-01-04 | 2021-11-16 | Masimo Corporation | Automated condition screening and detection |
US12004881B2 (en) | 2012-01-04 | 2024-06-11 | Masimo Corporation | Automated condition screening and detection |
USD680220S1 (en) | 2012-01-12 | 2013-04-16 | Coviden IP | Slider handle for laparoscopic device |
US20130188452A1 (en) * | 2012-01-19 | 2013-07-25 | Andre ST-ONGE | Assessing stress strain and fluid pressure in strata surrounding a borehole based on borehole casing resonance |
US10149616B2 (en) | 2012-02-09 | 2018-12-11 | Masimo Corporation | Wireless patient monitoring device |
JP5901327B2 (ja) * | 2012-02-09 | 2016-04-06 | キヤノン株式会社 | 現像装置、プロセスカートリッジ、および画像形成装置 |
US9480435B2 (en) | 2012-02-09 | 2016-11-01 | Masimo Corporation | Configurable patient monitoring system |
US10307111B2 (en) | 2012-02-09 | 2019-06-04 | Masimo Corporation | Patient position detection system |
US8880155B2 (en) | 2012-02-24 | 2014-11-04 | Covidien Lp | Hypovolemia diagnosis technique |
US20130317368A1 (en) | 2012-03-12 | 2013-11-28 | Ivwatch, Llc | System for Mitigating the Effects of Tissue Blood Volume Changes to Aid in Diagnosing Infiltration or Extravasation in Animalia Tissue |
WO2013148605A1 (en) | 2012-03-25 | 2013-10-03 | Masimo Corporation | Physiological monitor touchscreen interface |
US9131881B2 (en) | 2012-04-17 | 2015-09-15 | Masimo Corporation | Hypersaturation index |
US9179876B2 (en) | 2012-04-30 | 2015-11-10 | Nellcor Puritan Bennett Ireland | Systems and methods for identifying portions of a physiological signal usable for determining physiological information |
US20130324812A1 (en) * | 2012-05-31 | 2013-12-05 | Atlantis Limited Partnership | Cardiac pulse coefficient of variation and breathing monitoring system and method for extracting information from the cardiac pulse |
US10542903B2 (en) | 2012-06-07 | 2020-01-28 | Masimo Corporation | Depth of consciousness monitor |
EP2861142A4 (en) * | 2012-06-18 | 2016-04-06 | Eso Technologies Inc | COMPOSITIONS AND METHODS FOR MEASURING OXYGEN SATURATION IN BLOOD FILLED STRUCTURES |
US8948832B2 (en) | 2012-06-22 | 2015-02-03 | Fitbit, Inc. | Wearable heart rate monitor |
JP2014011408A (ja) | 2012-07-02 | 2014-01-20 | Toshiba Corp | 半導体装置の製造方法および研磨装置 |
US9697928B2 (en) | 2012-08-01 | 2017-07-04 | Masimo Corporation | Automated assembly sensor cable |
US10827961B1 (en) | 2012-08-29 | 2020-11-10 | Masimo Corporation | Physiological measurement calibration |
KR101317824B1 (ko) * | 2012-09-06 | 2013-10-15 | 이동화 | 생체 신호 처리 방법 |
US8868148B2 (en) | 2012-09-11 | 2014-10-21 | Covidien Lp | Methods and systems for qualifying physiological values based on segments of a physiological signal |
US9186108B2 (en) | 2012-09-11 | 2015-11-17 | Covidien Lp | Methods and systems for determining an algorithm setting based on a skew metric |
US9155478B2 (en) | 2012-09-11 | 2015-10-13 | Covidien Lp | Methods and systems for determining an algorithm setting based on a difference signal |
US9357936B2 (en) | 2012-09-11 | 2016-06-07 | Covidien Lp | Methods and systems for determining physiological information based on a correlation matrix |
US9186109B2 (en) | 2012-09-11 | 2015-11-17 | Covidien Lp | Methods and systems for qualifying physiological values based on metrics |
US9226670B2 (en) | 2012-09-11 | 2016-01-05 | Covidien Lp | Methods and systems for determining physiological information based on statistical regression analysis |
US9392974B2 (en) | 2012-09-11 | 2016-07-19 | Covidien Lp | Methods and systems for qualifying physiological values based on segments from a cross-correlation sequence |
US9186110B2 (en) | 2012-09-11 | 2015-11-17 | Covidien Lp | Methods and systems for qualifying calculated values based on a statistical metric |
US9314209B2 (en) | 2012-09-11 | 2016-04-19 | Covidien Lp | Methods and systems for determining physiological information based on a correlation sequence |
US9247887B2 (en) | 2012-09-11 | 2016-02-02 | Covidien Lp | Methods and systems for determining physiological information based on low and high frequency components |
US9339235B2 (en) | 2012-09-11 | 2016-05-17 | Covidien Lp | Methods and systems for determining signal-to-noise information from a physiological signal |
US9161723B2 (en) | 2012-09-11 | 2015-10-20 | Covidien Lp | Methods and systems for qualifying calculated values based on multiple difference signals |
US9149196B2 (en) | 2012-09-11 | 2015-10-06 | Covidien Lp | Methods and systems for determining an algorithm setting based on a difference signal |
US9392976B2 (en) | 2012-09-11 | 2016-07-19 | Covidien Lp | Methods and systems for determining physiological information based on a combined autocorrelation sequence |
US9259186B2 (en) | 2012-09-11 | 2016-02-16 | Covidien Lp | Methods and systems for determining noise information from a physiological signal |
US9186076B2 (en) | 2012-09-11 | 2015-11-17 | Covidien Lp | Methods and systems for qualifying a correlation lag value based on skewness |
US9241670B2 (en) | 2012-09-11 | 2016-01-26 | Covidien Lp | Methods and systems for conditioning physiological information using a normalization technique |
US9220423B2 (en) | 2012-09-11 | 2015-12-29 | Covidien Lp | Methods and systems for qualifying a calculated value based on differently sized sorted difference signals |
US9192310B2 (en) | 2012-09-11 | 2015-11-24 | Covidien Lp | Methods and systems for qualifying a calculated value based on baseline and deviation information |
US9149232B2 (en) | 2012-09-11 | 2015-10-06 | Covidien Lp | Methods and systems for qualifying calculated values based on state transitions |
US9119598B2 (en) | 2012-09-11 | 2015-09-01 | Covidien Lp | Methods and systems for determining physiological information using reference waveforms |
US9186101B2 (en) | 2012-09-11 | 2015-11-17 | Covidien Lp | Methods and systems for qualifying a correlation lag value based on a correlation value at a different lag |
US9749232B2 (en) | 2012-09-20 | 2017-08-29 | Masimo Corporation | Intelligent medical network edge router |
US9955937B2 (en) | 2012-09-20 | 2018-05-01 | Masimo Corporation | Acoustic patient sensor coupler |
US10244949B2 (en) | 2012-10-07 | 2019-04-02 | Rhythm Diagnostic Systems, Inc. | Health monitoring systems and methods |
US10610159B2 (en) | 2012-10-07 | 2020-04-07 | Rhythm Diagnostic Systems, Inc. | Health monitoring systems and methods |
USD850626S1 (en) | 2013-03-15 | 2019-06-04 | Rhythm Diagnostic Systems, Inc. | Health monitoring apparatuses |
US10413251B2 (en) | 2012-10-07 | 2019-09-17 | Rhythm Diagnostic Systems, Inc. | Wearable cardiac monitor |
US9717458B2 (en) | 2012-10-20 | 2017-08-01 | Masimo Corporation | Magnetic-flap optical sensor |
US10371732B2 (en) * | 2012-10-26 | 2019-08-06 | Keysight Technologies, Inc. | Method and system for performing real-time spectral analysis of non-stationary signal |
US9560996B2 (en) | 2012-10-30 | 2017-02-07 | Masimo Corporation | Universal medical system |
US9787568B2 (en) | 2012-11-05 | 2017-10-10 | Cercacor Laboratories, Inc. | Physiological test credit method |
US9750461B1 (en) | 2013-01-02 | 2017-09-05 | Masimo Corporation | Acoustic respiratory monitoring sensor with probe-off detection |
US9039614B2 (en) | 2013-01-15 | 2015-05-26 | Fitbit, Inc. | Methods, systems and devices for measuring fingertip heart rate |
US9724025B1 (en) | 2013-01-16 | 2017-08-08 | Masimo Corporation | Active-pulse blood analysis system |
TWI465701B (zh) * | 2013-01-28 | 2014-12-21 | Lextar Electronics Corp | 偵測環境光源閃爍頻率的方法與系統 |
US9560978B2 (en) | 2013-02-05 | 2017-02-07 | Covidien Lp | Systems and methods for determining respiration information from a physiological signal using amplitude demodulation |
US9554712B2 (en) | 2013-02-27 | 2017-01-31 | Covidien Lp | Systems and methods for generating an artificial photoplethysmograph signal |
US9687159B2 (en) | 2013-02-27 | 2017-06-27 | Covidien Lp | Systems and methods for determining physiological information by identifying fiducial points in a physiological signal |
US9750442B2 (en) | 2013-03-09 | 2017-09-05 | Masimo Corporation | Physiological status monitor |
US10441181B1 (en) | 2013-03-13 | 2019-10-15 | Masimo Corporation | Acoustic pulse and respiration monitoring system |
US9965946B2 (en) | 2013-03-13 | 2018-05-08 | Masimo Corporation | Systems and methods for monitoring a patient health network |
WO2014158820A1 (en) | 2013-03-14 | 2014-10-02 | Cercacor Laboratories, Inc. | Patient monitor as a minimally invasive glucometer |
US9986952B2 (en) | 2013-03-14 | 2018-06-05 | Masimo Corporation | Heart sound simulator |
US9936917B2 (en) | 2013-03-14 | 2018-04-10 | Masimo Laboratories, Inc. | Patient monitor placement indicator |
US10456038B2 (en) | 2013-03-15 | 2019-10-29 | Cercacor Laboratories, Inc. | Cloud-based physiological monitoring system |
US20160066863A1 (en) * | 2013-04-05 | 2016-03-10 | Nitto Denko Corporation | METHOD AND APPARATUS FOR DETERMINING SpO2 OF A SUBJECT FROM AN OPTICAL MEASUREMENT |
AU2014274953A1 (en) | 2013-06-04 | 2016-01-21 | Intelomed, Inc | Hemodynamic risk severity based upon detection and quantification of cardiac dysrhythmia behavior using a pulse volume waveform |
WO2014201183A1 (en) | 2013-06-11 | 2014-12-18 | Intelomed, Inc | Predicting hypovolemic hypotensive conditions using a pulse volume waveform |
US10512407B2 (en) * | 2013-06-24 | 2019-12-24 | Fitbit, Inc. | Heart rate data collection |
EP3536231B1 (en) * | 2013-07-01 | 2024-03-20 | Mayo Foundation for Medical Education and Research | Sensor types and sensor positioning for a remote patient monitoring system |
US9891079B2 (en) | 2013-07-17 | 2018-02-13 | Masimo Corporation | Pulser with double-bearing position encoder for non-invasive physiological monitoring |
WO2015020911A2 (en) | 2013-08-05 | 2015-02-12 | Cercacor Laboratories, Inc. | Blood pressure monitor with valve-chamber assembly |
WO2015023692A1 (en) | 2013-08-12 | 2015-02-19 | Intelomed, Inc. | Methods for monitoring and analyzing cardiovascular states |
WO2015038683A2 (en) | 2013-09-12 | 2015-03-19 | Cercacor Laboratories, Inc. | Medical device management system |
US11147518B1 (en) | 2013-10-07 | 2021-10-19 | Masimo Corporation | Regional oximetry signal processor |
US20150099950A1 (en) | 2013-10-07 | 2015-04-09 | Masimo Corporation | Regional oximetry sensor |
US10828007B1 (en) | 2013-10-11 | 2020-11-10 | Masimo Corporation | Acoustic sensor with attachment portion |
US10832818B2 (en) | 2013-10-11 | 2020-11-10 | Masimo Corporation | Alarm notification system |
US10022068B2 (en) | 2013-10-28 | 2018-07-17 | Covidien Lp | Systems and methods for detecting held breath events |
US10279247B2 (en) | 2013-12-13 | 2019-05-07 | Masimo Corporation | Avatar-incentive healthcare therapy |
US9848820B2 (en) | 2014-01-07 | 2017-12-26 | Covidien Lp | Apnea analysis system and method |
US9918666B2 (en) | 2014-01-13 | 2018-03-20 | The Board Of Regents, The University Of Texas System | Systems and methods for physiological signal enhancement and biometric extraction using non-invasive optical sensors |
US10206612B2 (en) | 2014-01-13 | 2019-02-19 | The Board Of Regents, The University Of Texas System | Methods and systems for extracting venous pulsation and respiratory information from photoplethysmographs |
US10086138B1 (en) | 2014-01-28 | 2018-10-02 | Masimo Corporation | Autonomous drug delivery system |
US11259745B2 (en) | 2014-01-28 | 2022-03-01 | Masimo Corporation | Autonomous drug delivery system |
EP3107449A1 (en) | 2014-02-20 | 2016-12-28 | Covidien LP | Systems and methods for filtering autocorrelation peaks and detecting harmonics |
US10532174B2 (en) | 2014-02-21 | 2020-01-14 | Masimo Corporation | Assistive capnography device |
EP3348186A1 (en) | 2014-03-20 | 2018-07-18 | Physical Enterprises, Inc. (dba Mio Global) | Activity score determination for health risk indicator determination |
WO2015150200A1 (en) | 2014-04-03 | 2015-10-08 | Koninklijke Philips N.V. | Monitoring device and method for compensating non-linearity effects in vital signs monitoring |
US10610160B1 (en) * | 2014-04-17 | 2020-04-07 | Cerner Innovation, Inc. | Stream-based alarm filtering |
JP6629242B2 (ja) | 2014-05-28 | 2020-01-15 | コーニンクレッカ フィリップス エヌ ヴェKoninklijke Philips N.V. | マルチチャネルppg信号を使用するモーションアーチファクト低減 |
US9924897B1 (en) | 2014-06-12 | 2018-03-27 | Masimo Corporation | Heated reprocessing of physiological sensors |
US10231670B2 (en) | 2014-06-19 | 2019-03-19 | Masimo Corporation | Proximity sensor in pulse oximeter |
KR20160019294A (ko) * | 2014-08-11 | 2016-02-19 | 삼성전자주식회사 | 신호 처리 방법 및 장치 |
WO2016036985A1 (en) | 2014-09-04 | 2016-03-10 | Masimo Corportion | Total hemoglobin index system |
US10383520B2 (en) | 2014-09-18 | 2019-08-20 | Masimo Semiconductor, Inc. | Enhanced visible near-infrared photodiode and non-invasive physiological sensor |
WO2016057553A1 (en) | 2014-10-07 | 2016-04-14 | Masimo Corporation | Modular physiological sensors |
US11553844B2 (en) * | 2014-10-14 | 2023-01-17 | East Carolina University | Methods, systems and computer program products for calculating MetaKG signals for regions having multiple sets of optical characteristics |
WO2016061041A1 (en) * | 2014-10-14 | 2016-04-21 | East Carolina University | Methods, systems and computer program products for determining hemodynamic status parameters using signals derived from multispectral blood flow and perfusion imaging |
CN107405094A (zh) | 2014-10-14 | 2017-11-28 | 东卡罗莱娜大学 | 用于使用成像技术来可视化解剖结构以及血流和灌注生理机能的方法、系统和计算机程序产品 |
US10463315B2 (en) | 2014-12-01 | 2019-11-05 | Covidien Lp | Adaptive alarm for physiological monitoring |
US10413476B2 (en) | 2015-01-20 | 2019-09-17 | Covidien Lp | System and method for cardiopulmonary resuscitation |
CN107405108B (zh) | 2015-01-23 | 2020-10-23 | 迈心诺瑞典公司 | 鼻腔/口腔插管系统及制造 |
EP3254339A1 (en) | 2015-02-06 | 2017-12-13 | Masimo Corporation | Connector assembly with pogo pins for use with medical sensors |
USD755392S1 (en) | 2015-02-06 | 2016-05-03 | Masimo Corporation | Pulse oximetry sensor |
JP6817213B2 (ja) | 2015-02-06 | 2021-01-20 | マシモ・コーポレイション | フレックス回路生理学的センサを効率的に製造する方法 |
US10568553B2 (en) | 2015-02-06 | 2020-02-25 | Masimo Corporation | Soft boot pulse oximetry sensor |
US10390718B2 (en) | 2015-03-20 | 2019-08-27 | East Carolina University | Multi-spectral physiologic visualization (MSPV) using laser imaging methods and systems for blood flow and perfusion imaging and quantification in an endoscopic design |
JP2016192985A (ja) * | 2015-03-31 | 2016-11-17 | 富士フイルム株式会社 | 内視鏡システム、プロセッサ装置、及び、内視鏡システムの作動方法 |
US10524738B2 (en) | 2015-05-04 | 2020-01-07 | Cercacor Laboratories, Inc. | Noninvasive sensor system with visual infographic display |
US11653862B2 (en) | 2015-05-22 | 2023-05-23 | Cercacor Laboratories, Inc. | Non-invasive optical physiological differential pathlength sensor |
US10448871B2 (en) | 2015-07-02 | 2019-10-22 | Masimo Corporation | Advanced pulse oximetry sensor |
KR102677391B1 (ko) | 2015-08-11 | 2024-06-24 | 마시모 코오퍼레이션 | 신체 조직에 의해 약화된 광에 반응하는 징후를 포함하는 의료 모니터링 분석 및 리플레이 |
KR102612874B1 (ko) | 2015-08-31 | 2023-12-12 | 마시모 코오퍼레이션 | 무선 환자 모니터링 시스템들 및 방법들 |
US11504066B1 (en) | 2015-09-04 | 2022-11-22 | Cercacor Laboratories, Inc. | Low-noise sensor system |
US10426695B2 (en) | 2015-09-08 | 2019-10-01 | Covidien Lp | System and method for cardiopulmonary resuscitation |
KR102188747B1 (ko) * | 2015-10-12 | 2020-12-08 | 에스케이텔레콤 주식회사 | 하이브리드 빔포밍을 이용한 무선 통신 방법 및 장치 |
DE102015220092B3 (de) * | 2015-10-15 | 2016-10-27 | Bruker Biospin Mri Gmbh | Verfahren zur Bestimmung einer räumlichen Zuordnung oder räumlichen Verteilung von Magnetpartikeln |
US10646144B2 (en) | 2015-12-07 | 2020-05-12 | Marcelo Malini Lamego | Wireless, disposable, extended use pulse oximeter apparatus and methods |
US11206989B2 (en) | 2015-12-10 | 2021-12-28 | Fitbit, Inc. | Light field management in an optical biological parameter sensor |
US10568525B1 (en) | 2015-12-14 | 2020-02-25 | Fitbit, Inc. | Multi-wavelength pulse oximetry |
US11679579B2 (en) | 2015-12-17 | 2023-06-20 | Masimo Corporation | Varnish-coated release liner |
US10980423B2 (en) | 2015-12-22 | 2021-04-20 | University Of Washington | Devices and methods for predicting hemoglobin levels using electronic devices such as mobile phones |
KR102463076B1 (ko) | 2015-12-24 | 2022-11-03 | 삼성전자주식회사 | 산소 포화도 측정장치 및 그의 산소 포화도 측정방법 |
US9814388B2 (en) | 2016-02-11 | 2017-11-14 | General Electric Company | Wireless patient monitoring system and method |
US9883800B2 (en) | 2016-02-11 | 2018-02-06 | General Electric Company | Wireless patient monitoring system and method |
US10537253B2 (en) * | 2016-02-25 | 2020-01-21 | Samsung Electronics Company, Ltd. | Detecting live tissues using signal analysis |
US10537285B2 (en) | 2016-03-04 | 2020-01-21 | Masimo Corporation | Nose sensor |
US10993662B2 (en) | 2016-03-04 | 2021-05-04 | Masimo Corporation | Nose sensor |
JP6561000B2 (ja) * | 2016-03-09 | 2019-08-14 | 富士フイルム株式会社 | 内視鏡システム及びその作動方法 |
US10098558B2 (en) | 2016-04-25 | 2018-10-16 | General Electric Company | Wireless patient monitoring system and method |
CN109195510A (zh) | 2016-04-29 | 2019-01-11 | 飞比特公司 | 多信道光电容积脉搏波传感器 |
US11191484B2 (en) | 2016-04-29 | 2021-12-07 | Masimo Corporation | Optical sensor tape |
US9928408B2 (en) * | 2016-06-17 | 2018-03-27 | International Business Machines Corporation | Signal processing |
US10617302B2 (en) | 2016-07-07 | 2020-04-14 | Masimo Corporation | Wearable pulse oximeter and respiration monitor |
US11076777B2 (en) | 2016-10-13 | 2021-08-03 | Masimo Corporation | Systems and methods for monitoring orientation to reduce pressure ulcer formation |
US10623857B2 (en) * | 2016-11-23 | 2020-04-14 | Harman Becker Automotive Systems Gmbh | Individual delay compensation for personal sound zones |
US11504058B1 (en) | 2016-12-02 | 2022-11-22 | Masimo Corporation | Multi-site noninvasive measurement of a physiological parameter |
TWI610179B (zh) * | 2016-12-07 | 2018-01-01 | 慧榮科技股份有限公司 | 主機裝置與資料傳輸速率控制方法 |
CN108170370B (zh) | 2016-12-07 | 2021-01-26 | 慧荣科技股份有限公司 | 数据储存装置与数据传输速率控制方法 |
WO2018119239A1 (en) | 2016-12-22 | 2018-06-28 | Cercacor Laboratories, Inc | Methods and devices for detecting intensity of light with translucent detector |
US10721785B2 (en) | 2017-01-18 | 2020-07-21 | Masimo Corporation | Patient-worn wireless physiological sensor with pairing functionality |
US10327713B2 (en) | 2017-02-24 | 2019-06-25 | Masimo Corporation | Modular multi-parameter patient monitoring device |
US10388120B2 (en) | 2017-02-24 | 2019-08-20 | Masimo Corporation | Localized projection of audible noises in medical settings |
WO2018156648A1 (en) | 2017-02-24 | 2018-08-30 | Masimo Corporation | Managing dynamic licenses for physiological parameters in a patient monitoring environment |
US11086609B2 (en) | 2017-02-24 | 2021-08-10 | Masimo Corporation | Medical monitoring hub |
US11024064B2 (en) | 2017-02-24 | 2021-06-01 | Masimo Corporation | Augmented reality system for displaying patient data |
WO2018156804A1 (en) | 2017-02-24 | 2018-08-30 | Masimo Corporation | System for displaying medical monitoring data |
US11185262B2 (en) | 2017-03-10 | 2021-11-30 | Masimo Corporation | Pneumonia screener |
JP6747344B2 (ja) * | 2017-03-14 | 2020-08-26 | オムロンヘルスケア株式会社 | 血圧データ処理装置、血圧データ処理方法および血圧データ処理プログラム |
US11051706B1 (en) | 2017-04-07 | 2021-07-06 | Fitbit, Inc. | Multiple source-detector pair photoplethysmography (PPG) sensor |
WO2018194992A1 (en) | 2017-04-18 | 2018-10-25 | Masimo Corporation | Nose sensor |
US10918281B2 (en) | 2017-04-26 | 2021-02-16 | Masimo Corporation | Medical monitoring device having multiple configurations |
EP4368104A2 (en) | 2017-04-28 | 2024-05-15 | Masimo Corporation | Spot check measurement system |
USD835284S1 (en) | 2017-04-28 | 2018-12-04 | Masimo Corporation | Medical monitoring device |
USD835285S1 (en) | 2017-04-28 | 2018-12-04 | Masimo Corporation | Medical monitoring device |
USD835282S1 (en) | 2017-04-28 | 2018-12-04 | Masimo Corporation | Medical monitoring device |
USD835283S1 (en) | 2017-04-28 | 2018-12-04 | Masimo Corporation | Medical monitoring device |
CN108784650A (zh) * | 2017-05-03 | 2018-11-13 | 深圳迈瑞生物医疗电子股份有限公司 | 生理信号的同源性识别方法及装置 |
US10932705B2 (en) | 2017-05-08 | 2021-03-02 | Masimo Corporation | System for displaying and controlling medical monitoring data |
US11103145B1 (en) | 2017-06-14 | 2021-08-31 | Vivaquant Llc | Physiological signal monitoring and apparatus therefor |
WO2019014629A1 (en) | 2017-07-13 | 2019-01-17 | Cercacor Laboratories, Inc. | MEDICAL MONITORING DEVICE FOR HARMONIZING PHYSIOLOGICAL MEASUREMENTS |
USD906970S1 (en) | 2017-08-15 | 2021-01-05 | Masimo Corporation | Connector |
USD890708S1 (en) | 2017-08-15 | 2020-07-21 | Masimo Corporation | Connector |
US10637181B2 (en) | 2017-08-15 | 2020-04-28 | Masimo Corporation | Water resistant connector for noninvasive patient monitor |
US10806933B2 (en) | 2017-09-06 | 2020-10-20 | General Electric Company | Patient monitoring systems and methods that detect interference with pacemaker |
JP6965066B2 (ja) * | 2017-09-12 | 2021-11-10 | オムロン株式会社 | 脈波測定装置、血圧測定装置、機器、脈波測定方法、および血圧測定方法 |
FR3071714B1 (fr) * | 2017-10-02 | 2022-03-25 | Centre Nat Rech Scient | Dispositif biotelemetrique ingestible et implantable in vivo |
CN111212599B (zh) | 2017-10-19 | 2024-07-02 | 迈心诺公司 | 医疗监测系统的显示布置 |
USD925597S1 (en) | 2017-10-31 | 2021-07-20 | Masimo Corporation | Display screen or portion thereof with graphical user interface |
CN111372517B (zh) | 2017-10-31 | 2023-02-17 | 梅西莫股份有限公司 | 用于显示氧气状态指示的系统 |
US11766198B2 (en) | 2018-02-02 | 2023-09-26 | Cercacor Laboratories, Inc. | Limb-worn patient monitoring device |
US10659963B1 (en) | 2018-02-12 | 2020-05-19 | True Wearables, Inc. | Single use medical device apparatus and methods |
GB2571100A (en) * | 2018-02-15 | 2019-08-21 | Airbus Operations Ltd | Controller for an aircraft braking system |
WO2019204368A1 (en) | 2018-04-19 | 2019-10-24 | Masimo Corporation | Mobile patient alarm display |
US11883129B2 (en) | 2018-04-24 | 2024-01-30 | Cercacor Laboratories, Inc. | Easy insert finger sensor for transmission based spectroscopy sensor |
US11627919B2 (en) | 2018-06-06 | 2023-04-18 | Masimo Corporation | Opioid overdose monitoring |
US10779098B2 (en) | 2018-07-10 | 2020-09-15 | Masimo Corporation | Patient monitor alarm speaker analyzer |
US11872156B2 (en) | 2018-08-22 | 2024-01-16 | Masimo Corporation | Core body temperature measurement |
US11389093B2 (en) | 2018-10-11 | 2022-07-19 | Masimo Corporation | Low noise oximetry cable |
JP7128960B2 (ja) | 2018-10-11 | 2022-08-31 | マシモ・コーポレイション | 鉛直方向戻り止めを備えた患者コネクタ組立体 |
US11464410B2 (en) | 2018-10-12 | 2022-10-11 | Masimo Corporation | Medical systems and methods |
BR112021006910A2 (pt) | 2018-10-12 | 2021-07-20 | Masimo Corporation | sistema e método para emparelhamento de um conjunto de sensores não invasivos, sistema e método para coleta de dados fisiológicos, método de coleta e exibição de dados fisiológicos, circuito flexível para um conjunto de sensores descartáveis, sistema de emparelhamento para estabelecer uma comunicação sem fio, aparelho para armazenamento de um conjunto de transmissores sem fio reutilizáveis, método para acoplar um conjunto de transmissores sem fio, sistema para coletar parâmetros fisiológicos do paciente, método e circuito flexível para transmitir dados fisiológicos |
US12004869B2 (en) | 2018-11-05 | 2024-06-11 | Masimo Corporation | System to monitor and manage patient hydration via plethysmograph variablity index in response to the passive leg raising |
US11986289B2 (en) | 2018-11-27 | 2024-05-21 | Willow Laboratories, Inc. | Assembly for medical monitoring device with multiple physiological sensors |
US11918386B2 (en) | 2018-12-26 | 2024-03-05 | Flashback Technologies, Inc. | Device-based maneuver and activity state-based physiologic status monitoring |
US11931142B1 (en) | 2019-03-19 | 2024-03-19 | VIVAQUANT, Inc | Apneic/hypopneic assessment via physiological signals |
EP4021293A4 (en) | 2019-08-28 | 2023-08-09 | Rds | VITAL SIGNS OR HEALTH MONITORING SYSTEMS AND PROCEDURES |
CN110720929B (zh) * | 2019-09-23 | 2022-04-05 | 浙江工业大学 | 基于二值传感器有界递归优化融合的血液氧气含量估计方法 |
CA3167295A1 (en) | 2020-01-13 | 2021-07-22 | Masimo Corporation | Wearable device with physiological parameters monitoring |
EP4199778A1 (en) | 2020-08-19 | 2023-06-28 | Masimo Corporation | Strap for a wearable device |
KR20240032835A (ko) | 2021-07-13 | 2024-03-12 | 마시모 코오퍼레이션 | 생리학적 지표들을 감시하는 웨어러블 기기 |
EP4373386A1 (en) | 2021-07-21 | 2024-05-29 | Masimo Corporation | Wearable band for health monitoring device |
USD1036293S1 (en) | 2021-08-17 | 2024-07-23 | Masimo Corporation | Straps for a wearable device |
WO2024081827A1 (en) * | 2022-10-14 | 2024-04-18 | Normal Computing Corporation | Thermodynamic computing system for sampling high-dimensional probability distributions |
Citations (38)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US3638640A (en) * | 1967-11-01 | 1972-02-01 | Robert F Shaw | Oximeter and method for in vivo determination of oxygen saturation in blood using three or more different wavelengths |
US4038536A (en) * | 1976-03-29 | 1977-07-26 | Rockwell International Corporation | Adaptive recursive least mean square error filter |
US4157708A (en) * | 1975-10-29 | 1979-06-12 | Minolta Camera Kabushiki Kaisha | Eye fundus plethysmograph assembly |
US4458691A (en) * | 1982-02-11 | 1984-07-10 | Arrhythmia Research Technology, Inc. | System and method for predicting ventricular tachycardia by adaptive high pass filter |
US4537200A (en) * | 1983-07-07 | 1985-08-27 | The Board Of Trustees Of The Leland Stanford Junior University | ECG enhancement by adaptive cancellation of electrosurgical interference |
US4582068A (en) * | 1981-12-21 | 1986-04-15 | American Home Products Corporation | Systems and methods for processing physiological signals |
US4653498A (en) * | 1982-09-13 | 1987-03-31 | Nellcor Incorporated | Pulse oximeter monitor |
US4667680A (en) * | 1983-11-14 | 1987-05-26 | Hewlett-Packard Company | Apparatus and method for reduction in respiration artifact in pulmonary artery pressure measurement |
US4714341A (en) * | 1984-02-23 | 1987-12-22 | Minolta Camera Kabushiki Kaisha | Multi-wavelength oximeter having a means for disregarding a poor signal |
US4751931A (en) * | 1986-09-22 | 1988-06-21 | Allegheny-Singer Research Institute | Method and apparatus for determining his-purkinje activity |
US4793361A (en) * | 1987-03-13 | 1988-12-27 | Cardiac Pacemakers, Inc. | Dual channel P-wave detection in surface electrocardiographs |
US4799493A (en) * | 1987-03-13 | 1989-01-24 | Cardiac Pacemakers, Inc. | Dual channel coherent fibrillation detection system |
US4799486A (en) * | 1987-03-13 | 1989-01-24 | Cardiac Pacemakers, Inc. | Refractoriless atrial sensing in dual chamber pacemakers |
US4800885A (en) * | 1987-12-02 | 1989-01-31 | The Boc Group, Inc. | Blood constituent monitoring apparatus and methods with frequency division multiplexing |
US4802486A (en) * | 1985-04-01 | 1989-02-07 | Nellcor Incorporated | Method and apparatus for detecting optical pulses |
US4863265A (en) * | 1987-10-16 | 1989-09-05 | Mine Safety Appliances Company | Apparatus and method for measuring blood constituents |
US4867571A (en) * | 1986-09-26 | 1989-09-19 | Sensormedics Corporation | Wave form filter pulse detector and method for modulated signal |
US4869254A (en) * | 1988-03-30 | 1989-09-26 | Nellcor Incorporated | Method and apparatus for calculating arterial oxygen saturation |
US4883353A (en) * | 1988-02-11 | 1989-11-28 | Puritan-Bennett Corporation | Pulse oximeter |
US4911167A (en) * | 1985-06-07 | 1990-03-27 | Nellcor Incorporated | Method and apparatus for detecting optical pulses |
US4928692A (en) * | 1985-04-01 | 1990-05-29 | Goodman David E | Method and apparatus for detecting optical pulses |
US4934372A (en) * | 1985-04-01 | 1990-06-19 | Nellcor Incorporated | Method and apparatus for detecting optical pulses |
US4949710A (en) * | 1988-10-06 | 1990-08-21 | Protocol Systems, Inc. | Method of artifact rejection for noninvasive blood-pressure measurement by prediction and adjustment of blood-pressure data |
US4951680A (en) * | 1987-09-30 | 1990-08-28 | National Research Development Corporation | Fetal monitoring during labor |
US4955379A (en) * | 1987-08-14 | 1990-09-11 | National Research Development Corporation | Motion artefact rejection system for pulse oximeters |
US4960126A (en) * | 1988-01-15 | 1990-10-02 | Criticare Systems, Inc. | ECG synchronized pulse oximeter |
US4967571A (en) * | 1988-09-16 | 1990-11-06 | Messer. Griesheim Gmbh | Device for the cryogenic pelletization of liquids |
US5036857A (en) * | 1989-10-26 | 1991-08-06 | Rutgers, The State University Of New Jersey | Noninvasive diagnostic system for coronary artery disease |
US5040201A (en) * | 1989-05-26 | 1991-08-13 | U.S. Philips Corporation | X-ray exposure synchronization method and apparatus |
US5041187A (en) * | 1988-04-29 | 1991-08-20 | Thor Technology Corporation | Oximeter sensor assembly with integral cable and method of forming the same |
US5054495A (en) * | 1989-07-10 | 1991-10-08 | Colin Electronics Co., Ltd. | Automatic blood-pressure measuring apparatus |
US5152296A (en) * | 1990-03-01 | 1992-10-06 | Hewlett-Packard Company | Dual-finger vital signs monitor |
US5218962A (en) * | 1991-04-15 | 1993-06-15 | Nellcor Incorporated | Multiple region pulse oximetry probe and oximeter |
US5243993A (en) * | 1991-06-28 | 1993-09-14 | Life Fitness | Apparatus and method for measuring heart rate |
US5379774A (en) * | 1990-10-23 | 1995-01-10 | Sankyo Company Limited | Measurement of arterial elasticity and the frequency characteristic of the compliance of an artery |
USRE35122E (en) * | 1985-04-01 | 1995-12-19 | Nellcor Incorporated | Method and apparatus for detecting optical pulses |
US5494032A (en) * | 1991-07-12 | 1996-02-27 | Sandia Corporation | Oximeter for reliable clinical determination of blood oxygen saturation in a fetus |
US5853364A (en) * | 1995-08-07 | 1998-12-29 | Nellcor Puritan Bennett, Inc. | Method and apparatus for estimating physiological parameters using model-based adaptive filtering |
Family Cites Families (291)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US363120A (en) * | 1887-05-17 | Water-filter | ||
US2009A (en) * | 1841-03-18 | Improvement in machines for boring war-rockets | ||
US3704706A (en) | 1969-10-23 | 1972-12-05 | Univ Drexel | Heart rate and respiratory monitor |
US3647299A (en) | 1970-04-20 | 1972-03-07 | American Optical Corp | Oximeter |
US3659591A (en) | 1970-08-24 | 1972-05-02 | Doll Research | Electromagnetic flowmeter |
US3991277A (en) | 1973-02-15 | 1976-11-09 | Yoshimutsu Hirata | Frequency division multiplex system using comb filters |
JPS5725217B2 (US20020128544A1-20020912-P00018.png) | 1974-10-14 | 1982-05-28 | ||
CA1037285A (en) | 1975-04-30 | 1978-08-29 | Glenfield Warner | Ear oximetry process and apparatus |
HU171629B (hu) | 1975-06-30 | 1978-02-28 | Medicor Muevek | Elektroskhema dlja opredelenija kraskorastvorennykh krivykh in vivo i in vitro, dlja rascheta minyty-ob ema serdca |
US4197836A (en) | 1975-11-06 | 1980-04-15 | Bios Inc. | Nuclear cardiac blood volume detecting apparatus |
US4063551A (en) | 1976-04-06 | 1977-12-20 | Unisen, Inc. | Blood pulse sensor and readout |
US4167331A (en) | 1976-12-20 | 1979-09-11 | Hewlett-Packard Company | Multi-wavelength incremental absorbence oximeter |
US4281645A (en) | 1977-06-28 | 1981-08-04 | Duke University, Inc. | Method and apparatus for monitoring metabolism in body organs |
JPS5493890A (en) | 1977-12-30 | 1979-07-25 | Minolta Camera Kk | Eyeeground oximeter |
US4238746A (en) | 1978-03-20 | 1980-12-09 | The United States Of America As Represented By The Secretary Of The Navy | Adaptive line enhancer |
JPS5524004A (en) | 1978-06-22 | 1980-02-20 | Minolta Camera Kk | Oxymeter |
US4519396A (en) | 1979-03-30 | 1985-05-28 | American Home Products Corporation (Del.) | Fetal heart rate monitor apparatus and method for combining electrically and mechanically derived cardiographic signals |
US4243935A (en) | 1979-05-18 | 1981-01-06 | The United States Of America As Represented By The Secretary Of The Navy | Adaptive detector |
JPS56104646A (en) | 1980-01-25 | 1981-08-20 | Minolta Camera Kk | Optical analyzer for forming ratio of element contained in organism |
US4407290A (en) | 1981-04-01 | 1983-10-04 | Biox Technology, Inc. | Blood constituent measuring device and method |
JPS5865137A (ja) | 1981-10-13 | 1983-04-18 | 株式会社東芝 | 生体情報処理装置 |
JPS5865137U (ja) | 1981-10-28 | 1983-05-02 | 日本発条株式会社 | 高さ調整装置 |
JPS58143243A (ja) | 1982-02-19 | 1983-08-25 | Minolta Camera Co Ltd | 非観血式血中色素測定装置 |
US4519936A (en) * | 1982-07-20 | 1985-05-28 | Veb Werk Fuer Fernsehelektronik Im Kombinat Mikroelektronik | Nematic liquid crystals and method of production |
EP0102816A3 (en) | 1982-09-02 | 1985-08-28 | Nellcor Incorporated | Pulse oximeter |
EP0104771B1 (en) | 1982-09-02 | 1990-05-23 | Nellcor Incorporated | Pulse oximeter monitor |
DE3234388A1 (de) | 1982-09-16 | 1984-04-05 | Siemens AG, 1000 Berlin und 8000 München | Verfahren und vorrichtung zur quantitativen ermittlung der blut-sauerstoff-saettigung aus fotometrischen messwerten |
DE3328862A1 (de) | 1982-09-16 | 1985-02-28 | Siemens AG, 1000 Berlin und 8000 München | Verfahren und vorrichtung zur gewebefotometrie, insbesondere zur quantitativen ermittlung der blut-sauerstoff-saettigung aus fotometrischen messwerten |
US4623248A (en) | 1983-02-16 | 1986-11-18 | Abbott Laboratories | Apparatus and method for determining oxygen saturation levels with increased accuracy |
US4893630A (en) | 1984-04-06 | 1990-01-16 | Trinity Computing Systems, Inc. | Apparatus and method for analyzing physiological conditions within an organ of a living body |
US4585199A (en) * | 1984-05-22 | 1986-04-29 | Selfix, Inc. | Fixture mounting arrangement |
US4649505A (en) | 1984-07-02 | 1987-03-10 | General Electric Company | Two-input crosstalk-resistant adaptive noise canceller |
GB2166326B (en) | 1984-10-29 | 1988-04-27 | Hazeltine Corp | Lms adaptive loop module |
US4617589A (en) | 1984-12-17 | 1986-10-14 | Rca Corporation | Adaptive frame comb filter system |
US4781200A (en) | 1985-10-04 | 1988-11-01 | Baker Donald A | Ambulatory non-invasive automatic fetal monitoring system |
US4870588A (en) | 1985-10-21 | 1989-09-26 | Sundstrand Data Control, Inc. | Signal processor for inertial measurement using coriolis force sensing accelerometer arrangements |
JPS62135020A (ja) | 1985-12-06 | 1987-06-18 | Nec Corp | 雑音消去装置 |
US4892101A (en) | 1986-08-18 | 1990-01-09 | Physio-Control Corporation | Method and apparatus for offsetting baseline portion of oximeter signal |
US4859056A (en) | 1986-08-18 | 1989-08-22 | Physio-Control Corporation | Multiple-pulse method and apparatus for use in oximetry |
US4800495A (en) | 1986-08-18 | 1989-01-24 | Physio-Control Corporation | Method and apparatus for processing signals used in oximetry |
US5259381A (en) | 1986-08-18 | 1993-11-09 | Physio-Control Corporation | Apparatus for the automatic calibration of signals employed in oximetry |
US4913150A (en) | 1986-08-18 | 1990-04-03 | Physio-Control Corporation | Method and apparatus for the automatic calibration of signals employed in oximetry |
US4819646A (en) | 1986-08-18 | 1989-04-11 | Physio-Control Corporation | Feedback-controlled method and apparatus for processing signals used in oximetry |
US4869253A (en) | 1986-08-18 | 1989-09-26 | Physio-Control Corporation | Method and apparatus for indicating perfusion and oxygen saturation trends in oximetry |
JPS6365845A (ja) | 1986-09-05 | 1988-03-24 | ミノルタ株式会社 | オキシメ−タ装置 |
US4824242A (en) | 1986-09-26 | 1989-04-25 | Sensormedics Corporation | Non-invasive oximeter and method |
US4897162A (en) | 1986-11-14 | 1990-01-30 | The Cleveland Clinic Foundation | Pulse voltammetry |
BR8707578A (pt) | 1986-12-12 | 1988-12-06 | Mark Yelderman | Aparelho e processo para medir nao invasivamente a concentracao de um constituinte sanguineo |
USRE33643E (en) | 1987-04-30 | 1991-07-23 | Nonin Medical, Inc. | Pulse oximeter with circuit leakage and ambient light compensation |
US4773422A (en) | 1987-04-30 | 1988-09-27 | Nonin Medical, Inc. | Single channel pulse oximeter |
JPS63275324A (ja) | 1987-05-08 | 1988-11-14 | Hamamatsu Photonics Kk | 診断装置 |
DE3768088D1 (de) | 1987-06-03 | 1991-03-28 | Hewlett Packard Gmbh | Verfahren zur bestimmung der perfusion. |
DE3723881A1 (de) | 1987-07-18 | 1989-01-26 | Nicolay Gmbh | Verfahren zum ermitteln der sauerstoffsaettigung des blutes eines lebenden organismus und elektronische schaltung sowie vorrichtung zum durchfuehren dieses verfahrens |
US4860759A (en) | 1987-09-08 | 1989-08-29 | Criticare Systems, Inc. | Vital signs monitor |
US4796636A (en) * | 1987-09-10 | 1989-01-10 | Nippon Colin Co., Ltd. | Noninvasive reflectance oximeter |
US4819752A (en) | 1987-10-02 | 1989-04-11 | Datascope Corp. | Blood constituent measuring device and method |
US4848901A (en) | 1987-10-08 | 1989-07-18 | Critikon, Inc. | Pulse oximeter sensor control system |
US4807631A (en) | 1987-10-09 | 1989-02-28 | Critikon, Inc. | Pulse oximetry system |
US4965840A (en) | 1987-11-27 | 1990-10-23 | State University Of New York | Method and apparatus for determining the distances between surface-patches of a three-dimensional spatial scene and a camera system |
US4927264A (en) | 1987-12-02 | 1990-05-22 | Omron Tateisi Electronics Co. | Non-invasive measuring method and apparatus of blood constituents |
US5078136A (en) | 1988-03-30 | 1992-01-07 | Nellcor Incorporated | Method and apparatus for calculating arterial oxygen saturation based plethysmographs including transients |
JPH06105190B2 (ja) | 1988-03-31 | 1994-12-21 | 工業技術院長 | 信号解析装置 |
US5069213A (en) | 1988-04-29 | 1991-12-03 | Thor Technology Corporation | Oximeter sensor assembly with integral cable and encoder |
US4964408A (en) | 1988-04-29 | 1990-10-23 | Thor Technology Corporation | Oximeter sensor assembly with integral cable |
DE3884191T2 (de) | 1988-05-09 | 1994-01-13 | Hewlett Packard Gmbh | Verarbeitungsverfahren von Signalen, besonders für Oximetriemessungen im lebenden menschlichen Gewebe. |
US4948248A (en) | 1988-07-22 | 1990-08-14 | Invivo Research Inc. | Blood constituent measuring device and method |
EP0352923A1 (en) | 1988-07-25 | 1990-01-31 | BAXTER INTERNATIONAL INC. (a Delaware corporation) | Spectrophotometric apparatus and method for monitoring oxygen saturation |
US5246022A (en) | 1988-07-29 | 1993-09-21 | Gina Israel | Apparatus for holding dental floss containers and spools |
US4858199A (en) | 1988-09-06 | 1989-08-15 | Mobile Oil Corporation | Method and apparatus for cancelling nonstationary sinusoidal noise from seismic data |
US4883356A (en) | 1988-09-13 | 1989-11-28 | The Perkin-Elmer Corporation | Spectrometer detector mounting assembly |
JPH0657216B2 (ja) | 1988-09-14 | 1994-08-03 | 住友電気工業株式会社 | 肝機能検査装置 |
US5042499A (en) | 1988-09-30 | 1991-08-27 | Frank Thomas H | Noninvasive electrocardiographic method of real time signal processing for obtaining and displaying instantaneous fetal heart rate and fetal heart rate beat-to-beat variability |
US5163438A (en) | 1988-11-14 | 1992-11-17 | Paramed Technology Incorporated | Method and apparatus for continuously and noninvasively measuring the blood pressure of a patient |
US4960128A (en) | 1988-11-14 | 1990-10-02 | Paramed Technology Incorporated | Method and apparatus for continuously and non-invasively measuring the blood pressure of a patient |
JPH06103257B2 (ja) | 1988-12-19 | 1994-12-14 | 大塚電子株式会社 | 光散乱を用いた物質の吸光係数測定方法および装置 |
US5353356A (en) | 1989-02-09 | 1994-10-04 | Waugh Richard M | Product gauge methods and apparatus for use in the optical determination of the acceptability of products |
SU1674798A1 (ru) | 1989-02-20 | 1991-09-07 | Смоленский филиал Московского энергетического института | Устройство дл анализа фотоплетизмографического сигнала |
EP0385805B1 (en) | 1989-03-03 | 1996-06-05 | Edward W. Stark | Signal processing method and apparatus |
US4956867A (en) | 1989-04-20 | 1990-09-11 | Massachusetts Institute Of Technology | Adaptive beamforming for noise reduction |
US5193124A (en) | 1989-06-29 | 1993-03-09 | The Research Foundation Of State University Of New York | Computational methods and electronic camera apparatus for determining distance of objects, rapid autofocusing, and obtaining improved focus images |
EP0410658B1 (en) | 1989-07-25 | 1995-09-27 | Seiko Instruments Inc. | Pulsimeter |
GB2235288B (en) | 1989-07-27 | 1993-02-10 | Nat Res Dev | Oximeters |
US5299120A (en) | 1989-09-15 | 1994-03-29 | Hewlett-Packard Company | Method for digitally processing signals containing information regarding arterial blood flow |
US5190038A (en) | 1989-11-01 | 1993-03-02 | Novametrix Medical Systems, Inc. | Pulse oximeter with improved accuracy and response time |
US4958867A (en) | 1990-01-31 | 1990-09-25 | Champagne Phillip A | Locking device for washers and dryers |
DE69030513T2 (de) | 1990-02-15 | 1997-07-24 | Hewlett Packard Gmbh | Gerät und Verfahren zur nichtinvasiven Messung der Sauerstoffsättigung |
GB9011887D0 (en) | 1990-05-26 | 1990-07-18 | Le Fit Ltd | Pulse responsive device |
US5319355A (en) | 1991-03-06 | 1994-06-07 | Russek Linda G | Alarm for patient monitor and life support equipment system |
WO1992015955A1 (en) | 1991-03-07 | 1992-09-17 | Vital Signals, Inc. | Signal processing apparatus and method |
MX9702434A (es) | 1991-03-07 | 1998-05-31 | Masimo Corp | Aparato de procesamiento de señales. |
US5632272A (en) | 1991-03-07 | 1997-05-27 | Masimo Corporation | Signal processing apparatus |
US5490505A (en) | 1991-03-07 | 1996-02-13 | Masimo Corporation | Signal processing apparatus |
JP3136637B2 (ja) | 1991-03-18 | 2001-02-19 | スズキ株式会社 | 車両用バンパーのエネルギー吸収体 |
US5638818A (en) * | 1991-03-21 | 1997-06-17 | Masimo Corporation | Low noise optical probe |
US6580086B1 (en) * | 1999-08-26 | 2003-06-17 | Masimo Corporation | Shielded optical probe and method |
US5645440A (en) * | 1995-10-16 | 1997-07-08 | Masimo Corporation | Patient cable connector |
US6541756B2 (en) * | 1991-03-21 | 2003-04-01 | Masimo Corporation | Shielded optical probe having an electrical connector |
US5995855A (en) * | 1998-02-11 | 1999-11-30 | Masimo Corporation | Pulse oximetry sensor adapter |
US5170791A (en) | 1991-03-28 | 1992-12-15 | Hewlett-Packard Company | Method and apparatus for calculating the fetal heart rate |
US5377676A (en) * | 1991-04-03 | 1995-01-03 | Cedars-Sinai Medical Center | Method for determining the biodistribution of substances using fluorescence spectroscopy |
US5273036A (en) | 1991-04-03 | 1993-12-28 | Ppg Industries, Inc. | Apparatus and method for monitoring respiration |
JPH05189617A (ja) | 1991-04-15 | 1993-07-30 | Microsoft Corp | 手書き文字認識に於けるアークのセグメント化の方法と装置 |
US5934277A (en) * | 1991-09-03 | 1999-08-10 | Datex-Ohmeda, Inc. | System for pulse oximetry SpO2 determination |
US5481620A (en) | 1991-09-27 | 1996-01-02 | E. I. Du Pont De Nemours And Company | Adaptive vision system |
WO1993008534A1 (en) | 1991-10-24 | 1993-04-29 | Hewlett-Packard Gmbh | Apparatus and method for evaluating the fetal condition |
AU667199B2 (en) | 1991-11-08 | 1996-03-14 | Physiometrix, Inc. | EEG headpiece with disposable electrodes and apparatus and system and method for use therewith |
US5246002A (en) | 1992-02-11 | 1993-09-21 | Physio-Control Corporation | Noise insensitive pulse transmittance oximeter |
US5331394A (en) | 1992-04-10 | 1994-07-19 | Metaphase Corporation | Automated lensometer |
US5355880A (en) | 1992-07-06 | 1994-10-18 | Sandia Corporation | Reliable noninvasive measurement of blood gases |
JP3116252B2 (ja) | 1992-07-09 | 2000-12-11 | 日本光電工業株式会社 | パルスオキシメータ |
US5283306A (en) * | 1992-08-26 | 1994-02-01 | Nalco Chemical Company | Hydrophobic polyelectrolytes used in removing color |
WO1995021567A1 (en) | 1992-09-15 | 1995-08-17 | Increa Oy | Method and apparatus for measuring physical condition |
US5368224A (en) * | 1992-10-23 | 1994-11-29 | Nellcor Incorporated | Method for reducing ambient noise effects in electronic monitoring instruments |
US5270942A (en) | 1992-12-04 | 1993-12-14 | United Technologies Corporation | Processing ultrasonic measurements of a rotating hollow workpiece |
JPH08503867A (ja) | 1992-12-07 | 1996-04-30 | クラテクノロジーズ インク | 電子聴診器 |
US5384451A (en) | 1993-01-29 | 1995-01-24 | United Parcel Service Of America, Inc. | Method and apparatus for decoding bar code symbols using composite signals |
US5404003A (en) | 1993-02-01 | 1995-04-04 | United Parcel Service Of America, Inc. | Method and apparatus for decoding bar code symbols using byte-based searching |
US5368026A (en) * | 1993-03-26 | 1994-11-29 | Nellcor Incorporated | Oximeter with motion detection for alarm modification |
US5341805A (en) | 1993-04-06 | 1994-08-30 | Cedars-Sinai Medical Center | Glucose fluorescence monitor and method |
US5494043A (en) | 1993-05-04 | 1996-02-27 | Vital Insite, Inc. | Arterial sensor |
USD353196S (en) | 1993-05-28 | 1994-12-06 | Gary Savage | Stethoscope head |
USD353195S (en) | 1993-05-28 | 1994-12-06 | Gary Savage | Electronic stethoscope housing |
US5452717A (en) | 1993-07-14 | 1995-09-26 | Masimo Corporation | Finger-cot probe |
US5337744A (en) | 1993-07-14 | 1994-08-16 | Masimo Corporation | Low noise finger cot probe |
JP3387171B2 (ja) | 1993-09-28 | 2003-03-17 | セイコーエプソン株式会社 | 脈波検出装置および運動強度測定装置 |
US5456252A (en) | 1993-09-30 | 1995-10-10 | Cedars-Sinai Medical Center | Induced fluorescence spectroscopy blood perfusion and pH monitor and method |
US7376453B1 (en) | 1993-10-06 | 2008-05-20 | Masimo Corporation | Signal processing apparatus |
US5357965A (en) | 1993-11-24 | 1994-10-25 | General Electric Company | Method for controlling adaptive color flow processing using fuzzy logic |
US5419396A (en) * | 1993-12-29 | 1995-05-30 | Amoco Corporation | Method for stimulating a coal seam to enhance the recovery of methane from the coal seam |
US5533511A (en) | 1994-01-05 | 1996-07-09 | Vital Insite, Incorporated | Apparatus and method for noninvasive blood pressure measurement |
USD359546S (en) | 1994-01-27 | 1995-06-20 | The Ratechnologies Inc. | Housing for a dental unit disinfecting device |
US5398003A (en) | 1994-03-30 | 1995-03-14 | Apple Computer, Inc. | Pulse width modulation speaker amplifier |
US5575284A (en) | 1994-04-01 | 1996-11-19 | University Of South Florida | Portable pulse oximeter |
US5785659A (en) | 1994-04-15 | 1998-07-28 | Vital Insite, Inc. | Automatically activated blood pressure measurement device |
US6371921B1 (en) * | 1994-04-15 | 2002-04-16 | Masimo Corporation | System and method of determining whether to recalibrate a blood pressure monitor |
US5791347A (en) | 1994-04-15 | 1998-08-11 | Vital Insite, Inc. | Motion insensitive pulse detector |
US5904654A (en) | 1995-10-20 | 1999-05-18 | Vital Insite, Inc. | Exciter-detector unit for measuring physiological parameters |
US5590649A (en) | 1994-04-15 | 1997-01-07 | Vital Insite, Inc. | Apparatus and method for measuring an induced perturbation to determine blood pressure |
US5810734A (en) * | 1994-04-15 | 1998-09-22 | Vital Insite, Inc. | Apparatus and method for measuring an induced perturbation to determine a physiological parameter |
USD362063S (en) | 1994-04-21 | 1995-09-05 | Gary Savage | Stethoscope headset |
USD361840S (en) | 1994-04-21 | 1995-08-29 | Gary Savage | Stethoscope head |
USD363120S (en) | 1994-04-21 | 1995-10-10 | Gary Savage | Stethoscope ear tip |
US5561275A (en) | 1994-04-28 | 1996-10-01 | Delstar Services Informatiques (1993) Inc. | Headset for electronic stethoscope |
US5458128A (en) | 1994-06-17 | 1995-10-17 | Polanyi; Michael | Method and apparatus for noninvasively measuring concentration of a dye in arterial blood |
US5549111A (en) | 1994-08-05 | 1996-08-27 | Acuson Corporation | Method and apparatus for adjustable frequency scanning in ultrasound imaging |
US8019400B2 (en) * | 1994-10-07 | 2011-09-13 | Masimo Corporation | Signal processing apparatus |
JP2643872B2 (ja) * | 1994-11-29 | 1997-08-20 | 日本電気株式会社 | ボンディング・オプション回路 |
US5562002A (en) | 1995-02-03 | 1996-10-08 | Sensidyne Inc. | Positive displacement piston flow meter with damping assembly |
US5662105A (en) | 1995-05-17 | 1997-09-02 | Spacelabs Medical, Inc. | System and method for the extractment of physiological signals |
US5758644A (en) | 1995-06-07 | 1998-06-02 | Masimo Corporation | Manual and automatic probe calibration |
US5743262A (en) | 1995-06-07 | 1998-04-28 | Masimo Corporation | Blood glucose monitoring system |
US6517283B2 (en) | 2001-01-16 | 2003-02-11 | Donald Edward Coffey | Cascading chute drainage system |
US6931268B1 (en) | 1995-06-07 | 2005-08-16 | Masimo Laboratories, Inc. | Active pulse blood constituent monitoring |
US5760910A (en) * | 1995-06-07 | 1998-06-02 | Masimo Corporation | Optical filter for spectroscopic measurement and method of producing the optical filter |
US5638816A (en) | 1995-06-07 | 1997-06-17 | Masimo Corporation | Active pulse blood constituent monitoring |
US5645060A (en) | 1995-06-14 | 1997-07-08 | Nellcor Puritan Bennett Incorporated | Method and apparatus for removing artifact and noise from pulse oximetry |
US5800348A (en) | 1995-08-31 | 1998-09-01 | Hewlett-Packard Company | Apparatus and method for medical monitoring, in particular pulse oximeter |
EP0760223A1 (en) | 1995-08-31 | 1997-03-05 | Hewlett-Packard GmbH | Apparatus for monitoring, in particular pulse oximeter |
EP0761159B1 (en) | 1995-08-31 | 1999-09-29 | Hewlett-Packard Company | Apparatus for medical monitoring, in particular pulse oximeter |
USD393830S (en) | 1995-10-16 | 1998-04-28 | Masimo Corporation | Patient cable connector |
US5588435A (en) | 1995-11-22 | 1996-12-31 | Siemens Medical Systems, Inc. | System and method for automatic measurement of body structures |
US6232609B1 (en) * | 1995-12-01 | 2001-05-15 | Cedars-Sinai Medical Center | Glucose monitoring apparatus and method using laser-induced emission spectroscopy |
US6253097B1 (en) | 1996-03-06 | 2001-06-26 | Datex-Ohmeda, Inc. | Noninvasive medical monitoring instrument using surface emitting laser devices |
US5890929A (en) | 1996-06-19 | 1999-04-06 | Masimo Corporation | Shielded medical connector |
USD393476S (en) | 1996-06-24 | 1998-04-14 | Gebruder Lodige Maschinenbau-Gesellschaft mit beschrankter Haftung | Industrial mixing machine |
US6027452A (en) | 1996-06-26 | 2000-02-22 | Vital Insite, Inc. | Rapid non-invasive blood pressure measuring device |
US5842981A (en) | 1996-07-17 | 1998-12-01 | Criticare Systems, Inc. | Direct to digital oximeter |
US5830137A (en) | 1996-11-18 | 1998-11-03 | University Of South Florida | Green light pulse oximeter |
CA2283860A1 (en) | 1997-03-21 | 1998-10-01 | Nellcor Puritan Bennett Inc. | Method and apparatus for arbitrating to obtain best estimates for blood constituent values and rejecting harmonics |
JP2001517991A (ja) | 1997-03-21 | 2001-10-09 | ネルコー・ピューリタン・ベネット・インコーポレイテッド | データ信号適応平均化方法及び装置 |
DE69700253T2 (de) | 1997-04-12 | 1999-09-23 | Hewlett-Packard Co., Palo Alto | Verfahren und Vorrichtung zur Bestimmung der Konzentration eines Bestandteils |
US6002952A (en) | 1997-04-14 | 1999-12-14 | Masimo Corporation | Signal processing apparatus and method |
US6229856B1 (en) | 1997-04-14 | 2001-05-08 | Masimo Corporation | Method and apparatus for demodulating signals in a pulse oximetry system |
US5919134A (en) * | 1997-04-14 | 1999-07-06 | Masimo Corp. | Method and apparatus for demodulating signals in a pulse oximetry system |
US6124597A (en) * | 1997-07-07 | 2000-09-26 | Cedars-Sinai Medical Center | Method and devices for laser induced fluorescence attenuation spectroscopy |
US6184521B1 (en) * | 1998-01-06 | 2001-02-06 | Masimo Corporation | Photodiode detector with integrated noise shielding |
US6241683B1 (en) | 1998-02-20 | 2001-06-05 | INSTITUT DE RECHERCHES CLINIQUES DE MONTRéAL (IRCM) | Phonospirometry for non-invasive monitoring of respiration |
US6525386B1 (en) * | 1998-03-10 | 2003-02-25 | Masimo Corporation | Non-protruding optoelectronic lens |
US6165005A (en) | 1998-03-19 | 2000-12-26 | Masimo Corporation | Patient cable sensor switch |
US5997343A (en) | 1998-03-19 | 1999-12-07 | Masimo Corporation | Patient cable sensor switch |
US7899518B2 (en) | 1998-04-06 | 2011-03-01 | Masimo Laboratories, Inc. | Non-invasive tissue glucose level monitoring |
US6728560B2 (en) * | 1998-04-06 | 2004-04-27 | The General Hospital Corporation | Non-invasive tissue glucose level monitoring |
US6505059B1 (en) * | 1998-04-06 | 2003-01-07 | The General Hospital Corporation | Non-invasive tissue glucose level monitoring |
US6721582B2 (en) * | 1999-04-06 | 2004-04-13 | Argose, Inc. | Non-invasive tissue glucose level monitoring |
US6334065B1 (en) * | 1998-06-03 | 2001-12-25 | Masimo Corporation | Stereo pulse oximeter |
US6128521A (en) | 1998-07-10 | 2000-10-03 | Physiometrix, Inc. | Self adjusting headgear appliance using reservoir electrodes |
US6285896B1 (en) | 1998-07-13 | 2001-09-04 | Masimo Corporation | Fetal pulse oximetry sensor |
US6129675A (en) | 1998-09-11 | 2000-10-10 | Jay; Gregory D. | Device and method for measuring pulsus paradoxus |
US7245953B1 (en) | 1999-04-12 | 2007-07-17 | Masimo Corporation | Reusable pulse oximeter probe and disposable bandage apparatii |
USRE41912E1 (en) | 1998-10-15 | 2010-11-02 | Masimo Corporation | Reusable pulse oximeter probe and disposable bandage apparatus |
US6721585B1 (en) | 1998-10-15 | 2004-04-13 | Sensidyne, Inc. | Universal modular pulse oximeter probe for use with reusable and disposable patient attachment devices |
US6343224B1 (en) * | 1998-10-15 | 2002-01-29 | Sensidyne, Inc. | Reusable pulse oximeter probe and disposable bandage apparatus |
US6321100B1 (en) | 1999-07-13 | 2001-11-20 | Sensidyne, Inc. | Reusable pulse oximeter probe with disposable liner |
US6684091B2 (en) * | 1998-10-15 | 2004-01-27 | Sensidyne, Inc. | Reusable pulse oximeter probe and disposable bandage method |
US6519487B1 (en) * | 1998-10-15 | 2003-02-11 | Sensidyne, Inc. | Reusable pulse oximeter probe and disposable bandage apparatus |
US6144868A (en) | 1998-10-15 | 2000-11-07 | Sensidyne, Inc. | Reusable pulse oximeter probe and disposable bandage apparatus |
US6463311B1 (en) | 1998-12-30 | 2002-10-08 | Masimo Corporation | Plethysmograph pulse recognition processor |
US6684090B2 (en) * | 1999-01-07 | 2004-01-27 | Masimo Corporation | Pulse oximetry data confidence indicator |
US6606511B1 (en) * | 1999-01-07 | 2003-08-12 | Masimo Corporation | Pulse oximetry pulse indicator |
US6770028B1 (en) | 1999-01-25 | 2004-08-03 | Masimo Corporation | Dual-mode pulse oximeter |
US6658276B2 (en) | 1999-01-25 | 2003-12-02 | Masimo Corporation | Pulse oximeter user interface |
US20020140675A1 (en) | 1999-01-25 | 2002-10-03 | Ali Ammar Al | System and method for altering a display mode based on a gravity-responsive sensor |
WO2000042911A1 (en) * | 1999-01-25 | 2000-07-27 | Masimo Corporation | Universal/upgrading pulse oximeter |
US6438399B1 (en) | 1999-02-16 | 2002-08-20 | The Children's Hospital Of Philadelphia | Multi-wavelength frequency domain near-infrared cerebral oximeter |
US6360114B1 (en) * | 1999-03-25 | 2002-03-19 | Masimo Corporation | Pulse oximeter probe-off detector |
WO2000077674A1 (de) | 1999-06-10 | 2000-12-21 | Koninklijke Philips Electronics N.V. | Erkennung eines nutzsignals in einem messsignal |
JP4495378B2 (ja) | 1999-06-10 | 2010-07-07 | コーニンクレッカ フィリップス エレクトロニクス エヌ ヴィ | とりわけ酸素飽和度測定からの医学的測定信号のような測定信号のための品質インディケータ |
EP1192560A1 (de) | 1999-06-10 | 2002-04-03 | Agilent Technologies, Inc. (a Delaware corporation) | Störungsunterdrückung für messsignale mit periodischem nutzsignal |
CN1358075A (zh) | 1999-06-18 | 2002-07-10 | 马西默有限公司 | 脉冲血氧计探头移离检测系统 |
US6301493B1 (en) | 1999-07-10 | 2001-10-09 | Physiometrix, Inc. | Reservoir electrodes for electroencephalograph headgear appliance |
US6515273B2 (en) * | 1999-08-26 | 2003-02-04 | Masimo Corporation | System for indicating the expiration of the useful operating life of a pulse oximetry sensor |
JP2001069362A (ja) * | 1999-08-27 | 2001-03-16 | Minolta Co Ltd | 色補正装置 |
US6943348B1 (en) | 1999-10-19 | 2005-09-13 | Masimo Corporation | System for detecting injection holding material |
US6430437B1 (en) | 1999-10-27 | 2002-08-06 | Physiometrix, Inc. | Module for acquiring electroencephalograph signals from a patient |
US6317627B1 (en) | 1999-11-02 | 2001-11-13 | Physiometrix, Inc. | Anesthesia monitoring system based on electroencephalographic signals |
US6639668B1 (en) | 1999-11-03 | 2003-10-28 | Argose, Inc. | Asynchronous fluorescence scan |
US6542764B1 (en) * | 1999-12-01 | 2003-04-01 | Masimo Corporation | Pulse oximeter monitor for expressing the urgency of the patient's condition |
US6671531B2 (en) | 1999-12-09 | 2003-12-30 | Masimo Corporation | Sensor wrap including foldable applicator |
US6950687B2 (en) | 1999-12-09 | 2005-09-27 | Masimo Corporation | Isolation and communication element for a resposable pulse oximetry sensor |
US6377829B1 (en) | 1999-12-09 | 2002-04-23 | Masimo Corporation | Resposable pulse oximetry sensor |
US6152754A (en) | 1999-12-21 | 2000-11-28 | Masimo Corporation | Circuit board based cable connector |
JP2003522577A (ja) | 2000-02-18 | 2003-07-29 | アーゴス インク | 細胞サンプルおよび組織サンプルの緑色〜紫外スペクトルの多変量分析 |
US6597932B2 (en) | 2000-02-18 | 2003-07-22 | Argose, Inc. | Generation of spatially-averaged excitation-emission map in heterogeneous tissue |
US6430525B1 (en) * | 2000-06-05 | 2002-08-06 | Masimo Corporation | Variable mode averager |
US6470199B1 (en) | 2000-06-21 | 2002-10-22 | Masimo Corporation | Elastic sock for positioning an optical probe |
US6697656B1 (en) * | 2000-06-27 | 2004-02-24 | Masimo Corporation | Pulse oximetry sensor compatible with multiple pulse oximetry systems |
US6640116B2 (en) | 2000-08-18 | 2003-10-28 | Masimo Corporation | Optical spectroscopy pathlength measurement system |
US6368283B1 (en) * | 2000-09-08 | 2002-04-09 | Institut De Recherches Cliniques De Montreal | Method and apparatus for estimating systolic and mean pulmonary artery pressures of a patient |
US6760607B2 (en) | 2000-12-29 | 2004-07-06 | Masimo Corporation | Ribbon cable substrate pulse oximetry sensor |
US6985764B2 (en) | 2001-05-03 | 2006-01-10 | Masimo Corporation | Flex circuit shielded optical sensor |
US6850787B2 (en) | 2001-06-29 | 2005-02-01 | Masimo Laboratories, Inc. | Signal component processor |
US6697658B2 (en) | 2001-07-02 | 2004-02-24 | Masimo Corporation | Low power pulse oximeter |
US6595316B2 (en) | 2001-07-18 | 2003-07-22 | Andromed, Inc. | Tension-adjustable mechanism for stethoscope earpieces |
US6701170B2 (en) * | 2001-11-02 | 2004-03-02 | Nellcor Puritan Bennett Incorporated | Blind source separation of pulse oximetry signals |
US6934570B2 (en) | 2002-01-08 | 2005-08-23 | Masimo Corporation | Physiological sensor combination |
US7355512B1 (en) * | 2002-01-24 | 2008-04-08 | Masimo Corporation | Parallel alarm processor |
US6822564B2 (en) * | 2002-01-24 | 2004-11-23 | Masimo Corporation | Parallel measurement alarm processor |
WO2003065557A2 (en) * | 2002-01-25 | 2003-08-07 | Masimo Corporation | Power supply rail controller |
US7020507B2 (en) | 2002-01-31 | 2006-03-28 | Dolphin Medical, Inc. | Separating motion from cardiac signals using second order derivative of the photo-plethysmogram and fast fourier transforms |
US6961598B2 (en) | 2002-02-22 | 2005-11-01 | Masimo Corporation | Pulse and active pulse spectraphotometry |
US7509494B2 (en) | 2002-03-01 | 2009-03-24 | Masimo Corporation | Interface cable |
US6850788B2 (en) | 2002-03-25 | 2005-02-01 | Masimo Corporation | Physiological measurement communications adapter |
US6661161B1 (en) | 2002-06-27 | 2003-12-09 | Andromed Inc. | Piezoelectric biological sound monitor with printed circuit board |
US7096054B2 (en) | 2002-08-01 | 2006-08-22 | Masimo Corporation | Low noise optical housing |
US7341559B2 (en) * | 2002-09-14 | 2008-03-11 | Masimo Corporation | Pulse oximetry ear sensor |
US7274955B2 (en) | 2002-09-25 | 2007-09-25 | Masimo Corporation | Parameter compensated pulse oximeter |
US7142901B2 (en) | 2002-09-25 | 2006-11-28 | Masimo Corporation | Parameter compensated physiological monitor |
US7096052B2 (en) | 2002-10-04 | 2006-08-22 | Masimo Corporation | Optical probe including predetermined emission wavelength based on patient type |
US7027849B2 (en) * | 2002-11-22 | 2006-04-11 | Masimo Laboratories, Inc. | Blood parameter measurement system |
US6970792B1 (en) | 2002-12-04 | 2005-11-29 | Masimo Laboratories, Inc. | Systems and methods for determining blood oxygen saturation values using complex number encoding |
US7919713B2 (en) | 2007-04-16 | 2011-04-05 | Masimo Corporation | Low noise oximetry cable including conductive cords |
US7225006B2 (en) | 2003-01-23 | 2007-05-29 | Masimo Corporation | Attachment and optical probe |
US6920345B2 (en) | 2003-01-24 | 2005-07-19 | Masimo Corporation | Optical sensor including disposable and reusable elements |
US7003338B2 (en) * | 2003-07-08 | 2006-02-21 | Masimo Corporation | Method and apparatus for reducing coupling between signals |
WO2005007215A2 (en) | 2003-07-09 | 2005-01-27 | Glucolight Corporation | Method and apparatus for tissue oximetry |
US7500950B2 (en) | 2003-07-25 | 2009-03-10 | Masimo Corporation | Multipurpose sensor port |
US7254431B2 (en) | 2003-08-28 | 2007-08-07 | Masimo Corporation | Physiological parameter tracking system |
US7254434B2 (en) | 2003-10-14 | 2007-08-07 | Masimo Corporation | Variable pressure reusable sensor |
US7483729B2 (en) | 2003-11-05 | 2009-01-27 | Masimo Corporation | Pulse oximeter access apparatus and method |
US7373193B2 (en) | 2003-11-07 | 2008-05-13 | Masimo Corporation | Pulse oximetry data capture system |
WO2005065241A2 (en) | 2003-12-24 | 2005-07-21 | Argose, Inc. | Smmr (small molecule metabolite reporters) for use as in vivo glucose biosensors |
US7280858B2 (en) | 2004-01-05 | 2007-10-09 | Masimo Corporation | Pulse oximetry sensor |
US7510849B2 (en) | 2004-01-29 | 2009-03-31 | Glucolight Corporation | OCT based method for diagnosis and therapy |
US7371981B2 (en) | 2004-02-20 | 2008-05-13 | Masimo Corporation | Connector switch |
US7438683B2 (en) | 2004-03-04 | 2008-10-21 | Masimo Corporation | Application identification sensor |
EP1722676B1 (en) | 2004-03-08 | 2012-12-19 | Masimo Corporation | Physiological parameter system |
US7292883B2 (en) | 2004-03-31 | 2007-11-06 | Masimo Corporation | Physiological assessment system |
CA2464634A1 (en) | 2004-04-16 | 2005-10-16 | Andromed Inc. | Pap estimator |
US7343186B2 (en) * | 2004-07-07 | 2008-03-11 | Masimo Laboratories, Inc. | Multi-wavelength physiological monitor |
US7937128B2 (en) | 2004-07-09 | 2011-05-03 | Masimo Corporation | Cyanotic infant sensor |
US7254429B2 (en) | 2004-08-11 | 2007-08-07 | Glucolight Corporation | Method and apparatus for monitoring glucose levels in a biological tissue |
US7976472B2 (en) | 2004-09-07 | 2011-07-12 | Masimo Corporation | Noninvasive hypovolemia monitor |
USD566282S1 (en) * | 2005-02-18 | 2008-04-08 | Masimo Corporation | Stand for a portable patient monitor |
USD554263S1 (en) | 2005-02-18 | 2007-10-30 | Masimo Corporation | Portable patient monitor |
WO2006094171A1 (en) | 2005-03-01 | 2006-09-08 | Masimo Laboratories, Inc. | Multiple wavelength sensor drivers |
US7937129B2 (en) | 2005-03-21 | 2011-05-03 | Masimo Corporation | Variable aperture sensor |
WO2006110859A2 (en) | 2005-04-13 | 2006-10-19 | Glucolight Corporation | Method for data reduction and calibration of an oct-based blood glucose monitor |
US7962188B2 (en) | 2005-10-14 | 2011-06-14 | Masimo Corporation | Robust alarm system |
US7530942B1 (en) | 2005-10-18 | 2009-05-12 | Masimo Corporation | Remote sensing infant warmer |
US7990382B2 (en) | 2006-01-03 | 2011-08-02 | Masimo Corporation | Virtual display |
US7941199B2 (en) | 2006-05-15 | 2011-05-10 | Masimo Laboratories, Inc. | Sepsis monitor |
US8028701B2 (en) | 2006-05-31 | 2011-10-04 | Masimo Corporation | Respiratory monitoring |
US20080056823A1 (en) * | 2006-07-17 | 2008-03-06 | Farrell Joseph E Jr | Beach erosion abatement |
USD614305S1 (en) | 2008-02-29 | 2010-04-20 | Masimo Corporation | Connector assembly |
USD587657S1 (en) | 2007-10-12 | 2009-03-03 | Masimo Corporation | Connector assembly |
USD609193S1 (en) | 2007-10-12 | 2010-02-02 | Masimo Corporation | Connector assembly |
US7880626B2 (en) | 2006-10-12 | 2011-02-01 | Masimo Corporation | System and method for monitoring the life of a physiological sensor |
US7791155B2 (en) | 2006-12-22 | 2010-09-07 | Masimo Laboratories, Inc. | Detector shield |
US8048040B2 (en) | 2007-09-13 | 2011-11-01 | Masimo Corporation | Fluid titration system |
USD621516S1 (en) | 2008-08-25 | 2010-08-10 | Masimo Laboratories, Inc. | Patient monitoring sensor |
USD606659S1 (en) | 2008-08-25 | 2009-12-22 | Masimo Laboratories, Inc. | Patient monitor |
-
1997
- 1997-04-03 MX MX9702434A patent/MX9702434A/es unknown
-
1998
- 1998-11-17 US US09/195,791 patent/US7328053B1/en not_active Expired - Fee Related
-
2001
- 2001-12-03 US US10/006,427 patent/US6745060B2/en not_active Expired - Fee Related
- 2001-12-04 US US10/005,631 patent/US6650917B2/en not_active Expired - Fee Related
-
2002
- 2002-01-30 US US10/062,859 patent/US20020128544A1/en not_active Abandoned
-
2003
- 2003-09-30 US US10/677,050 patent/US7496393B2/en not_active Expired - Fee Related
- 2003-09-30 US US10/676,534 patent/US7254433B2/en not_active Expired - Fee Related
-
2004
- 2004-05-04 US US10/838,814 patent/US7530955B2/en not_active Expired - Fee Related
- 2004-05-04 US US10/838,593 patent/US7215984B2/en not_active Expired - Fee Related
- 2004-12-03 US US11/003,231 patent/US7383070B2/en not_active Expired - Fee Related
-
2005
- 2005-03-02 US US11/070,081 patent/US8948834B2/en not_active Expired - Fee Related
- 2005-06-15 US US11/154,093 patent/US7215986B2/en not_active Expired - Fee Related
-
2006
- 2006-05-11 US US11/432,278 patent/US7454240B2/en not_active Expired - Fee Related
-
2007
- 2007-05-25 US US11/754,238 patent/US8942777B2/en not_active Expired - Fee Related
- 2007-06-21 US US11/766,700 patent/US8046042B2/en not_active Expired - Fee Related
- 2007-06-21 US US11/766,714 patent/US8046041B2/en not_active Expired - Fee Related
- 2007-06-21 US US11/766,719 patent/US8036728B2/en not_active Expired - Fee Related
- 2007-08-20 US US11/842,117 patent/US7509154B2/en not_active Expired - Fee Related
-
2008
- 2008-09-29 JP JP2008249714A patent/JP2009039548A/ja active Pending
-
2012
- 2012-02-09 US US13/370,239 patent/US8364226B2/en not_active Expired - Fee Related
Patent Citations (41)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US3638640A (en) * | 1967-11-01 | 1972-02-01 | Robert F Shaw | Oximeter and method for in vivo determination of oxygen saturation in blood using three or more different wavelengths |
US4157708A (en) * | 1975-10-29 | 1979-06-12 | Minolta Camera Kabushiki Kaisha | Eye fundus plethysmograph assembly |
US4038536A (en) * | 1976-03-29 | 1977-07-26 | Rockwell International Corporation | Adaptive recursive least mean square error filter |
US4582068A (en) * | 1981-12-21 | 1986-04-15 | American Home Products Corporation | Systems and methods for processing physiological signals |
US4458691A (en) * | 1982-02-11 | 1984-07-10 | Arrhythmia Research Technology, Inc. | System and method for predicting ventricular tachycardia by adaptive high pass filter |
US4653498A (en) * | 1982-09-13 | 1987-03-31 | Nellcor Incorporated | Pulse oximeter monitor |
US4653498B1 (US20020128544A1-20020912-P00018.png) * | 1982-09-13 | 1989-04-18 | ||
US4537200A (en) * | 1983-07-07 | 1985-08-27 | The Board Of Trustees Of The Leland Stanford Junior University | ECG enhancement by adaptive cancellation of electrosurgical interference |
US4667680A (en) * | 1983-11-14 | 1987-05-26 | Hewlett-Packard Company | Apparatus and method for reduction in respiration artifact in pulmonary artery pressure measurement |
US4714341A (en) * | 1984-02-23 | 1987-12-22 | Minolta Camera Kabushiki Kaisha | Multi-wavelength oximeter having a means for disregarding a poor signal |
USRE35122E (en) * | 1985-04-01 | 1995-12-19 | Nellcor Incorporated | Method and apparatus for detecting optical pulses |
US4934372A (en) * | 1985-04-01 | 1990-06-19 | Nellcor Incorporated | Method and apparatus for detecting optical pulses |
US4928692A (en) * | 1985-04-01 | 1990-05-29 | Goodman David E | Method and apparatus for detecting optical pulses |
US4802486A (en) * | 1985-04-01 | 1989-02-07 | Nellcor Incorporated | Method and apparatus for detecting optical pulses |
US4911167A (en) * | 1985-06-07 | 1990-03-27 | Nellcor Incorporated | Method and apparatus for detecting optical pulses |
US4751931A (en) * | 1986-09-22 | 1988-06-21 | Allegheny-Singer Research Institute | Method and apparatus for determining his-purkinje activity |
US4867571A (en) * | 1986-09-26 | 1989-09-19 | Sensormedics Corporation | Wave form filter pulse detector and method for modulated signal |
US4793361A (en) * | 1987-03-13 | 1988-12-27 | Cardiac Pacemakers, Inc. | Dual channel P-wave detection in surface electrocardiographs |
US4799493A (en) * | 1987-03-13 | 1989-01-24 | Cardiac Pacemakers, Inc. | Dual channel coherent fibrillation detection system |
US4799486A (en) * | 1987-03-13 | 1989-01-24 | Cardiac Pacemakers, Inc. | Refractoriless atrial sensing in dual chamber pacemakers |
US4955379A (en) * | 1987-08-14 | 1990-09-11 | National Research Development Corporation | Motion artefact rejection system for pulse oximeters |
US4951680A (en) * | 1987-09-30 | 1990-08-28 | National Research Development Corporation | Fetal monitoring during labor |
US4863265A (en) * | 1987-10-16 | 1989-09-05 | Mine Safety Appliances Company | Apparatus and method for measuring blood constituents |
US4800885A (en) * | 1987-12-02 | 1989-01-31 | The Boc Group, Inc. | Blood constituent monitoring apparatus and methods with frequency division multiplexing |
US4960126A (en) * | 1988-01-15 | 1990-10-02 | Criticare Systems, Inc. | ECG synchronized pulse oximeter |
US4883353A (en) * | 1988-02-11 | 1989-11-28 | Puritan-Bennett Corporation | Pulse oximeter |
US4869254A (en) * | 1988-03-30 | 1989-09-26 | Nellcor Incorporated | Method and apparatus for calculating arterial oxygen saturation |
US5041187A (en) * | 1988-04-29 | 1991-08-20 | Thor Technology Corporation | Oximeter sensor assembly with integral cable and method of forming the same |
US4967571A (en) * | 1988-09-16 | 1990-11-06 | Messer. Griesheim Gmbh | Device for the cryogenic pelletization of liquids |
US4949710A (en) * | 1988-10-06 | 1990-08-21 | Protocol Systems, Inc. | Method of artifact rejection for noninvasive blood-pressure measurement by prediction and adjustment of blood-pressure data |
US5040201A (en) * | 1989-05-26 | 1991-08-13 | U.S. Philips Corporation | X-ray exposure synchronization method and apparatus |
US5054495A (en) * | 1989-07-10 | 1991-10-08 | Colin Electronics Co., Ltd. | Automatic blood-pressure measuring apparatus |
US5036857A (en) * | 1989-10-26 | 1991-08-06 | Rutgers, The State University Of New Jersey | Noninvasive diagnostic system for coronary artery disease |
US5152296A (en) * | 1990-03-01 | 1992-10-06 | Hewlett-Packard Company | Dual-finger vital signs monitor |
US5379774A (en) * | 1990-10-23 | 1995-01-10 | Sankyo Company Limited | Measurement of arterial elasticity and the frequency characteristic of the compliance of an artery |
US5218962A (en) * | 1991-04-15 | 1993-06-15 | Nellcor Incorporated | Multiple region pulse oximetry probe and oximeter |
US5243993A (en) * | 1991-06-28 | 1993-09-14 | Life Fitness | Apparatus and method for measuring heart rate |
US5494032A (en) * | 1991-07-12 | 1996-02-27 | Sandia Corporation | Oximeter for reliable clinical determination of blood oxygen saturation in a fetus |
US5853364A (en) * | 1995-08-07 | 1998-12-29 | Nellcor Puritan Bennett, Inc. | Method and apparatus for estimating physiological parameters using model-based adaptive filtering |
US6083172A (en) * | 1995-08-07 | 2000-07-04 | Nellcor Puritan Bennett Incorporated | Method and apparatus for estimating physiological parameters using model-based adaptive filtering |
US6411833B1 (en) * | 1995-08-07 | 2002-06-25 | Nellcor Puritan Bennett Incorporated | Method and apparatus for estimating physiological parameters using model-based adaptive filtering |
Cited By (208)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US8364226B2 (en) | 1991-03-07 | 2013-01-29 | Masimo Corporation | Signal processing apparatus |
US7469157B2 (en) | 1991-03-07 | 2008-12-23 | Masimo Corporation | Signal processing apparatus |
US8128572B2 (en) | 1991-03-07 | 2012-03-06 | Masimo Corporation | Signal processing apparatus |
US8046041B2 (en) | 1991-03-07 | 2011-10-25 | Masimo Corporation | Signal processing apparatus |
US8046042B2 (en) | 1991-03-07 | 2011-10-25 | Masimo Corporation | Signal processing apparatus |
US8036728B2 (en) | 1991-03-07 | 2011-10-11 | Masimo Corporation | Signal processing apparatus |
US7937130B2 (en) | 1991-03-07 | 2011-05-03 | Masimo Corporation | Signal processing apparatus |
US7254433B2 (en) | 1991-03-07 | 2007-08-07 | Masimo Corporation | Signal processing apparatus |
US7383070B2 (en) | 1991-03-07 | 2008-06-03 | Masimo Corporation | Signal processing apparatus |
US7496393B2 (en) | 1991-03-07 | 2009-02-24 | Masimo Corporation | Signal processing apparatus |
US7962190B1 (en) | 1991-03-07 | 2011-06-14 | Masimo Corporation | Signal processing apparatus |
US8948834B2 (en) | 1991-03-07 | 2015-02-03 | Masimo Corporation | Signal processing apparatus |
US8942777B2 (en) | 1991-03-07 | 2015-01-27 | Masimo Corporation | Signal processing apparatus |
US8560034B1 (en) | 1993-10-06 | 2013-10-15 | Masimo Corporation | Signal processing apparatus |
US8755856B2 (en) | 1994-10-07 | 2014-06-17 | Masimo Corporation | Signal processing apparatus |
US8463349B2 (en) | 1994-10-07 | 2013-06-11 | Masimo Corporation | Signal processing apparatus |
US8126528B2 (en) | 1994-10-07 | 2012-02-28 | Masimo Corporation | Signal processing apparatus |
US8359080B2 (en) | 1994-10-07 | 2013-01-22 | Masimo Corporation | Signal processing apparatus |
US20080033266A1 (en) * | 1994-10-07 | 2008-02-07 | Diab Mohamed K | Signal processing apparatus |
US8019400B2 (en) | 1994-10-07 | 2011-09-13 | Masimo Corporation | Signal processing apparatus |
US7865224B2 (en) | 1995-08-07 | 2011-01-04 | Nellcor Puritan Bennett Llc | Method and apparatus for estimating a physiological parameter |
US20050143634A1 (en) * | 1995-08-07 | 2005-06-30 | Nellcor Incorporated, A Delaware Corporation | Method and apparatus for estimating a physiological parameter |
US20050085735A1 (en) * | 1995-08-07 | 2005-04-21 | Nellcor Incorporated, A Delaware Corporation | Method and apparatus for estimating a physiological parameter |
US20110071375A1 (en) * | 1995-08-07 | 2011-03-24 | Nellcor Incorporated, A Delaware Corporation | Method and apparatus for estimating physiological parameters using model-based adaptive filtering |
US7931599B2 (en) | 1995-08-07 | 2011-04-26 | Nellcor Puritan Bennett Llc | Method and apparatus for estimating a physiological parameter |
US8649839B2 (en) | 1996-10-10 | 2014-02-11 | Covidien Lp | Motion compatible sensor for non-invasive optical blood analysis |
US7720516B2 (en) | 1996-10-10 | 2010-05-18 | Nellcor Puritan Bennett Llc | Motion compatible sensor for non-invasive optical blood analysis |
US9468378B2 (en) | 1997-01-27 | 2016-10-18 | Lawrence A. Lynn | Airway instability detection system and method |
US9042952B2 (en) | 1997-01-27 | 2015-05-26 | Lawrence A. Lynn | System and method for automatic detection of a plurality of SPO2 time series pattern types |
US8190227B2 (en) | 1997-04-14 | 2012-05-29 | Masimo Corporation | Signal processing apparatus and method |
US8180420B2 (en) | 1997-04-14 | 2012-05-15 | Masimo Corporation | Signal processing apparatus and method |
US8888708B2 (en) | 1997-04-14 | 2014-11-18 | Masimo Corporation | Signal processing apparatus and method |
US9289167B2 (en) | 1997-04-14 | 2016-03-22 | Masimo Corporation | Signal processing apparatus and method |
US9521971B2 (en) | 1997-07-14 | 2016-12-20 | Lawrence A. Lynn | System and method for automatic detection of a plurality of SPO2 time series pattern types |
US8133176B2 (en) | 1999-04-14 | 2012-03-13 | Tyco Healthcare Group Lp | Method and circuit for indicating quality and accuracy of physiological measurements |
US20070000494A1 (en) * | 1999-06-30 | 2007-01-04 | Banner Michael J | Ventilator monitor system and method of using same |
US7689259B2 (en) | 2000-04-17 | 2010-03-30 | Nellcor Puritan Bennett Llc | Pulse oximeter sensor with piece-wise function |
US8224412B2 (en) | 2000-04-17 | 2012-07-17 | Nellcor Puritan Bennett Llc | Pulse oximeter sensor with piece-wise function |
US8078246B2 (en) | 2000-04-17 | 2011-12-13 | Nellcor Puritan Bennett Llc | Pulse oximeter sensor with piece-wise function |
US8932227B2 (en) | 2000-07-28 | 2015-01-13 | Lawrence A. Lynn | System and method for CO2 and oximetry integration |
US10058269B2 (en) | 2000-07-28 | 2018-08-28 | Lawrence A. Lynn | Monitoring system for identifying an end-exhalation carbon dioxide value of enhanced clinical utility |
US10032526B2 (en) | 2001-05-17 | 2018-07-24 | Lawrence A. Lynn | Patient safety processor |
US11439321B2 (en) | 2001-05-17 | 2022-09-13 | Lawrence A. Lynn | Monitoring system for identifying an end-exhalation carbon dioxide value of enhanced clinical utility |
US8862196B2 (en) | 2001-05-17 | 2014-10-14 | Lawrence A. Lynn | System and method for automatic detection of a plurality of SP02 time series pattern types |
US10366790B2 (en) | 2001-05-17 | 2019-07-30 | Lawrence A. Lynn | Patient safety processor |
US10354753B2 (en) | 2001-05-17 | 2019-07-16 | Lawrence A. Lynn | Medical failure pattern search engine |
US9031793B2 (en) | 2001-05-17 | 2015-05-12 | Lawrence A. Lynn | Centralized hospital monitoring system for automatically detecting upper airway instability and for preventing and aborting adverse drug reactions |
US10297348B2 (en) | 2001-05-17 | 2019-05-21 | Lawrence A. Lynn | Patient safety processor |
US8666467B2 (en) | 2001-05-17 | 2014-03-04 | Lawrence A. Lynn | System and method for SPO2 instability detection and quantification |
US8433383B2 (en) | 2001-10-12 | 2013-04-30 | Covidien Lp | Stacked adhesive optical sensor |
US9053222B2 (en) | 2002-05-17 | 2015-06-09 | Lawrence A. Lynn | Patient safety processor |
US9668661B2 (en) | 2002-06-20 | 2017-06-06 | University Of Florida Research Foundation, Inc. | Devices, systems and methods for plethysmographic monitoring at the nose |
US9198586B2 (en) | 2002-06-20 | 2015-12-01 | University Of Florida Research Foundation, Inc. | Methods of monitoring oxygenation by positive end expiratory pressure using photoplethysmography |
US8483790B2 (en) | 2002-10-18 | 2013-07-09 | Covidien Lp | Non-adhesive oximeter sensor for sensitive skin |
US8983800B2 (en) | 2003-01-13 | 2015-03-17 | Covidien Lp | Selection of preset filter parameters based on signal quality |
US20040186804A1 (en) * | 2003-03-19 | 2004-09-23 | Anindya Chakraborty | Methods and systems for analytical-based multifactor multiobjective portfolio risk optimization |
US7640201B2 (en) | 2003-03-19 | 2009-12-29 | General Electric Company | Methods and systems for analytical-based multifactor Multiobjective portfolio risk optimization |
US20040186814A1 (en) * | 2003-03-19 | 2004-09-23 | Chalermkraivuth Kete Charles | Methods and systems for analytical-based multifactor multiobjective portfolio risk optimization |
US7593880B2 (en) | 2003-03-19 | 2009-09-22 | General Electric Company | Methods and systems for analytical-based multifactor multiobjective portfolio risk optimization |
US7542932B2 (en) | 2004-02-20 | 2009-06-02 | General Electric Company | Systems and methods for multi-objective portfolio optimization |
US20050187847A1 (en) * | 2004-02-20 | 2005-08-25 | Bonissone Piero P. | Systems and methods for multi-objective portfolio analysis and decision-making using visualization techniques |
US8126795B2 (en) | 2004-02-20 | 2012-02-28 | General Electric Company | Systems and methods for initial sampling in multi-objective portfolio analysis |
US7469228B2 (en) | 2004-02-20 | 2008-12-23 | General Electric Company | Systems and methods for efficient frontier supplementation in multi-objective portfolio analysis |
US20050187848A1 (en) * | 2004-02-20 | 2005-08-25 | Bonissone Piero P. | Systems and methods for efficient frontier supplementation in multi-objective portfolio analysis |
US20050187849A1 (en) * | 2004-02-20 | 2005-08-25 | Srinivas Bollapragada | Systems and methods for initial sampling in multi-objective portfolio analysis |
US20050187846A1 (en) * | 2004-02-20 | 2005-08-25 | Subbu Rajesh V. | Systems and methods for multi-objective portfolio analysis using pareto sorting evolutionary algorithms |
US8219477B2 (en) | 2004-02-20 | 2012-07-10 | General Electric Company | Systems and methods for multi-objective portfolio analysis using pareto sorting evolutionary algorithms |
US7630928B2 (en) | 2004-02-20 | 2009-12-08 | General Electric Company | Systems and methods for multi-objective portfolio analysis and decision-making using visualization techniques |
US20050187844A1 (en) * | 2004-02-20 | 2005-08-25 | Kete Charles Chalermkraivuth | Systems and methods for multi-objective portfolio optimization |
US20070132618A1 (en) * | 2004-02-25 | 2007-06-14 | Nellcor Puritan Bennett Inc. | Multi-bit ADC with sigma-delta modulation |
US7190985B2 (en) * | 2004-02-25 | 2007-03-13 | Nellcor Puritan Bennett Inc. | Oximeter ambient light cancellation |
US20050184895A1 (en) * | 2004-02-25 | 2005-08-25 | Nellcor Puritan Bennett Inc. | Multi-bit ADC with sigma-delta modulation |
US7142142B2 (en) * | 2004-02-25 | 2006-11-28 | Nelicor Puritan Bennett, Inc. | Multi-bit ADC with sigma-delta modulation |
US8874181B2 (en) | 2004-02-25 | 2014-10-28 | Covidien Lp | Oximeter ambient light cancellation |
US7355539B2 (en) | 2004-02-25 | 2008-04-08 | Nellcor Puritan Bennett Inc. | Multi-bit ADC with sigma-delta modulation |
US20050187448A1 (en) * | 2004-02-25 | 2005-08-25 | Nellcor Puritan Bennett Inc. | Oximeter ambient light cancellation |
US7534212B2 (en) * | 2004-03-08 | 2009-05-19 | Nellcor Puritan Bennett Llc | Pulse oximeter with alternate heart-rate determination |
US20050197552A1 (en) * | 2004-03-08 | 2005-09-08 | Nellcor Puritan Bennett Incorporated | Pulse oximeter with alternate heart-rate determination |
US8007441B2 (en) * | 2004-03-08 | 2011-08-30 | Nellcor Puritan Bennett Llc | Pulse oximeter with alternate heart-rate determination |
US20050215919A1 (en) * | 2004-03-27 | 2005-09-29 | Samsung Electronics Co., Ltd. | Apparatus and method for simultaneously measuring bio signals |
US7758513B2 (en) * | 2004-03-27 | 2010-07-20 | Samsung Electronics Co., Ltd. | Apparatus and method for simultaneously measuring bio signals |
US20100249537A1 (en) * | 2004-03-27 | 2010-09-30 | Samsung Electronics Co., Ltd. | Apparatus and method for simultaneously measuring bio signals |
US8043226B2 (en) | 2004-03-27 | 2011-10-25 | Samsung Electronics Co., Ltd. | Apparatus and method for simultaneously measuring bio signals |
US8679028B2 (en) | 2004-08-11 | 2014-03-25 | University Of Florida Research Foundation, Inc. | Methods and devices for countering grativity induced loss of consciousness and novel pulse oximeter probes |
US20080058621A1 (en) * | 2004-08-11 | 2008-03-06 | Melker Richard J | Methods and Devices for Countering Grativity Induced Loss of Consciousness and Novel Pulse Oximeter Probes |
US10368758B2 (en) | 2004-08-11 | 2019-08-06 | University Of Florida Research Foundation, Inc. | Methods and devices for central photoplethysmographic monitoring |
US8772693B2 (en) * | 2005-03-25 | 2014-07-08 | Massachusetts Institute Of Technology | System and method for Hilbert phase imaging |
US20060291712A1 (en) * | 2005-03-25 | 2006-12-28 | Gabriel Popescu | System and method for Hilbert phase imaging |
US10256262B2 (en) | 2005-03-25 | 2019-04-09 | Massachusetts Institute Of Technology | System and method for Hilbert phase imaging |
US7785262B2 (en) * | 2005-04-25 | 2010-08-31 | University Of Florida Research Foundation, Inc. | Method and apparatus for diagnosing respiratory disorders and determining the degree of exacerbations |
US20060241506A1 (en) * | 2005-04-25 | 2006-10-26 | Melker Richard J | Method and apparatus for diagnosing respiratory disorders and determining the degree of exacerbations |
US7684843B2 (en) | 2005-08-08 | 2010-03-23 | Nellcor Puritan Bennett Llc | Medical sensor and technique for using the same |
US7647084B2 (en) | 2005-08-08 | 2010-01-12 | Nellcor Puritan Bennett Llc | Medical sensor and technique for using the same |
US8528185B2 (en) | 2005-08-08 | 2013-09-10 | Covidien Lp | Bi-stable medical sensor and technique for using the same |
US7738937B2 (en) | 2005-08-08 | 2010-06-15 | Nellcor Puritan Bennett Llc | Medical sensor and technique for using the same |
US7693559B2 (en) | 2005-08-08 | 2010-04-06 | Nellcor Puritan Bennett Llc | Medical sensor having a deformable region and technique for using the same |
US8311602B2 (en) | 2005-08-08 | 2012-11-13 | Nellcor Puritan Bennett Llc | Compliant diaphragm medical sensor and technique for using the same |
US7657294B2 (en) | 2005-08-08 | 2010-02-02 | Nellcor Puritan Bennett Llc | Compliant diaphragm medical sensor and technique for using the same |
US7657295B2 (en) | 2005-08-08 | 2010-02-02 | Nellcor Puritan Bennett Llc | Medical sensor and technique for using the same |
US7657296B2 (en) | 2005-08-08 | 2010-02-02 | Nellcor Puritan Bennett Llc | Unitary medical sensor assembly and technique for using the same |
US8260391B2 (en) | 2005-09-12 | 2012-09-04 | Nellcor Puritan Bennett Llc | Medical sensor for reducing motion artifacts and technique for using the same |
US8060171B2 (en) | 2005-09-29 | 2011-11-15 | Nellcor Puritan Bennett Llc | Medical sensor for reducing motion artifacts and technique for using the same |
US7904130B2 (en) | 2005-09-29 | 2011-03-08 | Nellcor Puritan Bennett Llc | Medical sensor and technique for using the same |
US8965473B2 (en) | 2005-09-29 | 2015-02-24 | Covidien Lp | Medical sensor for reducing motion artifacts and technique for using the same |
US8092379B2 (en) | 2005-09-29 | 2012-01-10 | Nellcor Puritan Bennett Llc | Method and system for determining when to reposition a physiological sensor |
US8600469B2 (en) | 2005-09-29 | 2013-12-03 | Covidien Lp | Medical sensor and technique for using the same |
US7869850B2 (en) | 2005-09-29 | 2011-01-11 | Nellcor Puritan Bennett Llc | Medical sensor for reducing motion artifacts and technique for using the same |
US7729736B2 (en) | 2005-09-29 | 2010-06-01 | Nellcor Puritan Bennett Llc | Medical sensor and technique for using the same |
US7650177B2 (en) | 2005-09-29 | 2010-01-19 | Nellcor Puritan Bennett Llc | Medical sensor for reducing motion artifacts and technique for using the same |
US7899510B2 (en) | 2005-09-29 | 2011-03-01 | Nellcor Puritan Bennett Llc | Medical sensor and technique for using the same |
US7676253B2 (en) | 2005-09-29 | 2010-03-09 | Nellcor Puritan Bennett Llc | Medical sensor and technique for using the same |
US8352009B2 (en) | 2005-09-30 | 2013-01-08 | Covidien Lp | Medical sensor and technique for using the same |
US8062221B2 (en) | 2005-09-30 | 2011-11-22 | Nellcor Puritan Bennett Llc | Sensor for tissue gas detection and technique for using the same |
US8233954B2 (en) | 2005-09-30 | 2012-07-31 | Nellcor Puritan Bennett Llc | Mucosal sensor for the assessment of tissue and blood constituents and technique for using the same |
US8352010B2 (en) | 2005-09-30 | 2013-01-08 | Covidien Lp | Folding medical sensor and technique for using the same |
US8386002B2 (en) | 2005-09-30 | 2013-02-26 | Covidien Lp | Optically aligned pulse oximetry sensor and technique for using the same |
US7881762B2 (en) | 2005-09-30 | 2011-02-01 | Nellcor Puritan Bennett Llc | Clip-style medical sensor and technique for using the same |
US20070078354A1 (en) * | 2005-10-04 | 2007-04-05 | Welch Allyn, Inc. | Method and apparatus for removing baseline wander from an ECG signal |
US20070083094A1 (en) * | 2005-10-11 | 2007-04-12 | Colburn Joel C | Medical sensor and technique for using the same |
US7811276B2 (en) | 2005-11-10 | 2010-10-12 | Nellcor Puritan Bennett Llc | Medical sensor and technique for using the same |
US7668579B2 (en) | 2006-02-10 | 2010-02-23 | Lynn Lawrence A | System and method for the detection of physiologic response to stimulation |
US8728001B2 (en) | 2006-02-10 | 2014-05-20 | Lawrence A. Lynn | Nasal capnographic pressure monitoring system |
US8437826B2 (en) | 2006-05-02 | 2013-05-07 | Covidien Lp | Clip-style medical sensor and technique for using the same |
US8073518B2 (en) | 2006-05-02 | 2011-12-06 | Nellcor Puritan Bennett Llc | Clip-style medical sensor and technique for using the same |
US8641635B2 (en) | 2006-08-15 | 2014-02-04 | University Of Florida Research Foundation, Inc. | Methods and devices for central photoplethysmographic monitoring methods |
US20100192952A1 (en) * | 2006-08-15 | 2010-08-05 | Melker Richard J | Methods and Devices for Central Photoplethysmographic Monitoring Methods |
US8577436B2 (en) | 2006-08-22 | 2013-11-05 | Covidien Lp | Medical sensor for reducing signal artifacts and technique for using the same |
US8145288B2 (en) | 2006-08-22 | 2012-03-27 | Nellcor Puritan Bennett Llc | Medical sensor for reducing signal artifacts and technique for using the same |
US8219170B2 (en) | 2006-09-20 | 2012-07-10 | Nellcor Puritan Bennett Llc | System and method for practicing spectrophotometry using light emitting nanostructure devices |
US8190224B2 (en) | 2006-09-22 | 2012-05-29 | Nellcor Puritan Bennett Llc | Medical sensor for reducing signal artifacts and technique for using the same |
US8190225B2 (en) | 2006-09-22 | 2012-05-29 | Nellcor Puritan Bennett Llc | Medical sensor for reducing signal artifacts and technique for using the same |
US8195264B2 (en) | 2006-09-22 | 2012-06-05 | Nellcor Puritan Bennett Llc | Medical sensor for reducing signal artifacts and technique for using the same |
US8396527B2 (en) | 2006-09-22 | 2013-03-12 | Covidien Lp | Medical sensor for reducing signal artifacts and technique for using the same |
US8175671B2 (en) | 2006-09-22 | 2012-05-08 | Nellcor Puritan Bennett Llc | Medical sensor for reducing signal artifacts and technique for using the same |
US20080083265A1 (en) * | 2006-09-25 | 2008-04-10 | Rafael Ostrowski | Carbon dioxide detector having borosilicate substrate |
US8420405B2 (en) | 2006-09-25 | 2013-04-16 | Covidien Lp | Carbon dioxide detector having borosilicate substrate |
US20080081003A1 (en) * | 2006-09-25 | 2008-04-03 | Rafael Ostrowski | Carbon dioxide detector having borosilicate substrate |
US8431087B2 (en) | 2006-09-25 | 2013-04-30 | Covidien Lp | Carbon dioxide detector having borosilicate substrate |
US20080078394A1 (en) * | 2006-09-25 | 2008-04-03 | Rafael Ostrowski | Carbon dioxide detector having borosilicate substrate |
US8431088B2 (en) | 2006-09-25 | 2013-04-30 | Covidien Lp | Carbon dioxide detector having borosilicate substrate |
US8449834B2 (en) | 2006-09-25 | 2013-05-28 | Covidien Lp | Carbon dioxide detector having borosilicate substrate |
US20080075633A1 (en) * | 2006-09-25 | 2008-03-27 | Rafael Ostrowski | Carbon dioxide detector having borosilicate substrate |
US7869849B2 (en) | 2006-09-26 | 2011-01-11 | Nellcor Puritan Bennett Llc | Opaque, electrically nonconductive region on a medical sensor |
US8315685B2 (en) | 2006-09-27 | 2012-11-20 | Nellcor Puritan Bennett Llc | Flexible medical sensor enclosure |
US8660626B2 (en) | 2006-09-28 | 2014-02-25 | Covidien Lp | System and method for mitigating interference in pulse oximetry |
US20080082009A1 (en) * | 2006-09-28 | 2008-04-03 | Nellcor Puritan Bennett Inc. | System and method for pulse rate calculation using a scheme for alternate weighting |
US10022058B2 (en) | 2006-09-28 | 2018-07-17 | Covidien Lp | System and method for pulse rate calculation using a scheme for alternate weighting |
US7922665B2 (en) | 2006-09-28 | 2011-04-12 | Nellcor Puritan Bennett Llc | System and method for pulse rate calculation using a scheme for alternate weighting |
US7890153B2 (en) | 2006-09-28 | 2011-02-15 | Nellcor Puritan Bennett Llc | System and method for mitigating interference in pulse oximetry |
US8801622B2 (en) | 2006-09-28 | 2014-08-12 | Covidien Lp | System and method for pulse rate calculation using a scheme for alternate weighting |
US7796403B2 (en) | 2006-09-28 | 2010-09-14 | Nellcor Puritan Bennett Llc | Means for mechanical registration and mechanical-electrical coupling of a faraday shield to a photodetector and an electrical circuit |
US8068891B2 (en) | 2006-09-29 | 2011-11-29 | Nellcor Puritan Bennett Llc | Symmetric LED array for pulse oximetry |
US7658652B2 (en) | 2006-09-29 | 2010-02-09 | Nellcor Puritan Bennett Llc | Device and method for reducing crosstalk |
US7680522B2 (en) | 2006-09-29 | 2010-03-16 | Nellcor Puritan Bennett Llc | Method and apparatus for detecting misapplied sensors |
US7684842B2 (en) | 2006-09-29 | 2010-03-23 | Nellcor Puritan Bennett Llc | System and method for preventing sensor misuse |
US7794266B2 (en) | 2006-09-29 | 2010-09-14 | Nellcor Puritan Bennett Llc | Device and method for reducing crosstalk |
US7848891B2 (en) | 2006-09-29 | 2010-12-07 | Nellcor Puritan Bennett Llc | Modulation ratio determination with accommodation of uncertainty |
US8175667B2 (en) | 2006-09-29 | 2012-05-08 | Nellcor Puritan Bennett Llc | Symmetric LED array for pulse oximetry |
US20080081325A1 (en) * | 2006-09-29 | 2008-04-03 | Nellcor Puritan Bennett Inc. | Modulation ratio determination with accommodation of uncertainty |
US8265724B2 (en) | 2007-03-09 | 2012-09-11 | Nellcor Puritan Bennett Llc | Cancellation of light shunting |
US8280469B2 (en) | 2007-03-09 | 2012-10-02 | Nellcor Puritan Bennett Llc | Method for detection of aberrant tissue spectra |
US7894869B2 (en) | 2007-03-09 | 2011-02-22 | Nellcor Puritan Bennett Llc | Multiple configuration medical sensor and technique for using the same |
US20090043179A1 (en) * | 2007-08-08 | 2009-02-12 | Melker Richard J | Processing of Photoplethysmography Signals |
US8529459B2 (en) | 2007-08-08 | 2013-09-10 | Convergent Engineering, Inc. | Processing of photoplethysmography signals |
US8346328B2 (en) | 2007-12-21 | 2013-01-01 | Covidien Lp | Medical sensor and technique for using the same |
US8352004B2 (en) | 2007-12-21 | 2013-01-08 | Covidien Lp | Medical sensor and technique for using the same |
US8366613B2 (en) | 2007-12-26 | 2013-02-05 | Covidien Lp | LED drive circuit for pulse oximetry and method for using same |
US8577434B2 (en) | 2007-12-27 | 2013-11-05 | Covidien Lp | Coaxial LED light sources |
US8452364B2 (en) | 2007-12-28 | 2013-05-28 | Covidien LLP | System and method for attaching a sensor to a patient's skin |
US8442608B2 (en) | 2007-12-28 | 2013-05-14 | Covidien Lp | System and method for estimating physiological parameters by deconvolving artifacts |
US8070508B2 (en) | 2007-12-31 | 2011-12-06 | Nellcor Puritan Bennett Llc | Method and apparatus for aligning and securing a cable strain relief |
US8897850B2 (en) | 2007-12-31 | 2014-11-25 | Covidien Lp | Sensor with integrated living hinge and spring |
US8092993B2 (en) | 2007-12-31 | 2012-01-10 | Nellcor Puritan Bennett Llc | Hydrogel thin film for use as a biosensor |
US8199007B2 (en) | 2007-12-31 | 2012-06-12 | Nellcor Puritan Bennett Llc | Flex circuit snap track for a biometric sensor |
US8437822B2 (en) | 2008-03-28 | 2013-05-07 | Covidien Lp | System and method for estimating blood analyte concentration |
US8112375B2 (en) | 2008-03-31 | 2012-02-07 | Nellcor Puritan Bennett Llc | Wavelength selection and outlier detection in reduced rank linear models |
US7880884B2 (en) | 2008-06-30 | 2011-02-01 | Nellcor Puritan Bennett Llc | System and method for coating and shielding electronic sensor components |
US8071935B2 (en) | 2008-06-30 | 2011-12-06 | Nellcor Puritan Bennett Llc | Optical detector with an overmolded faraday shield |
US7887345B2 (en) | 2008-06-30 | 2011-02-15 | Nellcor Puritan Bennett Llc | Single use connector for pulse oximetry sensors |
US8364220B2 (en) | 2008-09-25 | 2013-01-29 | Covidien Lp | Medical sensor and technique for using the same |
US8914088B2 (en) | 2008-09-30 | 2014-12-16 | Covidien Lp | Medical sensor and technique for using the same |
US8423112B2 (en) | 2008-09-30 | 2013-04-16 | Covidien Lp | Medical sensor and technique for using the same |
US8417309B2 (en) | 2008-09-30 | 2013-04-09 | Covidien Lp | Medical sensor |
US8452366B2 (en) | 2009-03-16 | 2013-05-28 | Covidien Lp | Medical monitoring device with flexible circuitry |
US8221319B2 (en) | 2009-03-25 | 2012-07-17 | Nellcor Puritan Bennett Llc | Medical device for assessing intravascular blood volume and technique for using the same |
US8509869B2 (en) | 2009-05-15 | 2013-08-13 | Covidien Lp | Method and apparatus for detecting and analyzing variations in a physiologic parameter |
US8634891B2 (en) | 2009-05-20 | 2014-01-21 | Covidien Lp | Method and system for self regulation of sensor component contact pressure |
US8290730B2 (en) | 2009-06-30 | 2012-10-16 | Nellcor Puritan Bennett Ireland | Systems and methods for assessing measurements in physiological monitoring devices |
US8311601B2 (en) | 2009-06-30 | 2012-11-13 | Nellcor Puritan Bennett Llc | Reflectance and/or transmissive pulse oximeter |
US8505821B2 (en) | 2009-06-30 | 2013-08-13 | Covidien Lp | System and method for providing sensor quality assurance |
US9010634B2 (en) | 2009-06-30 | 2015-04-21 | Covidien Lp | System and method for linking patient data to a patient and providing sensor quality assurance |
US8636667B2 (en) | 2009-07-06 | 2014-01-28 | Nellcor Puritan Bennett Ireland | Systems and methods for processing physiological signals in wavelet space |
US8391941B2 (en) | 2009-07-17 | 2013-03-05 | Covidien Lp | System and method for memory switching for multiple configuration medical sensor |
US8417310B2 (en) | 2009-08-10 | 2013-04-09 | Covidien Lp | Digital switching in multi-site sensor |
US8428675B2 (en) | 2009-08-19 | 2013-04-23 | Covidien Lp | Nanofiber adhesives used in medical devices |
US20220330842A1 (en) * | 2010-07-22 | 2022-10-20 | Masimo Corporation | Non-invasive blood pressure measurement system |
US9950112B2 (en) | 2010-08-17 | 2018-04-24 | University Of Florida Research Foundation, Incorporated | Intelligent drug and/or fluid delivery system to optimizing medical treatment or therapy using pharmacodynamic and/or pharamacokinetic data |
US8649838B2 (en) | 2010-09-22 | 2014-02-11 | Covidien Lp | Wavelength switching for pulse oximetry |
US10159412B2 (en) | 2010-12-01 | 2018-12-25 | Cercacor Laboratories, Inc. | Handheld processing device including medical applications for minimally and non invasive glucose measurements |
US20120157857A1 (en) * | 2010-12-15 | 2012-06-21 | Sony Corporation | Respiratory signal processing apparatus, respiratory signal processing method, and program |
US11723579B2 (en) | 2017-09-19 | 2023-08-15 | Neuroenhancement Lab, LLC | Method and apparatus for neuroenhancement |
US11717686B2 (en) | 2017-12-04 | 2023-08-08 | Neuroenhancement Lab, LLC | Method and apparatus for neuroenhancement to facilitate learning and performance |
US11273283B2 (en) | 2017-12-31 | 2022-03-15 | Neuroenhancement Lab, LLC | Method and apparatus for neuroenhancement to enhance emotional response |
US11478603B2 (en) | 2017-12-31 | 2022-10-25 | Neuroenhancement Lab, LLC | Method and apparatus for neuroenhancement to enhance emotional response |
US11318277B2 (en) | 2017-12-31 | 2022-05-03 | Neuroenhancement Lab, LLC | Method and apparatus for neuroenhancement to enhance emotional response |
US11364361B2 (en) | 2018-04-20 | 2022-06-21 | Neuroenhancement Lab, LLC | System and method for inducing sleep by transplanting mental states |
US11452839B2 (en) | 2018-09-14 | 2022-09-27 | Neuroenhancement Lab, LLC | System and method of improving sleep |
US11786694B2 (en) | 2019-05-24 | 2023-10-17 | NeuroLight, Inc. | Device, method, and app for facilitating sleep |
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