WO2017174738A1 - Method and display for reliability of the real-time measurements of physiological signals - Google Patents

Method and display for reliability of the real-time measurements of physiological signals Download PDF

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Publication number
WO2017174738A1
WO2017174738A1 PCT/EP2017/058280 EP2017058280W WO2017174738A1 WO 2017174738 A1 WO2017174738 A1 WO 2017174738A1 EP 2017058280 W EP2017058280 W EP 2017058280W WO 2017174738 A1 WO2017174738 A1 WO 2017174738A1
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signal
parameter
physiological signal
noise
threshold
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PCT/EP2017/058280
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French (fr)
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Reza FIROOZABADI
Richard Earl GREGG
Saeed Babaeizadeh
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Koninklijke Philips N.V.
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Priority to US62/449,314 priority
Application filed by Koninklijke Philips N.V. filed Critical Koninklijke Philips N.V.
Publication of WO2017174738A1 publication Critical patent/WO2017174738A1/en

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    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7221Determining signal validity, reliability or quality
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/04Measuring bioelectric signals of the body or parts thereof
    • A61B5/04012Analysis of electro-cardiograms, electro-encephalograms, electro-myograms
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/04Measuring bioelectric signals of the body or parts thereof
    • A61B5/0402Electrocardiography, i.e. ECG
    • A61B5/044Displays specially adapted therefor
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/02Detecting, 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
    • A61B5/024Detecting, measuring or recording pulse rate or heart rate
    • A61B5/02416Detecting, measuring or recording pulse rate or heart rate using photoplethysmograph signals, e.g. generated by infra-red radiation
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/04Measuring bioelectric signals of the body or parts thereof
    • A61B5/0476Electroencephalography
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/04Measuring bioelectric signals of the body or parts thereof
    • A61B5/0488Electromyography
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/05Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radiowaves
    • A61B5/053Measuring electrical impedance or conductance of a portion of the body
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/74Details of notification to user or communication with user or patient ; user input means
    • A61B5/742Details of notification to user or communication with user or patient ; user input means using visual displays

Abstract

Various embodiments described herein relate to methods, systems, devices, and non- transitory machine -readable storage mediums for communicating (S41, S42) the reliability of parameters (PM) extracted from a physiological signal (MPS) (e.g. an ECG) versus a signal quality of the physiological signal (MPS) (e.g. a noise measure) (S43-S45). Some parameters (PM) are more sensitive to noise, while other parameters (PM) may exhibit acceptable low sensitivity to noise. By implementation of an independent noise tolerance threshold (NTTN) for each parameter (PM), various embodiments avoid treating all signal quality measurements of the physiological signal (MPS) in the same manner. In other words, the parameter-based threshold method may help operators to avoid discarding valid measurements of the physiological signal (MPS) or accepting unreliable measurements of the physiological signal (MPS). Various embodiments described herein provide a visual indication whether a measurement of the physiological signal (MPS) is reliable, uncertain or unreliable.

Description

Method And Display For Reliability Of The Real-Time Measurements Of Physiological Signals

TECHNICAL FIELD

[0001] Various embodiments described herein relate to systems for monitoring physiological signals.

BACKGROUND

[0002] Some physiological signal measurements are very sensitive to noise while physiological signal measurements may be fairly unaffected by high noise levels. Most clinical monitoring devices will display the physiological signal measurements only if the signal quality is good. The reliability is a Boolean value, either good enough or not good enough with no accounting for levels of quality. Some clinical systems use a single noise- tolerance threshold for signal quality, overlooking the fact that the noise tolerance level could be very diverse for different parameters. This may cause a physiological signal measurement being deemed unreliable relative to the single noise-tolerance threshold although such these physiological signal measurements may in fact be reliable, and conversely, in another extreme scenario the use of a single noise-tolerance threshold may lead to an adoption of all physiological signal measurements including the unreliable ones the clinical decision making process.

SUMMARY

[0003] Embodiments described herein are related to the reliability of parameters extracted from physiological signals (e.g. ECG) versus signal quality. Some parameters are more sensitive to the noise, while the others may exhibit acceptable low variations. The noise-sensitive measurements will be unreliable in higher noise levels, but the others could still be valid. The unreliable measurements displayed on the monitor may be misleading if not declared, which emphasizes the importance of detecting those uncertain measurements.

[0004] Various embodiments make use of the noise scores determined by any signal quality indicator for physiological signals (including ECG signals) and relate it to the parameters displayed on the screen or on printed reports. Making use of different noise tolerance levels identified by an empirical approach for each parameter, the system defines noise-tolerance thresholds for each parameter and determines the reliability of parameter according to that. Each parameter on the screen may be marked for reliability independent of the others. Acceptable reliability of a parameter may also depend on the monitoring application. In that way, each parameter may have a different reliability scale based on noise and the application.

[0005] By implementation of an independent noise tolerance threshold for each parameter, various embodiments avoid treating all measurements in the same manner. In other words, the parameter-based threshold method may help operators to avoid discarding valid measurements or accepting unreliable ones. Various embodiments described herein facilitate the operator in making the right decision whether a measurement should be used confidently, used with caution, or completely discarded.

[0006] One embodiment of the present disclosure is a parameter noise tolerance threshold generation method. The method involves obtaining of a physiological signal, and a generation of a plurality of noisy signals by adding noise to the physiological signal, wherein the plurality of noisy signals exhibit multiple different signal qualities. The method further involves extracting a base value for a physiological parameter from the clean physiological signal, extracting a plurality of test values for the physiological parameter from the plurality of noisy signals, and selecting one or more multiple different signal qualities as a noise tolerance threshold for the physiological parameter based on deviations of the test values from the base value. [0007] A second embodiment of the present disclosure is parameter noise tolerance threshold generation device (e.g., a controller for a physiological training device). The device comprises an interface for receiving a physiological signal. The device further employs a memory; and a processor configured to generate a plurality of noisy signals by adding noise to the physiological signal, wherein the plurality of noisy signals exhibit multiple different signal qualities. The processor is further configured to extract a base value for a physiological parameter from the physiological signal, extract a plurality of test values for the physiological parameter from the plurality of noisy signals, and select at least one of the multiple different signal qualities as a noise tolerance threshold for the physiological parameter based on deviations of the test values from the base value.

[0008] A third embodiment of the present disclosure is a non-transitory machine- readable medium encoded with instructions for generating parameter noise tolerance thresholds. The medium comprises instructions for obtaining a physiological signal and instructions for generating a plurality of noisy signals by adding noise to the physiological signal, wherein the plurality of noisy signals exhibit multiple different signal qualities. The medium further comprises instructions for extracting a base value for a physiological parameter from the physiological signal, instructions for extracting a plurality of test values for the physiological parameter from the plurality of noisy signals, and instructions for selecting at least one of the multiple different signal qualities as a noise tolerance threshold for the physiological parameter based on deviations of the test values from the base value.

[0009] A fourth embodiment of the present disclosure is a physiological signal reliability display method. The method involves obtaining a physiological signal and a measuring of the signal quality of the physiological signal. The method further involves extracting a noise tolerance threshold defined for a first physiological parameter from the physiological signal, extracting a noise tolerance threshold defined for a second physiological parameter from the physiological signal, and comparing the signal quality to the noise tolerance threshold defined for the first physiological parameter and to the noise tolerance threshold defined for the second physiological parameter. The method further involves displaying a reliability indicator of the physiological signal based on the comparison of the signal quality to the noise tolerance threshold defined for the first physiological parameter and to the noise tolerance threshold defined for the second physiological parameter. [0010] A fifth embodiment of the present disclosure is a physiological signal reliability display device (e.g., a controller of a physiological monitoring device). The device comprises an interface for receiving a physiological signal. The device further comprises a memory and a processor configured to measure the signal quality of the physiological signal, extract a noise tolerance threshold defined for a first physiological parameter from the physiological signal, extract a noise tolerance threshold defined for a second physiological parameter from the physiological signal, and compare the signal quality to the noise tolerance threshold defined for the first physiological parameter and to the noise tolerance threshold defined for the second physiological parameter. The processor is further configured to display a reliability indicator of the physiological signal based on the comparison of the signal quality to the noise tolerance threshold defined for the first physiological parameter and to the noise tolerance threshold defined for the second physiological parameter.

[0011] A sixth embodiment of the present disclosure is non-transitory machine- readable medium encoded with instructions for displaying a physiological signal indicator. The non-transitory machine -readable medium comprises instructions for obtaining a physiological signal, for extracting a noise tolerance threshold defined for a first physiological parameter from the physiological signal, for extracting a noise tolerance threshold defined for a second physiological parameter from the physiological signal, and for comparing the signal quality to the noise tolerance threshold defined for the first physiological parameter and to the noise tolerance threshold defined for the second physiological parameter. The non- transitory machine -readable medium further comprises instructions for displaying a reliability indicator of the physiological signal based on the comparison of the signal quality to the noise tolerance threshold defined for the first physiological parameter and to the noise tolerance threshold defined for the second physiological parameter.

[0012] A seventh embodiment of the present disclosure is a physiological signal reliability monitor employing a display, and a physiological signal reliability controller structured configured to extract a plurality of parameters from a physiological signal, calculate a signal quality of the physiological signal, identify at least one noise-tolerance threshold defined for at least one parameter, compare the signal quality of the physiological signal to the at least one noise-tolerance threshold defined for at least one parameter of the physiological signal, and control the display for visualizing a measurement of the

physiological signal and a signal reliability indicator indicative of a reliability of the measurement of the physiological signal derived from the comparison of the signal quality of the physiological signal to the at least one noise-tolerance threshold defined for the at least one parameter of the physiological signal.

[0013] An eighth embodiment of the present disclosure is a physiological signal reliability method involving an obtaining of a physiological signal, an extracting of a plurality of parameters from a physiological signal, a calculating of a signal quality of the physiological signal, an identifying at least one noise-tolerance threshold defined for at least one parameter, a comparing of the signal quality of the physiological signal to the at least one noise-tolerance threshold defined for the at least one parameter of the physiological signal, and a controlling of a display for visualizing a measurement of the physiological signal and a signal reliability indicator indicative of a reliability of the measurement of the physiological signal derived from the comparison of the signal quality of the physiological signal to the at least one noise- tolerance threshold defined for the at least one parameter of the physiological signal.

[0014] For purposes of describing the embodiments of the present disclosure:

[0015] (1) the term "physiological signal" broadly encompasses any signal, as known in the art of the present disclosure and exemplary described herein, quantitatively representative of a physical functioning of a living orgasm. Examples of physiological signals include, but are not limited to, an electrocardiogram (ECG), electroencephalogram (EEG), photoplethysmogram (PPG), electromyogram (EMG), and impedance.

[0016] (2) the term "parameter" of a physiological signal is to be broadly interpreted as known in the art of the present disclosure and exemplary described herein. Examples of a parameter ECG include, but are not limited to, a heart rate, a P-wave amplitude, a Q-wave amplitude, a R-wave amplitude, a T-wave amplitude, a ST-level, a QRS complex duration and a PR interval.

[0017] (3) the term "noise-tolerance threshold" broadly encompasses data, in accordance with the principles of the present disclosure and as exemplary described herein, informative of a noise measure level deemed to differentiate to any degree between a reliable sensitivity and an unreliable sensitivity of a parameter of a physiological signal to noise applied to the physiological signal.

[0018] (4) the terms "signal" and "data" broadly encompasses all forms of a detectable physical quantity or impulse (e.g., voltage, current, magnetic field strength, impedance, color) as understood in the art of the present disclosure and as exemplary described herein for transmitting information and/ or instructions in support of applying various principles of the present disclosure as subsequently described herein. Signal/ data communication encompassed by the present disclosure may involve any communication method as known in the art of the present disclosure including, but not limited to, data transmission/reception over any type of wired or wireless datalink and a reading of data uploaded to a computer-usable/ computer readable storage medium;

[0019] (5) the descriptive labels for terms "signal" and "data" herein facilitates a distinction between signals and data as described and claimed herein without specifying or implying any additional limitation to the terms "signal" and "data";

[0020] (6) the term "controller" broadly encompasses all structural

configurations, as understood in the art of the present disclosure and as exemplary described herein, of an application specific main board or an application specific integrated circuit housed within or linked to a monitoring device and/ or a workstation of a monitoring system for controlling an application of various principles of the present disclosure as subsequently described herein. The structural configuration of the controller may include, but is not limited to, processor(s), computer-usable/computer readable storage medium(s), an operating system, application module(s), peripheral device controller (s), slot(s) and port(s);

[0021] (7) the descriptive labels for term "controller" herein facilitates a distinction between controllers as described and claimed herein without specifying or implying any additional limitation to the term "controller";

[0022] (8) the term "module" broadly encompasses a module incorporated within or accessible by a controller consisting of an electronic circuit and/ or an executable program (e.g., executable software stored on non-transitory computer readable medium(s) and/ or firmware) for executing a specific application;

[0023] (9) the descriptive labels for term "module" herein facilitates a distinction between modules as described and claimed herein without specifying or implying any additional limitation to the term "module";

[0024] (10) Examples of a monitoring device include, but are not limited to, diagnostic ECG devices (e.g., Page Writer TC cardiographs, Efficia series of cardiograph), exercise ECG devices (e.g., ST80i stress testing system), ambulatory ECG devices (Holter monitor), bed-side monitoring ECG device (e.g., IntelliVue monitors, SureSigns monitors, and Goldway monitors), hemodynamic monitoring (e.g., per Flex Cardio Physiomonitoring system), telemetry ECG device (e.g., IntelliVue MX40 monitor); automated external defibrillator and advanced life support products (e.g., HeartStart MRx and HeartStart XL defibrillators, and Efficia DFM100 defibrillator /monitor);

[0025] (11) Examples of a monitoring system include, but are not limited to, a central monitoring system (e.g., PIIC iX and IntelliVue IL central monitoring systems); and

[0026] (12) the term "a physiological signal reliability display monitor" broadly encompasses all monitoring devices and monitoring systems incorporating the principles of the present disclosure for implementing an independent noise tolerance threshold for each parameter of a physiological signal as exemplary described herein.

[0027] The foregoing embodiments and other embodiments of the present disclosure as well as various features and advantages of the present disclosure will become further apparent from the following detailed description of various embodiments of the present disclosure read in conjunction with the accompanying drawings. The detailed description and drawings are merely illustrative of the present disclosure rather than limiting, the scope of present disclosure being defined by the appended claims and equivalents thereof.

BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to better understand various example embodiments, reference is made to the accompanying drawings, wherein:

[0029] FIG. 1 illustrates a data flow diagram of an exemplary embodiment of a parameter noise-threshold generation method in accordance with the principles of the present disclosure;

[0030] FIG. 2A illustrates an exemplary ECG 12-lead signal as known in the art of the present disclosure;

[0031] FIG. 2B illustrates exemplary noisy Lead II signals in accordance with the principles of the present disclosure;

[0032] FIG. 2C illustrates an exemplary variations in a ST-VM (ST- Vector

Magnitude) parameter over various levels of SNR in accordance in accordance with the principles of the present disclosure;

[0033] FIG. 3 illustrates an exemplary chart showing example thresholds for ECG

P-wave amplitude with 25μΎ and 50μν deviations in accordance with the principles of the present disclosure;

[0034] FIG. 4 illustrates an exemplary chart showing example thresholds for ECG

Q-wave amplitude with 25μΎ and 50μν deviations in accordance with the principles of the present disclosure;

[0035] FIG. 5 illustrates an exemplary chart showing example thresholds for ECG

R-wave amplitude with 25μΎ and 50μν deviations in accordance with the principles of the present disclosure;

[0036] FIG. 6 illustrates an exemplary chart showing example thresholds for ECG

T-wave amplitude with 25μν and 50μν deviations in accordance with the principles of the present disclosure;

[0037] FIG. 7 illustrates an exemplary chart showing example thresholds for ECG

ST-level amplitude with 25μν and 50μν deviations in accordance with the principles of the present disclosure;

[0038] FIG. 8 illustrates an exemplary chart showing example deviation of heart rate from its reference that always less lObpm where no threshold is defined in accordance with the principles of the present disclosure; [0039] FIG. 9 illustrates an exemplary chart showing example thresholds for ECG

QRS complex duration with 10msec and 20msec deviations in accordance with the principles of the present disclosure;

[0040] FIG. 10 illustrates an exemplary chart showing example thresholds for ECG

PR interval with 10msec and 20msec deviations in accordance with the principles of the present disclosure;

[0041] FIG. 11 illustrates a block diagram of an exemplary embodiment of a parameter noise tolerance threshold generation controller in accordance with the principles of the present disclosure in accordance with the principles of the present disclosure;

[0042] FIG. 12 a data flow diagram of an exemplary embodiment of a physiological signal reliability display method in accordance with the principles of the present disclosure;

[0043] FIG. 13 illustrates an exemplary reliability chart and exemplary reliability zones in accordance with the principles of the present disclosure;

[0044] FIG. 14 illustrates a block diagram of an exemplary ECG monitoring device in accordance with the principles of the present disclosure;

[0045] FIG. 15 illustrates an exemplary ECG monitoring display in accordance with the principles of the present disclosure;

[0046] FIG. 16 illustrates exemplary embodiments of graphical displays for measurement reliability, using the color bar next to the measurement, the signal bars, and the color intensity in accordance with the principles of the present disclosure; and

[0047] FIG. 17 illustrates a block diagram of an exemplary embodiment of a physiological signal reliability display controller in accordance with the principles of the present disclosure in accordance with the principles of the present disclosure;

DETAILED DESCRIPTION

[0048] The description and drawings presented herein illustrate various principles. It will be appreciated that those skilled in the art will be able to devise various arrangements that, although not explicitly described or shown herein, embody these principles and are included within the scope of this disclosure. As used herein, the term, "or," as used herein, refers to a non-exclusive or (i.e., and/ or), unless otherwise indicated (e.g., "or else" or "or in the alternative"). Additionally, the various embodiments described herein are not necessarily mutually exclusive and may be combined to produce additional embodiments that incorporate the principles described herein.

[0049] Parameters extracted from ECG (or other) signals mixed with artifact or noise have various levels of noise tolerance. This means that some parameters may be unaffected by the noise level, while the others could be less reliable or inaccurate. Choosing a single noise level threshold for all parameters may result in accepting invalid results or discarding valid parameters. Accordingly, various embodiments of the present disclosure derive a signal quality measurement (e.g., a noise measure) of a physiological signal in a noisy environment and compare the signal quality measurement to a noise-tolerance threshold for various ECG parameters.

[0050] Various embodiments described herein provide a signal quality indicator to measure the noise levels on the physiological signal and determines noise scores. An example of a signal quality indicator is a method where the high- and low- frequency components of the noise were measured independently but could be combined to provide a unique noise score.

[0051] In an empirical approach, deviation of parameter values from their reference values in a clean signal, are evaluated for noise-added signals of various noise levels from clean to quite noisy. The noise-tolerance threshold at each noise level is the point where the parameter values go beyond the acceptable range of deviation from the reference values. This is repeated for all desired parameters and a set of thresholds is obtained. More than a single threshold can be defined for a parameter if various reliability levels are desired, which in turn creates more than two reliability zones or even a continuous reliability on a probability scale from 0 to 1. [0052] Reliability of each parameter can be displayed on the visual output in various ways. Various embodiments described herein are flexible enough to be adjusted to the applications defined by operators or to the specific requirements of various monitoring devices and monitoring systems.

[0053] To facilitate an understanding of the present disclosure, the following description of FIGS. 1-11 teaches principles of parameter noise-threshold generation methods and controllers of the present disclosure. From the description of FIGS. 1 -11, those having ordinary skill in the art of the present disclosure will appreciate how to apply the principles of the present disclosure for making and using numerous and various embodiments of parameter noise-threshold generation methods, controllers and display methods of the present disclosure.

[0054] Referring to FIG. 1, a parameter noise-threshold generation method of the present disclosure provides for a determination of an individual and independent noise- tolerance threshold for one or more parameters of a same type of physiological signal. In practice, a parameter noise-threshold generation method of the present disclosure may use a training database including clean recordings of a physiological signal (high-quality signals essentially free of any noise), such as, for example, a clinical database 10 shown in FIG. 1, and may further use an additional training database including different level of noise recordings of physiological signals (absent the physiological component) within a monitoring environment, such as, for example, a real noise database 11 as shown in FIG. 1.

[0055] Different levels of noise derived from the noise recordings of physiological signals may be added to the clean recordings of physiological signals to generate noise-added signals. Using a signal quality indicator algorithm implemented by a parameter noise- threshold generation method of the present disclosure according to various embodiments, parameter values may be measured for each noise level. Deviation of these values from reference measurements may determine the parameter noise tolerance and the noise- tolerance threshold for that parameter.

[0056] Still referring to FIG. 1, a parameter noise-threshold generation method 10 of the present disclosure involves a stage Sl l encompassing an execution of a power level calculation of each recorded clean physiological signal CPS of an X number of such signals, X > 1, such as, for example, a peak-to-peak amplitude calculation PPLX of each clean physiological recording signal CPS as shown (e.g., via a Physionet sigamp tool). In practice, the clean physiological signals CPS may have any time interval deemed necessary for running a signal quality indication of the present disclosure (e.g., a five (5) minute time interval).

[0057] Parameter noise-threshold generation method 10 of the present disclosure involves a stage S12 encompassing an execution of a power level calculation of each recorded noisy physiological signals NPS of a Y number of such signals, Y > 1 , such as, for example, a root mean square ("RMS") amplitude calculation NPLy of each environment noise recording signal ENS as shown (e.g., via a Physionet sigamp tool). In practice, the noisy physiological signals NPS may have any time interval deemed necessary for running a signal quality indication of the present disclosure (e.g., a five (5) minute time interval).

[0058] An example of a recorded clean physiological signal CPS is a five (5) minute interval of 12-lead ECG segments pre-operatively or intra-operatively derived from a patient, and an example of recording noisy physiological signal NPS is noise generated by medical professional and associated ECG equipment within an operating environment of an ECG monitor with the time interval of the noisy physiological signal NPS containing

predominantly baseline wander, muscle artifact and electrode motion artifact.

[0059] A stage S13 of method 10 encompasses a normalization of additive noise for a desired signal-to-noise ("SNR") using the power levels of the clean physiological signals CPS and the noisy physiological signals NPS.

[0060] In one embodiment of stage SI 3, a noise measure for each clean

physiological signal CPS and the noisy physiological signal NPS is calculated in accordance with the following equation [1] :

[0061]

Noise Measure = [aNHF + NBW] [1]

[0062] where NHF is total high-frequency noise measure, A -is a total low-frequency noise measure, E[.] is the mean, a is a weighting factor and β is a weighting factor. The weighting factors are derived empirically based on perceived effect on total noise where the same level of high frequency noise has a much larger effect than baseline wander from beat to beat.

[0063] A noise model between the noise measures and SNR levels is thereafter derived by subtracting the noise measure of each clean physiological signal CPS from the noise measure of each noisy physiological signal NPS and plotting a log scale to achieve a log linear model in accordance with the following equation [2] :

[0064]

SNR = 58.195 - 19/304 log(N ^) [2] [0065] where NMS is the subtracted noise measures.

[0066] An example of stage S13 involves 10-second ECG recordings from databases

10 and 11 divided into 1 -second, 12-lead segments on which the high-frequency and low- frequency noise measures are calculated. The total noise measure in a 10-second recording is the combination of these two frequency components averaged in two dimensions, (1) across all 1 -second segments in time and (2) across the multiple ECG signals.

[0067] More particularly, the high-frequency noise measure in a 1 -second segment is estimated as the median of the standard deviations of the ECG samples in the short intervals with the lowest activity as known in the art of the present disclosure. The total high frequency noise measure in a 1 -second segment (NHF) is the average of the high frequency noise measures across the leads.

[0068] The low-frequency noise measure is identified by the difference in the adjacent 1 -second segment baselines which are calculated by averaging the ECG samples in the short-term low- activity intervals and taking the summation of difference between the current baseline and the last two (2) baselines. The total low-frequency noise measure in a 1- second segment (NBW) is the average of the low-frequency noise measures across all leads.

[0069] Stage SI 3 utilizes the following equations [3] [-4] to determine the appropriate scale gamma for each SNR (here from 2dB to 24dB by increments of ldB):

[0070]

Noise-added ECG = Base ECG + ^.Noise [3] ppk(Base ECG) )

SNR = 10 log y2.P(Noise)

!OC-SNR/20) PPk(Base ECG) )

V RMS(Noise) [0071] Equivalent noise measures ( M) at each SNR are derived from the log-linear model in [2]. These are the x-axis values in figures 3-10.

[0072] Scaled noise segments are added to the clean physiological signal. Using the scaled noise added to the physiological signal results in a final SNR of -6dB to 24dB.

[0073] Still referring to FIG. 1, a stage S14 of method 10 encompasses a generation of noised-added physiological signals NAPS derived from an addition of each scaled noise segment SNS to each recording of the clean physiological signals CPS, and a stage SI 5 of method 10 encompasses a generation of noise-tolerance thresholds NTTz, Z≥ 1, for each clean physiological signal CPS derived from a deviation limit associated with each noise- tolerance threshold NTT.

[0074] For example, as shown in FIG. 2B, stage SI 4 may involve an addition of scaled 10-second noise segments SNS having a SNR of -6 dB, -4 dB, -2dB, 0 dB, 8 dB, 16dB, and 24dB to a 10-second 12-lead ECG clean recording 20 in FIG. 2A to yield noise-added recordings including noise-added recording 21 for lead II.

[0075] From the noise-added recordings for each parameter, stage SI 5 may further involve a plotting of variations in the parameter across all noise-added recordings, such as, for example, a plotting 21 of variations in a ST-VM (V ector magnitude of ST level) parameter of a ECG signal across various noise-added recordings as shown in FIG. 2C.

[0076] Stage SI 5 may further involve a deviation limit of several ECG parameters for every 10-second segment in accordance

[0077] A noise-tolerance threshold for ECG parameter in a noise-added

physiological recording is defined above the highest noise level where the deviation of the 1 st and 3rd quartiles, as well as the base ECG median, for the parameter median at a particular noise level is still below a pre-defined deviation limit, such as, for example, the deviation limits of Table 1.

[0078] The noise tolerance threshold is defined in the middle point between the highest noise level meeting the following criteria [3], [4], and [5] and the next noise level:

[0079]

M - CM < Δ [3]

Q3 - M < Δ [4] M - Mo I < Δ [5]

[0080] where Qi, M, Q3 are the ECG parameter valves at 1st quartile, median and 3rd quartile of data at the current noise level, M0 is the parameter median in the base ECG and Δ is the deviation limit.

[0081] FIGS. 3-10 respectively illustrates variation plots 30-37 of generated thresholds for the following ECG parameters of TABLE 1 :

[0082] TABLE 1

ECG Parameter Reliable Noise-Tolerance Unreliable Noise-Tolerance

Threshold Threshold

P-wave amplitude 2.6 6.2

Q-wave amplitude 2.6 6.2

R-wave amplitude 11.8 17.2

T-wave amplitude 6.2 9.6

ST amplitude 5.0 9.6

Heart Rate Nil Nil

QRS duration 5.0 11.8

PR interval 11.8 12.4

[0083] Referring to TABLE 2 and FIGS. 3-10, a reliable noise-tolerance threshold represents a threshold upon which a variation in the noise sensitivity of a particular parameter exceeds a reliable deviation limit, and an unreliable noise-tolerance threshold represents a threshold upon which a variation in the noise sensitivity of a particular parameter exceeds an unreliable deviation limit

[0084] FIG. 11 illustrates a parameter noise-threshold generation controller 110 for implementing method 10 of FIG. 1. As shown, controller 110 includes a processor 111, a memory 112, a user interface 113, a network interface 114, and a storage 115 interconnected via one or more system buses 117. In practice, the actual organization of the components 111-117 of controller 110 may be more complex than illustrated.

[0085] The processor 111 may be any hardware device capable of executing instructions stored in memory or storage or otherwise processing data. As such, the processor 111 may include a microprocessor, field programmable gate array (FPGA), application-specific integrated circuit (ASIC), or other similar devices.

[0086] The memory 112 may include various memories such as, for example LI, L2, or L3 cache or system memory. As such, the memory 112 may include static random access memory (SRAM), dynamic RAM (DRAM), flash memory, read only memory (ROM), or other similar memory devices.

[0087] The user interface 113 may include one or more devices for enabling communication with a user such as an administrator. For example, the user interface 113 may include a display, a mouse, and a keyboard for receiving user commands. In some embodiments, the user interface 113 may include a command line interface or graphical user interface that may be presented to a remote terminal via the network interface 114.

[0088] The network interface 114 may include one or more devices for enabling communication with other hardware devices. For example, the network interface 114 may include a network interface card (NIC) configured to communicate according to the

Ethernet protocol. Additionally, the network interface 114 may implement a TCP/IP stack for communication according to the TCP/IP protocols. Various alternative or additional hardware or configurations for the network interface will be apparent.

[0089] The storage 115 may include one or more machine-readable storage media such as read-only memory (ROM), random-access memory (RAM), magnetic disk storage media, optical storage media, flash-memory devices, or similar storage media. In various embodiments, the storage 115 may store instructions for execution by the processor 111 or data upon with the processor 111 may operate. For example, the storage 115 store a base operating system 116a for controlling various basic operations of the hardware.

[0090] More particular to method 10, storage 115 also includes a power level calculation module 116b for implementing stage Sl l and S12 of method 10 as previously described herein, an additive noise normalization module 116c for implementing stage SI 3 of method 10 as previously described herein, a noise-added physiological signal generation module 116d for implementing stage S14 of FIG. 1 as previously described herein, and a noise-tolerance threshold generation module 116e for implementing stage SI 5 of FIG. 1 as previously described herein.

[0091] To facilitate an understanding of the present disclosure, the following description of FIGS. 12-17 teaches principles of physiological signal reliability methods and controllers of the present disclosure. From the description of FIGS. 12-17, those having ordinary skill in the art of the present disclosure will appreciate how to apply the principles of the present disclosure for making and using numerous and various embodiments of physiological signal reliability methods and controllers of the present disclosure.

[0092] Generally, physiological signal reliability methods provide for a measurement of a noise level on a monitored physiological signal and compare it to a pre-defined set of parameter noise-tolerance thresholds to determine if the parameter is in the reliability zone. This is done for each parameter to thereby generate a visual, an audible and/ or a tactile indication of a reliability of a measurement of the monitored physiological signal.

Additionally, a real-time learning algorithm may be added to automatically update the parameter noise-tolerance thresholds using a statistical behavior of each parameter.

[0093] Referring to FIG. 12, a physiological signal reliability display method 40 provides stages S41-S45 for processing a monitored physiological signal MPS (e.g., an ECG) to display a visual indication of a reliability of a measurement of the monitored physiological signal MPS.

[0094] A stage S41 of method 40 encompasses an extraction of a M number of parameters PM of the monitored physiological signal MPS for visual display during a stage S42 of method 10.

[0095] A stage S43 of method 40 encompasses a calculation of a noise measure NM of the monitored physiological signal in accordance with the following equation [1] as previously described herein:

[0096]

Noise Measure = E [OLNHF + NBW] [1]

[0097] where NHF is total high-frequency noise measure, A -is a total low-frequency noise measure, E[.] is the mean, a is a weighting factor and β is a weighting factor. The weighting factors are derived empirically based on perceived effect on total noise where the same level of high frequency noise has a much larger effect than baseline wander from beat to beat.

[0098] Stage S44 of method 40 encompasses a retrieval of a N number of noise- tolerance thresholds NTTN for the parameter(s) PM of the monitored physiological signal MPS as predefined by a noise-tolerance threshold generation method of the present disclosure (e.g., FIGS. 3-10).

[0099] Stage S45 of method 10 encompasses a comparison of noise measure NM of the monitored physiological signal to one or more of the noise-tolerance thresholds NTTN for the parameter(s) PM of the monitored physiological signal MPS to yield a signal reliability indicator SRI for visual display during stage S42.

[00100] For example, as shown in FIG. 13, a table 50 list each parameter PM with associated reliable threshold 52 and an unreliable threshold 53 defining chart 54 of a reliability zone 56, an uncertain zone 57 and an unreliable zone 58.

[00101] On a parameter by parameter basis, a signal reliability indicator SRI may be derived from a comparison of the noise measure NM to reliable threshold 52 and unreliable threshold 53 whereby the signal reliability indicator SRI will indicate which zone 56-58 is occupied by the particular parameter.

[00102] In practice after independent threshold evaluations of each parameter, a signal reliability indicator SRI for the monitored physiological signal MPS may be determined to be in reliable zone 56 if the noise measure NM is less than the reliable threshold 52 for all parameters PM or for a pre-defined subset of parameters PM.

[00103] Conversely in practice after independent threshold evaluations of each parameter, a signal reliability indicator SRI for the monitored physiological signal MPS may be determined to be in unreliable zone 58 if the noise measure NM is greater than the unreliable threshold 53 for all parameters PM or for a pre-defined subset of parameters PM.

[00104] Otherwise, the signal reliability indicator SRI for the monitored physiological signal MPS may be determined to be in uncertain zone 57 if the noise measure NM is great than reliable threshold 52 and less than the unreliable threshold 53 for all parameters PM or for a pre-defined subset of parameters PM, or if the noise measure NM is within the reliable zone 56 and the unreliable zone 57 for an equal or equivalent number of parameters PM.

[00105] Referring back to FIG. 13, stage S44 may further encompass method 10

(FIG. 1) as applied to the monitored physiological signal MPS to refine the noise-tolerance thresholds for parameter PM.

[00106] FIG. 14 illustrates an ECG monitor 70 implementing method 40 (FIG. 12) for determining a reliability of an ECG measurement of a patient 60 via leads 61. ECG monitor 70 employs a ECG display 80 for displaying an ECG 81 and a visual reliability indicator 82 in the form of a visual reliable indicator 83, a visual uncertain indicator 84 and visual unreliable indicator 85 as generated by a ECG display controller 80.

[00107] Specifically, ECG display controller 80 includes reliable thresholds and unreliable threshold for various ECG parameters (e.g., thresholds for ECG parameters shown in FIGS. 3-10) whereby ECG display controller 80 calculates and compares a noise measure of the ECM signal 81 to the reliable thresholds and unreliable threshold for various ECG parameters. User input devices (e.g., button(s), dial(s), touchpad, etc.) (not shown for clarity) and/ or a graphical user interface (not shown for clarity) may be used to interface with the display of the ECG 81.

[00108] ECG monitor 70 further may employ a printer 90 whereby ECG display controller 80 may further control a printing of ECG 81 and one of the visual indicators 82- 84 corresponding to the noise measurement comparison of the ECM signal 81 to the reliable thresholds and unreliable threshold for various ECG parameters.

[00109] FIG. 14 illustrates an exemplary detailed clinical display 81a for ECG monitor having a visual heart rate indicator as shown in FIG. 16.

[00110] In a color bar embodiment, a visual unreliable indicator 84a includes a red line under a heart rate indicator, a visual uncertain indicator 83a includes a yellow line under the heart rate indicator and a visual reliable indicator 82a includes a green line under the heart rate indicator.

[00111] In a signal bar embodiment, a visual unreliable indicator 84b includes zero (0) signal bar next to the heart rate indicator, a visual uncertain indicator 83b includes a single signal bar next to the heart rate indicator and a visual reliable indicator 82b includes two (2) signal bars next to the heart rate indicator.

[00112] In a color intensity embodiment, a visual unreliable indicator 84b is a substantially transparent view of the heart rate indicator, a visual uncertain indicator 83b is a semi-transparent view of a heart rate indicator and a visual reliable indicator 82b an opaque view of the heart rate indicator.

[00113] FIG. 17 illustrates a physiological signal reliability display controller 140 for implementing method 40 of FIG. 12. As shown, controller 140 includes a processor 141, a memory 142, a user interface 143, a network interface 144, and a storage 145 interconnected via one or more system buses 147. In practice, the actual organization of the components 141-117 of controller 140 may be more complex than illustrated. [00114] The processor 141 may be any hardware device capable of executing instructions stored in memory or storage or otherwise processing data. As such, the processor 141 may include a microprocessor, field programmable gate array (FPGA), application-specific integrated circuit (ASIC), or other similar devices.

[00115] The memory 142 may include various memories such as, for example LI, L2, or L3 cache or system memory. As such, the memory 142 may include static random access memory (SRAM), dynamic RAM (DRAM), flash memory, read only memory (ROM), or other similar memory devices.

[00116] The user interface 143 may include one or more devices for enabling communication with a user such as an administrator. For example, the user interface 143 may include a display, a mouse, and a keyboard for receiving user commands. In some embodiments, the user interface 143 may include a command line interface or graphical user interface that may be presented to a remote terminal via the network interface 144.

[00117] The network interface 144 may include one or more devices for enabling communication with other hardware devices. For example, the network interface 144 may include a network interface card (NIC) configured to communicate according to the Ethernet protocol. Additionally, the network interface 144 may implement a TCP/IP stack for communication according to the TCP/IP protocols. Various alternative or additional hardware or configurations for the network interface will be apparent.

[00118] The storage 145 may include one or more machine-readable storage media such as read-only memory (ROM), random-access memory (RAM), magnetic disk storage media, optical storage media, flash-memory devices, or similar storage media. In various embodiments, the storage 145 may store instructions for execution by the processor 141 or data upon with the processor 141 may operate. For example, the storage 145 store a base operating system 146a for controlling various basic operations of the hardware.

[00119] More particular to method 40, storage 145 also includes a parameter extraction module 146b for implementing stage S41 of method 40 as previously described herein, an noise measure calculation module 146c for implementing stage S13 of method 40 as previously described herein, a noise-added physiological signal generation module 146d for implementing stage S14 of FIG. 1 as previously described herein, and a noise-tolerance threshold generation module 146e for implementing stage SI 5 of FIG. 1 as previously described herein. [00120] More particular to method 40, storage 145 also includes a signal quality assessment module 146s having instructions for determining one or more signal quality metrics for a received signal in accordance with stage S43, a parameter extraction module 146c having instructions for extracting one or more parameters from a received signal in accordance with stage S41 and a parameter reliability assessment module 146d having instructions for determining a reliability rating, category, or other measure for each such extracted parameter by comparing the signal quality metric(s) to one or more thresholds defined for each parameter in accordance with stages S44 and S45. Storage 145 also includes a display module 146e having instructions for rendering a reliability display of the physiological signal in accordance with stage S42 {e.g., the display of FIG. 15), and may further have instructions for rendering a reliability display of one or more of the parameters.

[00121] Referring to FIGS. 11 and 17, in practice, controllers 110 and 140 may be integrated or linked whereby controller 110 executes noise-tolerance threshold management stage S44 (FIG. 12) on behalf of controller 140.

[00122] Referring to FIGS. 1-17, those having ordinary skill in the art will appreciate the many benefits of the systems and methods of the present disclosure including, but not limited to, parameter-based noise-threshold methods, systems and devices of the present disclosure may help operators of such systems and devices to avoid discarding valid monitored physiological signal measurements or accepting unreliable measurements of a monitored physiological signal.

[00123] Furthermore, it will be apparent that various information described as stored in the storage may be additionally or alternatively stored in the memory. In this respect, the memory may also be considered to constitute a "storage device" and the storage may be considered a "memory." Various other arrangements will be apparent. Further, the memory and storage may both be considered to be "non-transitory machine-readable media." As used herein, the term "non-transitory" will be understood to exclude transitory signals but to include all forms of storage, including both volatile and non-volatile memories.

[00124] While the device is shown as including one of each described component, the various components may be duplicated in various embodiments. For example, the processor may include multiple microprocessors that are configured to independently execute the methods described herein or are configured to perform steps or subroutines of the methods described herein such that the multiple processors cooperate to achieve the functionality described herein. Further, where the device is implemented in a cloud computing system, the various hardware components may belong to separate physical systems. For example, the processor may include a first processor in a first server and a second processor in a second server.

[00125] According to the foregoing, various embodiments may support any monitoring device showing real-time physiological waveforms and display various measurements of the signals. Various embodiments may be useful for physiological signals which are often disturbed by noise or artifact that limits the interpretation and analysis of the signal. Various embodiments may specify the unreliable measurements on the screen, and help the operator to make the right decision by discarding unreliable measurements and assuming the reliable ones under any noise circumstance.

[00126] It should be apparent from the foregoing description that various example embodiments may be implemented in hardware or firmware. Furthermore, various exemplary embodiments may be implemented as instructions stored on a machine-readable storage medium, which may be read and executed by at least one processor to perform the operations described in detail herein. A machine-readable storage medium may include any mechanism for storing information in a form readable by a machine, such as a personal or laptop computer, a server, or other computing device. Thus, a machine-readable storage medium may include read-only memory (ROM), random-access memory (RAM), magnetic disk storage media, optical storage media, flash-memory devices, and similar storage media.

[00127] It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative circuitry embodying the principles described herein. Similarly, it will be appreciated that any flow charts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes which may be substantially represented in machine readable media and so executed by a computer or processor, whether or not such computer or processor is explicitly shown.

[00128] Although the various exemplary embodiments have been described in detail with particular reference to certain exemplary aspects thereof, it should be understood that the systems and methods described herein are capable of other embodiments and its details are capable of modifications in various obvious respects. As is readily apparent to those skilled in the art, variations and modifications can be affected while remaining within the spirit and scope of the invention. Accordingly, the foregoing disclosure, description, and figures are for illustrative purposes only and do not in any way limit the invention, which is defined only by the claims.

Claims

CLAIMS What is claimed is:
1. A physiological signal reliability monitor (70), comprising:
a display (80); and
a physiological signal reliability controller (90) structured configured to:
extract a plurality of parameters from a physiological signal; calculate a signal quality of the physiological signal;
identify at least one noise-tolerance threshold defined for at least one parameter;
compare the signal quality of the physiological signal to the at least one noise- tolerance threshold defined for at least one parameter of the physiological signal; and
control the display (80) for visualizing a measurement of the physiological signal and a signal reliability indicator indicative of a reliability of the measurement of the physiological signal derived from the comparison of the signal quality of the physiological signal to the at least one noise-tolerance threshold defined for the at least one parameter of the physiological signal.
2. The physiological signal reliability monitor (70) of claim 1 ,
wherein the physiological signal is one of an electrocardiogram, an
electroencephalogram, a photoplethysmogram, and electromyogram (EMG), and an impedance signal.
3. The physiological signal reliability monitor (70) of claim 1,
wherein the at least one noise-tolerance threshold of the at least one parameter includes a reliable threshold;
wherein the signal reliability indicator indicates a reliability measurement of the physiological signal responsive to the signal quality being less than the reliable threshold for the at least one parameter; and wherein the signal reliability indicator indicates an unreliability measurement of the physiological signal responsive to the signal quality being greater than the reliable threshold for the at least one parameter.
4. The physiological signal reliability monitor (70) of claim 1 ,
wherein the at least one noise-tolerance threshold of the at least one parameter includes a reliable threshold; and
wherein the signal reliability indicator indicates an unreliability measurement of the physiological signal responsive to the signal quality being greater than the reliable threshold for a first parameter of the at least one parameter.
5. The physiological signal reliability monitor (70) of claim 1,
wherein the at least one noise-tolerance threshold of each parameter includes a reliable threshold and an unreliable threshold;
wherein the signal reliability indicator indicates a reliability measurement of the physiological signal responsive to the signal quality being less than the reliable threshold for the a least one parameter; and
wherein the signal reliability indicator indicates an unreliability measurement of the physiological signal responsive to the signal quality being greater than the unreliable threshold for the at least one parameter.
6. The physiological signal reliability monitor (70) of claim 1 ,
wherein the at least one noise-tolerance threshold of each parameter includes a reliable threshold and an unreliable threshold; and
wherein the signal reliability indicator indicates an uncertainty measurement of the physiological signal responsive to the signal quality being greater than the reliable threshold and less than the unreliable threshold for the at least one parameter.
7. The physiological signal reliability monitor (70) of claim 1 ,
wherein the at least one noise-tolerance threshold of each parameter includes a reliable threshold and an unreliable threshold; and wherein the signal reliability indicator indicates an uncertainty measurement of the physiological signal responsive to the signal quality being less than the reliable threshold for a first parameter of the at least one parameter and further responsive to the signal quality being greater than the reliable threshold and less than the unreliable threshold for a second parameter of the at least one parameter.
8. The physiological signal reliability monitor (70) of claim 1 ,
wherein the at least one noise-tolerance threshold of each parameter includes a reliable threshold and an unreliable threshold; and
wherein the signal reliability indicator indicates an unreliability measurement of the physiological signal responsive to the signal quality being less than the reliable threshold for a first parameter of the at least one parameter and further responsive to the signal quality being greater than the unreliable threshold for a second parameter of the at least one parameter.
9. The physiological signal reliability monitor (70) of claim 1 ,
wherein the at least one noise-tolerance threshold of each parameter includes a reliable threshold and an unreliable threshold; and
wherein the signal reliability indicator indicates an unreliability measurement of the physiological signal responsive to the signal quality being greater than the reliable threshold and less than the unreliable threshold for a first parameter of the at least one parameter and further responsive to the signal quality being greater than the unreliable threshold for a second parameter of the at least one parameter.
10. The physiological signal reliability monitor (70) of claim 1 ,
wherein the signal quality of the physiological signal is a noise measure of the physiological signal derived from a least one of a high-frequency noise component and a low- frequency noise component of the physiological signal.
11. The physiological signal reliability monitor (70) of claim 1 ,
wherein the at least one noise-tolerance threshold of the parameter is statistically derived from noise-dependent variations of corresponding parameters of a plurality of clinical physiological signals.
12. The physiological signal reliability monitor (70) of claim 1 ,
wherein the at least one noise-tolerance threshold defined for the at least one parameter is adaptable to the physiological signal.
13. The physiological signal reliability monitor (70) of claim 14, further comprising:
a parameter noise-tolerance threshold generator controller structurally configured to statistically derived the at least one noise-tolerance threshold of the at least one parameter from noise-dependent variations of corresponding f parameters of a plurality of clinical physiological signals.
14. The physiological signal reliability monitor (70) of claim 13, further comprising:
wherein the parameter noise-tolerance threshold generator controller is further structurally configured to adapt the at least one noise-tolerance threshold defined for the at least one parameter to the physiological signal.
15. The physiological signal reliability monitor (70) of claim 1 , further comprising:
a printer;
wherein the physiological signal reliability controller (90) is further structured configured to control the printer for printing the measurement of the physiological signal and the signal reliability indicator indicative of the reliability of the measurement of the physiological signal derived from the comparison of the signal quality of the physiological signal to the at least one noise-tolerance threshold defined for the at least one parameter of the physiological signal.
16. A physiological signal reliability method, comprising:
obtaining a physiological signal;
extracting a plurality of parameters from a physiological signal;
calculating a signal quality of the physiological signal;
identifying at least one noise-tolerance threshold defined for at least one parameter; comparing the signal quality of the physiological signal to the at least one noise- tolerance threshold defined for the at least one parameter of the physiological signal; and controlling a display (80) for visualizing a measurement of the physiological signal and a signal reliability indicator indicative of a reliability of the measurement of the physiological signal derived from the comparison of the signal quality of the physiological signal to the at least one noise-tolerance threshold defined for the at least one parameter of the physiological signal.
17. The physiological signal reliability method of claim 16,
wherein the at least one noise-tolerance threshold of each parameter includes a reliable threshold;
wherein the signal reliability indicator indicates a reliability measurement of the physiological signal responsive to the signal quality being less than the reliable threshold for the at least one parameter; and
wherein the signal reliability indicator indicates an unreliability measurement of the physiological signal responsive to the signal quality being greater than the reliable threshold for the at least one parameter.
18. The physiological signal reliability method of claim 16,
wherein the at least one noise-tolerance threshold of each parameter includes a reliable threshold and an unreliable threshold;
wherein the signal reliability indicator indicates a reliability measurement of the physiological signal responsive to the signal quality being less than the reliable threshold for the at least one parameter;
wherein the signal reliability indicator indicates an unreliability measurement of the physiological signal responsive to the signal quality being greater than the unreliable threshold for the at least one parameter; and
wherein the signal reliability indicator indicates an uncertainty measurement of the physiological signal responsive to the signal quality being greater than the reliable threshold and less than the unreliable threshold for the at least one parameter.
19. The physiological signal reliability method of claim 16,
wherein the at least one noise-tolerance threshold of the parameter is statistically derived from noise-dependent variations of corresponding variations of parameters of a plurality of physiological signals.
20. The physiological signal reliability method of claim 16,
wherein the signal quality of the physiological signal is a noise measure of the physiological signal derived from a least one of a high-frequency noise component and a frequency noise component of the physiological signal.
PCT/EP2017/058280 2016-04-08 2017-04-06 Method and display for reliability of the real-time measurements of physiological signals WO2017174738A1 (en)

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