WO2004066161A1 - Selection of preset filter parameters based on signal quality - Google Patents
Selection of preset filter parameters based on signal quality Download PDFInfo
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- WO2004066161A1 WO2004066161A1 PCT/US2003/041467 US0341467W WO2004066161A1 WO 2004066161 A1 WO2004066161 A1 WO 2004066161A1 US 0341467 W US0341467 W US 0341467W WO 2004066161 A1 WO2004066161 A1 WO 2004066161A1
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- signal
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- 238000000034 method Methods 0.000 claims abstract description 31
- 238000001914 filtration Methods 0.000 claims abstract description 14
- 230000000694 effects Effects 0.000 claims abstract description 11
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 18
- 229910052760 oxygen Inorganic materials 0.000 claims description 18
- 239000001301 oxygen Substances 0.000 claims description 18
- 230000003287 optical effect Effects 0.000 claims description 5
- 230000003595 spectral effect Effects 0.000 claims description 4
- 230000003044 adaptive effect Effects 0.000 description 11
- 238000004422 calculation algorithm Methods 0.000 description 11
- 238000002106 pulse oximetry Methods 0.000 description 8
- 239000008280 blood Substances 0.000 description 6
- 210000004369 blood Anatomy 0.000 description 6
- 238000013459 approach Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 5
- 238000012545 processing Methods 0.000 description 4
- 238000013442 quality metrics Methods 0.000 description 4
- 238000005259 measurement Methods 0.000 description 3
- 102000001554 Hemoglobins Human genes 0.000 description 2
- 108010054147 Hemoglobins Proteins 0.000 description 2
- 230000036772 blood pressure Effects 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 238000009499 grossing Methods 0.000 description 2
- 238000001228 spectrum Methods 0.000 description 2
- 241001465754 Metazoa Species 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 230000001143 conditioned effect Effects 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 239000004744 fabric Substances 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000001727 in vivo Methods 0.000 description 1
- 238000012886 linear function Methods 0.000 description 1
- 238000002496 oximetry Methods 0.000 description 1
- 230000000541 pulsatile effect Effects 0.000 description 1
- 238000001303 quality assessment method Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000012549 training Methods 0.000 description 1
Classifications
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
- A61B5/1455—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using optical sensors, e.g. spectral photometrical oximeters
- A61B5/14551—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using optical sensors, e.g. spectral photometrical oximeters for measuring blood gases
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
- A61B5/1455—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using optical sensors, e.g. spectral photometrical oximeters
- A61B5/14551—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using optical sensors, e.g. spectral photometrical oximeters for measuring blood gases
- A61B5/14552—Details of sensors specially adapted therefor
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7221—Determining signal validity, reliability or quality
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/725—Details of waveform analysis using specific filters therefor, e.g. Kalman or adaptive filters
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/01—Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/021—Measuring pressure in heart or blood vessels
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/024—Detecting, measuring or recording pulse rate or heart rate
- A61B5/02416—Detecting, measuring or recording pulse rate or heart rate using photoplethysmograph signals, e.g. generated by infrared radiation
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7239—Details of waveform analysis using differentiation including higher order derivatives
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/02—Preprocessing
Definitions
- the present invention relates to the processing of signals obtained from a medical diagnostic apparatus, such as a pulse oximeter, using a digital filter to reduce noise effects.
- a typical pulse oximeter measures two physiological parameters, percent oxygen saturation of arterial blood hemoglobin (SpO 2 or sat) and pulse rate. Oxygen saturation can be estimated using various techniques. In one common technique, the photocurrent generated by the photo-detector is conditioned and processed to determine the ratio of modulation ratios (ratio of ratios) of the red to infrared signals. This modulation ratio has been observed to correlate well to arterial oxygen saturation.
- the pulse oximeters and sensors are empirically calibrated by measuring the modulation ratio over a range of in vivo measured arterial oxygen saturations (SaO 2 ) on a set of patients, healthy volunteers, or animals.
- the observed correlation is used in an inverse manner to estimate blood oxygen saturation (SpO 2 ) based on the measured value of modulation ratios of a patient.
- the estimation of oxygen saturation using modulation ratios is described in U.S. Patent No. 5,853,364, entitled "METHOD AND APPARATUS FOR ESTIMATING PHYSIOLOGICAL PARAMETERS USING MODEL-BASED ADAPTINE FILTERING," issued December 29, 1998, and U.S. Patent No.
- the adaptive filter is actually a data processing algorithm, and in most typical applications, the filter is a computer program that is executed by a central processor. As such, the filter inherently incorporates discrete-time measurement samples rather than continuous time inputs.
- a type of digital filter that is used in pulse oximeter systems is a Kalman filter. While conventional adaptive digital filters in general and Kalman filters in particular have been assimilated in medical diagnostics system to help-reduce noise in a signal, there are still many challenges that need to be addressed to improve the techniques that are used to reduce noise effects in signals; noise effects such as those present in a medical diagnostic device.
- a Kalman filter is an adaptive filter whose functioning is mathematically-based and where its aim is to compare the output of the filter with a desired output, and reduce the error in the comparison by continuously varying the filter's coefficients. So, a Kalman filter generates filter coefficients in an adaptive manner to minimize an error. While this method has been adopted by many, it is still a method that is somewhat blind regarding the signal that it is being filtered. Such an approach does not take into account the unique attributes that an input signal may possess and which are physiologically based. Another shortcoming of the Kalman filtering is that the Kalman filter is linear in its input-output relationship. One can appreciate that in certain conditions, the requirement that the filter be linear in its input- output relationship is too constraining. Yet another shortcoming of a Kalman filter is that filter parameters are continuously tuned, which can be computationally expensive.
- the present invention is directed towards methods and devices for reducing noise effects in a system for measuring a physiological parameter, including receiving an input signal; obtaining an assessment of the signal quality of the input signal; selecting coefficients for a digital filter using the assessment of signal quality; and filtering the input signal using the digital filter, without comparing the filter's output signal with the input signal.
- the filter coefficients are selected from a plurality of discrete preset values.
- the discrete and preset values are fixed or non-changing values.
- the digital filter can have either a linear or preferably a non-linear input-output relationship.
- the quality of the input signal may be assessed by obtaining or measuring signal parameters that include the skew of the time derivative of the signal; the correlation between signals from different wavelengths; the variation in signal amplitude, as well as others.
- Other assessments such as maximum values or spectral peak frequencies, may also be used in determining filter parameters.
- the selection of filter parameters or coefficients is performed in real time, with the coefficients of the digital filter being determined using a current input sample. In certain other embodiments, the selection of filter parameters is performed using a previously stored input signal sample.
- the input signals can be a function of an oxygen saturation, or a pulse rate. Furthermore, these signals correspond with sensed optical energies from a plurality of wavelengths. [0011]
- FIG. 1 is a block diagram of an exemplary oximeter.
- Fig. 2 is a block diagram depicting the operation of the signal-quality- based filter operation in accordance with embodiments of the present invention.
- the methods and systems in accordance with embodiments of the present invention are directed towards selecting and adjusting the parameters of a digital filter based an assessment of the quality of the input signals to the filter.
- the invention is particularly applicable to and will be explained by reference to measurements of oxygen saturation of hemoglobin in arterial blood and patient heart rate, as in pulse oximeter monitors and pulse oximetry sensors.
- the invention is equally applicable to any generalized patient monitor and associated patient sensor, such as ECG, blood pressure, temperature, etc., and is not to be limited for use only with oximetry or pulse oximetry.
- Fig. 1 is a block diagram of one embodiment of a pulse oximeter that may be configured to implement the embodiments of present invention.
- The, filter embodiments of the present invention can be a data processing algorithm that is executed by the microprocessor 122, described below.
- Light from light source 110 passes into patient tissue 112, and is scattered and detected by photodetector 114.
- a sensor 100 containing the light source and photodetector may also contain an encoder 116 which provides signals indicative of the wavelength of light source 110 to allow the oximeter to select appropriate calibration coefficients for calculating oxygen saturation.
- Encoder 116 may, for instance, be a resistor.
- Sensor 100 is connected to a pulse oximeter 120.
- the oximeter includes a microprocessor 122 connected to an internal bus 124. Also connected to the bus is a RAM memory 126 and a display 128.
- a time processing unit (TPU) 130 provides timing control signals to light drive circuitry 132 which controls when light source 110 is illuminated, and if multiple light sources are used, the multiplexed timing for the different light sources.
- TPU 130 also controls the gating-in of signals from photodetector 114 through an amplifier 133 and a switching circuit 134. These signals are sampled at the proper time, depending upon which of multiple light sources is illuminated, if multiple light sources are used.
- the received signal is passed through an amplifier 136, a low pass filter 138, and an analog-to-digital converter 140.
- QSM queued serial module
- microprocessor 122 Based on the value of the received signals corresponding to the light received by photodetector 114, microprocessor 122 will calculate the oxygen saturation using various algorithms. These algorithms require coefficients, which may be empirically determined, corresponding to, for example, the wavelengths of light used. These are stored in a ROM 146. In a two-wavelength system, the particular set of coefficients chosen for any pair of wavelength spectrums is determined by the value indicated by encoder 116 corresponding to a particular light source in a particular sensor 100. In one embodiment, multiple resistor values may be assigned to select different sets of coefficients. In another embodiment, the same resistors are used to select from among the coefficients appropriate for an infrared source paired with either a near red source or far red source.
- Control inputs 154 may be, for instance, a switch on the pulse oximeter, a keyboard, or a port providing instructions from a remote host computer. Furthermore, any number of methods or algorithms may be used to determine a patient's pulse rate, oxygen saturation or any other desired physiological parameter.
- a signal quality indicator is a measured parameter that is capable of estimating the reliability and accuracy of a signal. For example, when measuring blood oxygen saturation using a pulse oximeter, a signal quality indicator is able to indirectly assess whether an estimate of a value of blood oxygen saturation is an accurate one. This determination of accuracy is made possible by a thorough and detailed study of volumes of measured values and various indicators to determine which indicators are indicative of signal quality and what, if any, is the correlation between the indicator and the accuracy of the estimated value.
- examples of signal quality indicators include the skew of the time derivative of the signal; the correlation between signals from different wavelengths; the variation in signal amplitude, as well as others. Other assessments, such as maximum values or spectral peak frequencies, may also be used in determining filter parameters. In addition to these signal quality indicators, other signal quality indicators may also be used for the selection of filter coefficients.
- these additional signal quality indicators include: a signal measure indicative of the degree of similarity of an infrared and red waveforms; a signal measure indicative of a low light level; a signal measure indicative of an arterial pulse shape; a signal measure indicative of the high frequency signal component in the measure value; a signal measure indicative of a consistency of a pulse shape; a signal measure indicative of an arterial pulse amplitude; a signal measure indicative of modulation ratios of red to infrared modulations and a signal measure indicative of a period of an arterial pulse.
- Fig. 2 is a block diagram 200 depicting the operation of the signal- quality-based selection of filter parameters in accordance with embodiments of the present invention.
- the digital filter is a linear filter.
- the filter can have either a finite or an infinite impulse response.
- the filter may be a non-linear filter.
- Inputs 202 are applied to the digital filter 204 and to a signal quality assessment subsystem 206 that assesses how noisy the inputs look.
- Subsystem 206 calculates various signal quality metrics and provides the information to the selection subsystem 208, which selects filter parameters according to the criteria calculated by the signal quality subsystem 206.
- Storage subsystem 210 interfaces with the subsystems 206 and 208 to store and provide signal quality metrics as well as filter parameters.
- the selection of filter parameters is performed in real time, with the filter parameters being determined using current input samples.
- the filter parameters are calculated using a buffer 212 of recent input samples.
- signal assessment criteria and filter parameters can also be held in storage 210 for reference or for use in the calculation of new values.
- various signal quality indicators may be used to select filter parameters. Additionally, the selection of the filter parameters may be based on more than one signal quality indicator. Furthermore, the selection of the filter parameters may be based on the output of an algorithm that combines several signal quality indicators. In one embodiment in an oximeter system, the variance in the raw saturation value is used to determine the filter's smoothing coefficients. In this embodiment, the selection is achieved by comparing the variance in the raw sat signal to several thresholds, and the filter's smoothing coefficients are selected depending on which range the variance falls in. [0024] In an alternate embodiment in an oximeter system used for average pulse estimation, the filter parameter selection algorithm uses a combination of various signal quality metrics, Z to select values for filter coefficients for the digital filter, where
- Wi, w 2 , and w 3 are weighting factors
- SQ1 is a measure of the variance in the raw saturation signal
- SQ2 is a measure of the correlation between signals from different wavelengths
- SQ3 is a measure of the skew of the derivative waveform
- a non-linear function of Z can be used to select a coefficient or coefficients for the filter.
- the selection algorithm may first be tuned before it is fully implemented in a particular diagnostics system. The tuning of the selection algorithm(s) may be done manually using heuristic approaches. Alternately, the selection algorithm may be tuned statistically, in a manner similar to training a neural network.
- Embodiments of the present invention offer several advantages over conventional adaptive filtering. It is known that conventional adaptive filtering seeks to optimize some output criterion by continuously tuning the coefficients in a linear filter. The approach as embodied by the present invention is advantageous over conventional adaptive filtering for the following reasons.
- filter parameters in accordance with embodiments of the present invention are selected by switching among several preset or fixed values, rather than being varied or tuned continuously. By switching the parameters of the digital filter among fixed, preset values, the embodiments of the present invention provide for computational savings and simplicity of implementation.
- the parameters of the digital filter are selected based upon an assessment of the input signal received by the filter rather than by comparing the filter's output with its input. This too, provides for computational savings and simplicity of implementation.
- the filter need not be a linear filter, that is the filter is not required to be linear in its input-output relationship. Since the filter in accordance with embodiments of the present invention is not constrained to be linear, the filter's design can correspond more to physiological than to mathematical requirements, as is the case with most conventional adaptive filtering schemes. This physiological-based filter parameter selection may be used to, for example, attenuate pulse amplitudes above a threshold, or respond more quickly to decreases than to increases in blood oxygen saturation. [0027] Accordingly, as will be understood by those of skill in the art, the present invention which is related to reducing noise effects in a system for measuring a physiological parameter, may be embodied in other specific forms without departing from the essential characteristics thereof.
- signals indicative of any physiological parameter other than oxygen saturation such as pulse rate, blood pressure, temperature, or any other physiological variable could be filtered using the techniques of the present invention.
- signals indicative of any physiological parameter other than oxygen saturation such as pulse rate, blood pressure, temperature, or any other physiological variable
- many other indicators of the quality of the input signal can be used as a basis for the selection of the filter's coefficients.
- present embodiments have been described in the time-domain, frequency-based methods are equally relevant to the embodiments of the present invention. Accordingly, the foregoing disclosure is intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims.
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Abstract
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Priority Applications (3)
Application Number | Priority Date | Filing Date | Title |
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CA2512579A CA2512579C (en) | 2003-01-13 | 2003-12-29 | Selection of preset filter parameters based on signal quality |
JP2004566955A JP2006512990A (en) | 2003-01-13 | 2003-12-29 | Select preset filter parameters based on signal quality |
EP03800274.7A EP1586051B1 (en) | 2003-01-13 | 2003-12-29 | Selection of preset filter parameters based on signal quality |
Applications Claiming Priority (2)
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US10/341,722 | 2003-01-13 | ||
US10/341,722 US7016715B2 (en) | 2003-01-13 | 2003-01-13 | Selection of preset filter parameters based on signal quality |
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WO2004066161A1 true WO2004066161A1 (en) | 2004-08-05 |
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PCT/US2003/041467 WO2004066161A1 (en) | 2003-01-13 | 2003-12-29 | Selection of preset filter parameters based on signal quality |
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US (3) | US7016715B2 (en) |
EP (1) | EP1586051B1 (en) |
JP (2) | JP2006512990A (en) |
CA (1) | CA2512579C (en) |
WO (1) | WO2004066161A1 (en) |
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JP2008524927A (en) * | 2004-12-17 | 2008-07-10 | テキサコ ディベラップメント コーポレイション | Dynamic cutoff frequency variable filter |
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US7016715B2 (en) | 2006-03-21 |
CA2512579C (en) | 2016-03-29 |
CA2512579A1 (en) | 2004-08-05 |
JP2010227602A (en) | 2010-10-14 |
US20060030766A1 (en) | 2006-02-09 |
EP1586051A1 (en) | 2005-10-19 |
US20040138538A1 (en) | 2004-07-15 |
US20150320360A1 (en) | 2015-11-12 |
JP2006512990A (en) | 2006-04-20 |
EP1586051B1 (en) | 2016-04-13 |
US8983800B2 (en) | 2015-03-17 |
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