SYSTEM AND METHOD FOR ANALYZING PHYSIOLOGICAL SIGNALS - Patent application

Abstract

The present disclosure provides a system for analyzing a physiological signal. The system includes an input configured to receive a discrete-time physiological signal and at least one processor module configured to: compare an indication associated with a current sample value of the physiological signal to an indication associated with a previous sample value of the physiological signal, determine a presence of a reference event if the indication associated with the current sample value differs from the indication associated with the previous sample value, count samples of the physiological signal within intervals defined between the reference events to obtain a respective total number of samples for each interval, and determine at least one physiological parameter based on the total number of samples.

Description

[Technical field]

[0001] Embodiments of the present disclosure relate to a system for analyzing physiological signals, a method for analyzing physiological signals, and a computer-readable storage medium for carrying out the method, particularly to morphological analysis of one or more physiological signals in an implantable medical device (IMD). [Background technology]

[0002] Implantable medical devices are widely used to monitor the health status of patients, for example, by analyzing physiological signals measured by the implantable medical device. Examples of physiological signals that can be measured by implantable medical devices include, but are not limited to, ECG, SpO2, blood pressure, activity, fluid status, inspiratory / expiratory effort, disturbed breathing, and respiration.

[0003] However, it can be difficult to obtain meaningful diagnostic information from the raw signal for a variety of reasons. For example, patients with monitoring-only devices may have more mobility / movement, which can interfere with measurements. In addition, there is usually no single point in time where all physiological processes occur simultaneously, rather than a window or region of time.

[0004] Some devices for analyzing physiological signals use conventional digital filters. However, conventional digital filters have out-of-band suppression characteristics and group delay limitations that lead to distortion of morphological features and ultimately to non-recognition or erroneous measurement of morphological features. Other devices for analyzing physiological signals use non-time domain methods (e.g., statistics and / or frequency domain), such that after the signal is processed, the information per event is lost and the relevant information cannot be analyzed anymore. Furthermore, such processing must collect measurements over time, and therefore early signs of deterioration may be missed. Ultimately, inaccurate estimation of physiological events occurs.

[0005] In light of the foregoing, a system for analyzing a physiological signal, a method for analyzing a physiological signal, and a computer-readable storage medium for performing the method that overcomes at least some of the problems in the art would be beneficial. Summary of the Invention [Problem to be solved by the invention]

[0006] It is an object of the present disclosure to provide a system for analyzing a physiological signal, a method for analyzing a physiological signal, and a computer-readable storage medium for carrying out the method that can improve the analysis of a physiological signal. In particular, it is an object of the present disclosure to accurately determine one or more physiological parameters based on the analysis of a physiological signal. [Means for solving the problem]

[0007] These objects are solved by the features of the independent claims. Preferred embodiments are defined in the dependent claims.

[0008] According to one independent aspect of the present disclosure, a system for analyzing a physiological signal is provided, the device including an input configured to receive a discrete-time physiological signal and at least one processor module. The at least one processor module comprises: - comparing an indication associated with a current sampled value of the physiological signal to an indication associated with a previous sampled value of the physiological signal; - determining the presence of a reference event when an indication associated with a current sample value differs from an indication associated with a previous sample value; - counting samples of the physiological signal within intervals defined between the reference events to obtain a total number of samples for each interval; - configured to determine at least one physiological parameter based on the total number of samples.

[0009] According to some embodiments, which may be combined with other embodiments described herein, at least one processor module comprises: - comparing an indication associated with a current sampled value of the physiological signal to an indication associated with a previous sampled value of the physiological signal; - determining the presence of a reference event when an indication associated with a current sample value differs from an indication associated with a previous sample value; - determining the acceptability of the inclusion or exclusion criteria events in determining the interval or parameter from the status of the flag indicating the passing or failing of the rejection criteria across the analysis interval; - counting samples of the physiological signal within intervals defined between the reference events to obtain a total number of samples for each interval; - configured to determine at least one physiological parameter based on the total number of samples.

[0010] According to some embodiments, which may be combined with other embodiments described herein, the physiological signal is selected from the group including or consisting of an electrocardiogram (ECG), oxygen saturation (SpO2), blood pressure, impedance, acceleration measurements, activity, fluid status, inspiratory and / or expiratory effort, and respiration.

[0011] Preferably, the physiological signal is respiration, which is one specific example of a meaningful physiological signal that may indicate normal respiratory function or a disorder such as COPD, asthma, pulmonary edema, emphysema, sleep apnea, Cheyne-Stroke syndrome, etc.

[0012] Throughout this disclosure, the term "discrete-time physiological signal" refers to a physiological signal having values ​​occurring at separate, discrete times. That is, time is viewed as a discrete variable. A discrete-time physiological signal can be obtained by sampling from a continuous-time [continuously-valued] physiological signal. A discrete-time physiological signal obtained by sampling a sequence at evenly spaced times ("sample values ​​of the physiological signal") has an associated sampling rate.

[0013] The indicia associated with the physiological signal indicate an attribute that is positive (+) or negative (-) relative to some baseline.

[0014] Throughout this disclosure, the term "reference event" refers to a morphological feature in a physiological signal. In particular, a reference event is a distinctive shape, structure, or feature in a physiological signal that can be identified as such and used for further analysis.

[0015] According to some embodiments, which can be combined with other embodiments described herein, the reference event may be a zero crossing or an extremum (minimum or maximum) of the physiological signal.

[0016] According to some embodiments, which may be combined with other embodiments described herein, the reference event includes or is a zero crossing.

[0017] Preferably, the at least one processor module is configured to identify a zero crossing of the baseline-centered signal when an indication associated with a current sample value differs from an indication associated with a previous sample value. For example, a zero crossing may be identified when an indication switches from negative to positive or from positive to negative.

[0018] Preferably, the total number of samples in an interval between two successive zero crossings corresponds to the total number of uninterrupted runs of positive indications (e.g., positive maximal fraction values) or negative indications (e.g., negative maximal fraction values) of sample values ​​in the interval. At each zero crossing, the total number of negative and positive maximal fractions can be summed and stored as a duration at a total number of known sample rates.

[0019] Preferably, at each zero crossing, the sample count for the next local maximum is reset and begins accumulating the sample count again, for example, while the signal is negative, a count of negative samples is accumulated and then latched while the signal is positive.

[0020] According to some embodiments, which may be combined with other embodiments described herein, the reference event includes or is an extreme value.

[0021] Preferably, the at least one processor module is configured to identify an extreme value when an indication associated with a slope of a current sample value differs from an indication associated with a slope of a previous sample value.

[0022] According to some embodiments, which can be combined with other embodiments described herein, the extreme value is a (local) maximum or a (local) minimum. Preferably, the maximum is a peak. Additionally or alternatively, the minimum is a trough.

[0023] Preferably, the interval of the sample count is defined between two successive extreme values ​​of the same type, for example between two peaks or between two troughs.

[0024] Preferably, the total number of samples in the interval between two successive extremes corresponds to the total number of uninterrupted runs of positive slopes from trough to peak (e.g., rising) or negative slopes from peak to trough (e.g., falling) of the sample values ​​in the interval. At each extreme, the total number of rises and falls can be summed and stored as a duration at a total number of known sample rates.

[0025] Preferably, at each extremum, the sample count for the next rise or fall is reset and the sample count begins accumulating again. For example, while the signal is bottoming out, the total number of rising samples is accumulated and then latched when the signal reaches a peak.

[0026] According to some embodiments, which may be combined with other embodiments described herein, the at least one processor module is configured to reject one or more reference events if one or more rejection criteria are met, i.e., the rejected reference events are not used in determining the interval and / or the at least one physiological parameter.

[0027] Preferably, the one or more rejection criteria relate to at least one of patient motion (e.g., based on acceleration data provided by an IMD acceleration sensor), operating limits of at least one signal processing element of the system (e.g., because the sensor input exceeds the undistorted operating limits of a quantizer), non-monotonicity (e.g., rising or falling) and noise (e.g., noise floor).

[0028] According to some embodiments, which may be combined with other embodiments described herein, the system is configured to determine at least one physiological parameter using a total number of samples associated with two or more reference events.

[0029] There may be multiple reference events. Preferably, the two or more reference events are of different types. It all depends on how the intervals are measured to create one full physiological interval. If the intervals span the same type of reference events, then the total number between them may span one full physiological interval. This would be the case if the configuration was set, for example, to measure the physiological interval using only positive zero crossings and to ignore other reference intervals. If the reference events used to measure the intervals are of the opposite type, for example, zero crossings up against zero crossings down, then only one half of the physiological interval is measured and it is necessary to pair it with the last or next zero crossing down to zero crossing up to complete a physiological cycle. The two half phases make up one full physiological cycle, for example, positive time + negative time, another example, rising time + falling time.

[0030] In a preferred embodiment, the baseline analysis is further decomposed into four phases for indication and slope. One example is from trough to trough down to zero crossing up to peak. In this case, four subintervals would be required to sum to yield the duration of one physiological cycle. If the entire physiological cycle is resolved more, measurements will occur within the entire physiological cycle, leading to the x4 statistical utilization point described below.

[0031] Preferably, the two or more reference events are selected from the group including or consisting of an upward zero crossing, a downward zero crossing, a peak, and a trough.

[0032] According to some embodiments, which may be combined with other embodiments described herein, the system is configured to determine at least one physiological parameter for one or more observation windows and / or as required by the standard yield allowed and the number of required standards to meet a resolution requirement that is dependent on respiration rate.

[0033] Preferably, the length of each observation window is based on a set minimum number of samples and / or a maximum processing time. In particular, in one preferred embodiment, a total event number limit (lower bound) is provided, whose value is calculated from the minimum number of samples required to estimate the respiration rate to a resolution of ±2 Bpm with 95% confidence, for example distinguishing between 16 Bpm and 18 Bpm. Secondly, a time limit (upper bound) is provided to limit the baseline analysis from continuing for too long, since the signal attributes are not giving rise to enough accepted baseline events to satisfy the former.

[0034] According to some embodiments, which may be combined with other embodiments described herein, a system includes an implantable medical device.

[0035] Preferably, the implantable medical device is configured to receive or generate a discrete-time physiological signal, for example, the implantable medical device includes or is connected to one or more sensors configured to measure the discrete-time physiological signal or the physiological signal from which the discrete-time physiological signal is derived.

[0036] Preferably, the implantable medical device is configured to perform at least the following aspects: comparing signatures, determining the presence of a reference event, and counting samples.

[0037] In some embodiments, the implantable medical device can be configured to determine at least one physiological parameter based on the total number of samples. In particular, the analysis can be implemented entirely in the implantable medical device. Alternatively, the implantable medical device can provide a baseline analysis output to an external entity (e.g., a server or other device), and the calculation of the physiological parameter can be completed entirely or partially in some other manner, e.g., in the server or other device.

[0038] According to some embodiments, which may be combined with other embodiments described herein, the system is configured to transmit the determined at least one physiological parameter to at least one external entity via a communication interface of the system, in particular a communication interface of the implantable medical device.

[0039] Preferably, the at least one external entity is the physician's mobile terminal and / or the physician's web (e.g., a website and / or web interface) and / or a web service used by the physician, e.g. a remote server having a physician-facing web interface.

[0040] Preferably, the physician's mobile device is selected from the group including (or consisting of): a smartphone, a personal digital assistant, a tablet, a notebook, a smart watch, any device with a web browser, and smart glasses.

[0041] Preferably, the communication interface is a wireless communication interface, however, the disclosure is not limited thereto and the communication interface may be a wired communication interface.

[0042] According to some embodiments, which may be combined with other embodiments described herein, the communication interface is configured for communication over at least one communication network, in particular the Internet.

[0043] Preferably, the at least one communication network includes or is a local area network and / or a wide area network.

[0044] Preferably, the wide area network is configured for at least one of Global System for Mobile Communications (GSM), General Package Radio Service (GPRS), Enhanced Data Rate for GSM Evolution (EDGE), Universal Mobile Telecommunications System (UMTS), Long-Term Evolution (LTE), and Fifth Generation Technology Standard (5G).

[0045] According to another independent aspect of the present disclosure, a method for analyzing a physiological signal is provided, the method comprising: - receiving a discrete-time physiological signal; - comparing an indication associated with a current sampled value of the physiological signal with an indication associated with a previous sampled value of the physiological signal; - determining the presence of a reference event when an indication associated with a current sample value differs from an indication associated with a previous sample value; - counting samples of the physiological signal within intervals defined between reference events to obtain a respective total number of samples for each interval; - determining at least one physiological parameter based on the total number of samples.

[0046] According to some embodiments, which may be combined with other embodiments described herein, the system and / or method includes a control mechanism that has the ability to select any subset of the four analysis criteria events and, in preferred embodiments, enable or disable any subset of the four rejection criteria.

[0047] The embodiments are also directed to systems / devices for implementing the disclosed methods, including systems / devices for performing each described method aspect. These method aspects may be performed using hardware components, a computer programmed by appropriate software, any combination of the two, or in any other manner. Furthermore, embodiments according to the invention are also directed to methods for operating the described devices / systems. The present disclosure includes method aspects for implementing any functionality of the devices / systems.

[0048] According to another independent aspect of the present disclosure, a machine-readable medium is provided that includes instructions executable by one or more processors to implement a method for analyzing physiological signals of an embodiment of the present disclosure.

[0049] The (e.g., non-transitory) machine-readable medium may include, for example, optical media, such as CD-ROMs and digital video disks (DVDs), and semiconductor memories, such as Electrically Programmable Read-Only Memory (EPROM) and Electrically Erasable Programmable Read-Only Memory (EEPROM). The machine-readable medium may be used to tangibly retain computer program instructions or code organized into one or more modules and written in any desired computer programming language. When executed, for example, by one or more processors, such computer program code may implement one or more of the methods described herein.

[0050] According to another independent aspect of the present disclosure, a device or system for analyzing a physiological signal is provided, the device or system including one or more processors and a memory (e.g., a machine-readable medium as described above) coupled to the one or more processors and including instructions executable by the one or more processors to implement a method for analyzing a physiological signal of an embodiment of the present disclosure.

[0051] According to some embodiments, which may be combined with other embodiments described herein, the device and / or system includes means for analyzing the physiological signal measuring the entire physiological period, or a half or quarter phase, or some other fragment corresponding to a selected reference point, such that there are multiple overlapping analysis intervals per entire physiological period yielding higher statistical power in the estimated parameters.

[0052] According to some embodiments, which may be combined with other embodiments described herein, the device and / or system includes means for analyzing the physiological signal to preserve the fidelity of the physiological signal, evaluating the signal for departures from its expected shape, e.g., non-monotonicity, and setting an indicator if the signal deviates from its expectations for each fraction of the entire period.

[0053] According to some embodiments, which may be combined with other embodiments described herein, the device and / or system includes means for analyzing physiological signals evaluating against configurable morphological or interference rejection standards for whole, half, quarter, or fractional periods, selecting which overlapping analysis intervals are included or omitted from contributing to the estimated parameters.

[0054] According to some embodiments, which may be combined with other embodiments described herein, the device and / or system includes means for analyzing the physiological signal evaluating against statistics or quality metrics for each whole, half, quarter, or fraction of a period, selecting which overlapping analysis intervals to include or omit from contributing to the estimated parameters.

[0055] According to some embodiments, which may be combined with other embodiments described herein, the device and / or system includes means for analyzing the physiological signal, proactively, adaptively, or retroactively selecting a subset of at least one sensor input or a combination of at least two sensor inputs from a plurality of sensor inputs having the best statistical attributes or quality metrics for additional observations, calculating parameters, and storing or reporting results to a server.

[0056] According to some embodiments, which may be combined with other embodiments described herein, the device and / or system includes means for analyzing the physiological signal to preserve the fidelity of the physiological signal, assessing departures from its expected rate of change, e.g., sighing, shortness of breath, or other respiratory irregularities, and setting an indicator if the signal deviates from its expectations for each fraction of the entire period.

[0057] So that the foregoing features of the present disclosure may be understood in detail, a more particular description of the present disclosure, briefly summarized above, may be had by reference to the following embodiments, the accompanying drawings of which relate to the embodiments of the present disclosure and which are described below. [Brief description of the drawings]

[0058] [Figure 1] FIG. 1 illustrates an exemplary generation of a zero-crossing criterion according to an embodiment of the present disclosure. [Diagram 2] FIG. 1 illustrates an example calculation of a zero-crossing duration according to an embodiment of the present disclosure. [Diagram 3] FIG. 2 illustrates a zero-crossing unit according to an embodiment of the present disclosure. [Figure 4] FIG. 1 illustrates an exemplary generation of extremum criteria according to an embodiment of the present disclosure. [Diagram 5] FIG. 1 illustrates an exemplary calculation of extremum duration according to an embodiment of the present disclosure. [Figure 6] FIG. 1 illustrates an extreme value unit according to an embodiment of the present disclosure. [Figure 7] FIG. 2 illustrates a storage and observation control unit according to an embodiment of the present disclosure. [Figure 8] FIG. 1 shows criteria events and their effective rates per minute. [Figure 9] FIG. 2 illustrates a reference control unit according to an embodiment of the present disclosure. [Figure 10] FIG. 13 illustrates overlap between types of reference intervals.

[0059] Reference will now be made in detail to various embodiments of the present disclosure, one or more examples of which are illustrated in the figures. In the following description of the drawings, like reference numerals refer to like components. Generally, only the differences with respect to the individual embodiments are described. Each example is provided as an explanation of the disclosure and is not intended as a limitation of the disclosure. Moreover, features illustrated or described as part of one embodiment can be used with or in conjunction with other embodiments to yield still further embodiments. It is intended that the description include such modifications and variations.

[0060] Implantable medical devices are widely used to monitor the health status of patients, for example, by analyzing physiological signals measured by the implantable medical device. However, it can be difficult to derive meaningful diagnostic information from the raw signals for at least some of the following reasons: - Patients with monitoring-only devices may have more mobility / movement which may interfere with measurements. - There is usually no single time at which all physiological processes occur simultaneously. The optimal subset of sensors needed for diagnosis or tracking of a health condition varies with implantation location and patient resting position. - There are morphological features in the physiological data that resemble false peaks, maxima, or minima. - There is device input that is noise or interference that falsely resembles or obscures the actual morphological features. - Intermittent respiratory sighs have a larger volume, which is expressed as a higher amplitude in the sensor output that can exceed the undistorted operating limits of some signal processing chains. Here, the point can be that the AutoGain function must leave some operating margin and / or guard band for expected physiological changes in amplitude so that the signal level remains at the undistorted operating limits of the signal processing pathway. AutoGain improves the individual gain functions by updating the gain locally for each speed range. It splits the movement into partial movements to update the profile of the aiming error, which it tries to reduce by adapting the gain response. - Heart failure can be manifested by as little as a change in two breaths per minute (Bpm) from baseline and is therefore difficult to detect.

[0061] Additionally, an increase in respiratory sigh or a change in sigh duration (maximum of amplitude difference) can be a means to determine a change in an individual's physiological state.

[0062] The embodiments of the present disclosure provide improved morphological analysis of physiological signals. In particular, the embodiments of the present disclosure analyze the morphology of physiological signals available through sensors in an IMD to yield information about a patient's health status, particularly attributes of respiration. In particular, signatures of the discrete-time physiological signals are analyzed to determine reference events, which in turn are used to determine physiological parameters indicative of the patient's health status.

[0063] In particular, a system for analyzing a physiological signal according to the present disclosure includes an input configured to receive a discrete-time physiological signal and at least one processor module. The physiological signal may be an electrocardiogram (ECG), oxygen saturation (SpO2), blood pressure, impedance, accelerometry, activity, fluid status, inspiratory and / or expiratory effort, or respiration. Preferably, the physiological signal is respiration.

[0064] At least one processor module is configured to compare an indication (positive or negative) associated with a current sampled value of the physiological signal (e.g., respiration) with an indication associated with a previous sampled value of the physiological signal, determine a presence of a reference event (e.g., a zero crossing or an extreme value) when the indication associated with the current sampled value differs from the indication associated with the previous sampled value, count samples of the physiological signal within intervals defined between the reference events to obtain a respective total number of samples for each interval (an interval may be one full, two halves, or four phases of an entire physiological cycle), and determine at least one physiological parameter (e.g., respiratory effort, respiratory dysfunction, and / or respiratory interruption) based on the total number of samples.

[0065] Thus, the indications of the discrete-time physiological signals are analyzed to determine reference events, which in turn are used to determine physiological parameters indicative of the patient's health status. For example, a device for analyzing physiological signals can accept intracardiac electrograms (IEGMs), continuous electrocardiograms (SECGs), impedance, accelerometer data, and / or pressure input streams, quantize them, precondition the inputs in a non-distorting manner, select observation times (particularly those that reveal heart failure), optimize a subset of the inputs, analyze reference events and extract relationships between them, process disturbing or confounding factors, generate output including statistics and quality metrics, and upload the results to a server.

[0066] Thereby, meaningful measurements of physiological parameters such as respiration, respiratory effort, respiratory dysfunction and respiratory interruption can be provided that can be used to monitor, track or diagnose the occurrence of diseases such as heart failure (HF).

[0067] Below is an overview of a process for determining physiological parameters, where the process implements a reference event analysis of an embodiment of the present disclosure.

[0068] Overview: A. Synchronizing observations to time or physiological parameters B. Finding Sensor Offset and Gain Settings C. Signal Preconditioning to Remove Residual Offset and Out-of-Band Signal Content D. Reference Algorithms for Producing Interval Data, Statistics, and Quality Metrics E. Prediction selection from multiple sensors using statistics and quality metrics. F. Determining Physiological Parameters from Accepted Interval Data

[0069] detail: A. Synchronizing observations to time or physiological parameters An embodiment of the present disclosure can determine expected daily cardiopulmonary, metabolic, or autonomic dysfunction and indicate optimal times for measurement by sampling inputs based on time of day, heart rate, heart rate difference, autonomic tone, temperature, movement, posture, or pressure indicators, or a subset thereof. Measurements can thus be triggered by combinations of these, or be put on standby or paused until any of these subsets subside.

[0070] B. Finding Sensor Offset and Gain Settings An embodiment of the present disclosure can determine or adjust real-time configurable parameters of the sensor unit, such as DC offset and gain, by bidirectional means, such as Fibonacci search, so that the sensor output signal level remains within undistorted operating limits. Additionally, the output level can be guardbanded in fixed or adaptive cases, such that the signal level remains so despite position, movement, or physiological changes, such as spontaneous enhanced breathing (sighing).

[0071] C. Signal Preconditioning to Remove Residual Offset and Out-of-Band Signal Content Embodiments of the present disclosure can precondition the inputs to approximate zero mean and reduce out-of-band signal content by means, such as a wavelet filter that preserves fidelity to the morphology of the subject, so that actual time relationships between criteria can be determined, including, but not limited to, times and slopes of zero crossings, times and amplitudes of peaks and troughs, areas under maxima, rising and falling boundaries of maxima, and the areas and percentages of these.

[0072] D. Reference Algorithms for Producing Interval Data, Statistics, and Quality Metrics a) An embodiment of the present disclosure can accept pre-conditioned inputs and evaluate criteria event intervals, amplitudes, slopes and extrema, each associated with a time index, sample value and duration, a concept that is described in more detail below. b) An embodiment of the present disclosure can evaluate the preconditioned inputs and correlate each reference event condition against a set of rejection standards and tag each with a flag indicating noise floor, non-monotonic signal change, rail or motion flag. This concept is described in more detail below. c) An embodiment of the present disclosure further divides an entire physiological interval so that sub-intervals of analysis overlap the entire interval multiple times, increasing the power of the estimation. For example, a single physiological cycle, e.g., breath or cardiac interval, N times, where N separate criteria are analyzed, for example, each breath interval can be oversampled by 4 analysis intervals: 2 zero crossing intervals (1 zero crossing above, 1 zero crossing below), 2 extreme intervals (1 peak to peak, 1 trough to trough), thereby reducing the uncertainty around the estimation by half.

[0073] E. Predictive Selection from Multiple Sensors Embodiments of the present disclosure can accept pre-conditioned inputs and proactively determine which of the sensors, alone or in a combined subset, best represents the subject's physiological signal using a combination of references, rejection standards, or quality metrics.

[0074] F. Determination of Physiological Parameters a) Embodiments of the present disclosure can select which criteria or rejection criteria to include or exclude based on a set of predefined settings, or can adaptively change the selection without changing the signal attributes. b) An embodiment of the present disclosure can collect observations from those criteria and rejection standards selected by configuration settings and provide instantaneous or estimated values ​​from the aggregate. c) An embodiment of the present disclosure can pause or stop an ongoing measurement when confounding signals occur or an excessive number of rejection standards are detected, whereupon features are readjusted and / or the measurement is canceled, paused and resumed, or rescheduled when the interfering signals subside. d) Embodiments of the present disclosure can provide estimates of central tendency, variance, and confidence limits of measurements for accepted observations. e) An embodiment of the present disclosure can determine metrics of the quality of the collected data, for example, the ratio of accepted to total observations. f) An embodiment of the present disclosure can determine if the resolution of the measurement is sufficient, for example, to answer the question "Have the required number of observations N been obtained and accepted (and / or assessed) that a respiration rate of X±Y (Bpm) can be reported with 95% confidence and quality metrics?"

[0075] The morphological analysis described above in steps A-F may account for at least some of the following aspects: - Preservation of the fidelity of the signal morphology so that the actual time relationships between signal features can be determined. - Compensation for patient movement or position or physiological changes so that the signal continues to contain physiologically significant and comparable information. - Incorporation of multiple cues of daily nadir (minimum) of sympathetic nerve activity. - Selection from available sensors to provide coverage during the measurement time for a given pose. - Analysis of multiple criteria for each event to provide statistical power of the analysis. - Customizable retention and rejection of analytical events to ensure robustness of results. - Analyzing single events in multiple ways to increase the statistical power of the estimator. - Customization of which analysis events are omitted or retained to increase efficiency. - Device cost protection: Pause, standby, reschedule, or skip analyses when confounding factors arise. - Instantaneous or collected observations. - Providing statistical estimates of the calculated parameters. - Providing metrics of measurement quality and resolution.

[0076] Step D, criteria analysis, is described in more detail below.

[0077] According to some embodiments, which can be combined with other embodiments described herein, the reference event may be a zero crossing or an extremum (minimum or maximum) of the physiological signal.

[0078] 1. Zero-crossing reference event The steps of generating the zero-crossing reference event of the analysis are illustrated in Fig. 1. In the upper subplot, an example waveform is shown whose amplitude is within the undistorted operating range of the signal path versus the time scale of seconds. The central subplot shows the indications of each input sample, where the first line 100 corresponds to the indication of the current sample and the second line 102 corresponds to the indication of the previous sample. The lower subplot shows the identification of the zero-crossing reference event ZC when the current indication and the previous indication differ, where +1 is up and -1 is down, respectively.

[0079] A zero-crossing reference event interval duration is calculated for each identified zero crossing (upper subplot of FIG. 2). The middle subplot shows that the number of samples during which the sign of the signal remains the same is counted, where a first line 200 corresponds to the total number of uninterrupted runs of negative maximal subvalues ​​and a second line 202 to the total number of positive maximal subvalues. At each zero crossing, the total number of negative and positive maximal subvalues ​​are summed and stored as a duration at a total number of known sample rates (lower subplot).

[0080] At each zero crossing, the sample count for the next local maximum is reset and begins accumulating the sample counts again. For example, the negative sample counts were accumulated while the signal was negative between 2 seconds and 4 seconds, then latched while the signal was positive between 4 seconds and 6 seconds, summed with the positive sample count at the downward zero crossing (-1) at 6 seconds, and started counting the negative samples again. In this example, the positive sample count was latched at 2 seconds as the signal went negative, summed with the negative sample count at 4 seconds, restarted, and latched at 6 seconds as the signal went negative.

[0081] As shown in FIG. 3, in one exemplary embodiment, the foregoing is accomplished by the following means.

[0082] Example IRQ: In one embodiment, the workflow goes into the zero crossing unit (Figure 3) in real time on a sample by sample basis. The system has analog sensors that are sampled resulting in separate valuable data streams at separate times. The system issues an interrupt when a sample is available (sample IRQ).

[0083] nxtVal: The sample value (nxtVal*) is taken from the quantizer output register. The variables marked with an asterisk will be saved later in the step store*. The test if nxtVal is negative is the entry point of the zero-crossing unit (Figure 1). The crossing (Xing) unit must evaluate the negative maxima (left branch) and the positive maxima (right branch).

[0084] nxtSgn: The sign of nxtVal is assigned as positive or negative (nxtSgn).

[0085] motnFlg: The system has an accelerometer and a unit for generating motion flags which are sampled and if motion is active the state is set (nxtNegMotnRjc / nxtPosMotnRjc).

[0086] railFlg: Since the sensor input may exceed the undistorted operating limits of the quantizer, the rail flags are sampled and their state is latched if the signal input has reached the operating limits (nxtNegRailRjc / nxtPosRailRjc).

[0087] prmThr: If the amplitude of the negative and positive maxima exceeds the noise floor (prmThr), the prominence rejection flag of the respective maxima is cleared nxtNegRjc / nxtPosRjc, otherwise it remains in the true state. In other words, an amplitude is assumed to be indistinguishable from noise until its amplitude indicates otherwise.

[0088] IstVal: If the workflow is now on the negative or positive nxtVal branch, an indication of the last value (IstVal) is called back to determine if the current sample has crossed zero.

[0089] Negative / Positive: If both previous (IstVal) and current (nxtVal) have the same indication, the fiducial identifier (xngFid), duration of the analysis interval (xngDur), prominence, rail, and motion rejection flags are cleared (xngPrmRjc, xngRailRjc, and xngMotnRjc). If the workflow is in the negative branch, the total number of negative samples is maintained. Otherwise, if the workflow is in the positive branch, the total number of positive samples is incremented.

[0090] Lower / upper zero crossings: If the previous (IstVal) and current (nxtVal) sample values ​​have different signs, a zero crossing has been identified. Upon a zero crossing event, the following steps may be performed: a reference identifier is assigned (xngFid*) and the duration (xngDur*) of this analysis interval (the last zero crossing in the same direction relative to the current zero crossing) is calculated.

[0091] Rejection Criteria: The rejection criteria xngPrmRjc*, xngRailRjc*, and xngMotnRjc* are tested and set to true if a negative or positive flag is set for the respective lifetime rejection criteria. At this point, a storing operation is performed on any variables that have been starred up to this point, where storing includes buffering in an array or collecting in an accumulator. The storing operation may be further filtered to omit any data that has any subset of possible criteria identifiers, lifetimes that are considered invalid, or any subset of rejection flags.

[0092] Remember*: The variables are stored at this point.

[0093] FIG. 10 shows that after an analysis interval is completed, the relevant breath duration, reference sample values, and other status data are stored. When evaluating respiration as half of a full physiological interval, the leading (next) data is stored as the (last) trailing data. Thus, for example, if a reject flag occurs at 7 seconds and 9 seconds and the prominence duration is stored at 9 seconds, the rejected data is carried over to the corresponding (last) trailing data since it is within the length of the prominence duration stored at 11 seconds. After this, the reject flag of the trailing prominence data is allowed to expire. The same applies for the zero crossing duration.

[0094] Saving for extreme values: The approaching extremum event follows and partially overlaps with the current zero-crossing analysis interval, therefore the analysis rejection states of the noise floor (xngPrmRjc), rail (xngRailRjc), and motion (xngMotnRjc) are saved for the approaching extremum in xtrPrmRjc, xtrRailRjc, and xtrMotnRjc, respectively.

[0095] Save for the rear end max: The zero crossing is the end of a maxima and saves the status of the rejection flags of the trailing maxima of the approaching analysis event. The current noise floor rejection flags (nxtPosRjc / nxtNegRjc), rail flags (nxtNegRailRjc / nxtPosRailRjc), and motion rejection flags (nxtNegMotnRjc / nxtPosMotnRjc) are saved in their trailing counterparts (IstPosRjc / IstNegRjc), (IstNegRailRjc / IstPosRailRjc), and (IstNegMotnRjc / IstPosMotnRjc), respectively.

[0096] Next max reset: The zero crossing is the beginning of a local maxima and the noise floor rejection flags (nxtNegRjc / nxtPosRjc) are set and assumed true until the signal magnitude exceeds the threshold and the rail (nxtPosRailRjc / nxtNegRailRjc) and motion rejection (nxtPosMotnRjc / nxtNegMotnRjc) flags are cleared.

[0097] Reset the (negative / positive) sample count: The zero crossing is the beginning of a maxima and restarts the total number of samples in the negative (negCnt) and positive (posCnt) maxima.

[0098] 2. Extreme Criterion Events The steps of generating the extreme reference events of the analysis are illustrated in Fig. 4. In the upper subplot, an example waveform is shown whose amplitude is within the undistorted operating range of the signal path on a time scale of seconds. The middle subplot shows the slope of each input sample up to an extremum, where the first line 400 corresponds to the slope of the current sample and the second line 402 corresponds to the slope of the previous sample. The lower subplot shows the identification of extreme reference events when the current and previous slopes differ in the signal extremum, where +1 is the peak and -1 is the trough, respectively.

[0099] An extremum reference event interval duration is calculated for each identified extremum. The upper subplot of FIG. 5 shows waypoints for latching and holding the duration of the peak P to trough T decline. The center subplot shows free running (reference number 500) and latched (reference number 502) decline counters. For example, when the signal leaves peak P (the current (reference number 500) and previous (reference number 502) slope indications differ at 5 seconds in the upper subplot), a running decline counter starts (reference number 500 in the center subplot). When the signal crosses zero (6 seconds), the latched decline counter tracks the free running counter. The latched decline counter stops tracking the free running counter when the signal nadir is reached (7 seconds). When trough T is reached, the total number of previous rises (trough to peak) and the total number of current declines (peak to trough) are summed, yielding the trough to trough duration.

[0100] As shown in FIG. 6, in one exemplary embodiment, the foregoing is accomplished by the following means.

[0101] xtrDur: When the sample is completed through the zero crossing unit, the workflow enters the gradient unit (Figure 6).

[0102] nxtSgn: A workflow branch regarding whether nxtSgn indicates that the local majority is negative or positive.

[0103] Negative / positive maximum: If the maxima are negative, the total number of samples of the gradient dip is maintained, otherwise, if the maxima are positive, the total number of samples of the gradient rise is increased.

[0104] Non-monotonic rejection (post-zero crossing phase): If the workflow is in a downward path and a positive slope occurs, a flag to reject the downward movement (nxtDscRjc) will be latched. Encountering a negative slope on an upward path will latch an upward rejection flag (nxtAscRjc). Alternatively, the non-monotonic amplitude can be tested against a threshold to allow for a non-zero noise floor. Also, alternatively, the rejection selector can be configured or adapted to ignore non-monotonicity. Note that a zero slope is a plateau and is not considered non-monotonic.

[0105] plateau: If the workflow is in a negative maximal branch and the gradient is not negative, the workflow proceeds on a plateau path, clearing the identifier and skipping the search for a deeper nadir. This path is also taken by an ascending leg after the deepest nadir of the analysis interval, so the ascending total is incremented. If the workflow is in a positive maximal branch and the gradient is not positive, the workflow proceeds on a plateau path, clearing the nxtSlp identifier and skipping the search for a higher peak. This path is also taken by a descending leg after the highest peak of the analysis interval, so the descending total is incremented.

[0106] Negative / Positive Gradient: The slope (nxtSlp) is determined to be negative or positive by testing the relationship between IstVal and nxtVal. If the current sample is also a zero crossing (xngFid) that matches the slope, or if the current sample is a deeper nadir or higher peak, the current value is stored as a possible extreme value for this analysis interval. With rapid physiological rates approaching the upper band limit, the signal value may represent the extreme value of only a single sample. The current sample count of the falling or rising limb (IstRjc / IstRjc) is cached, and the rejection status of the non-monotonic change in that limb (nxtDscRjc / nxtAscRjc) is cached (IstDscRjc / IstAscRjc). The sample count and non-monotonic rejection of the approaching rising or falling limb are cleared by the deepening nadir and higher peak, since the corresponding new limb begins when the nadir or peak is reached.

[0107] Non-monotonic rejection (post-extreme phase): If the current sample follows an extremum (nadir / apex) and the sign (nextSgn) and slope of the extremum match, a non-monotonic rejection flag will be latched in the corresponding leg (nxtAscRjc / nxtDscRjc). Note that extrema of larger magnitude may clear this flag.

[0108] Extreme troughs / peaks: For each sample, the relationship between the previous slope and the current slope (IstSlp / nxtSlp) is tested to determine if an extremum has occurred. Note that nxtSlp may also be zero, allowing for extremums with a plateau instead of a single sample apex. When identified, the prominence is identified as a trough or peak (prmFid). At this point, the length of time of the previous and current slope legs is calculated (prmDur) as they constitute one of two analysis intervals between extrema, the criteria of which are trough-rising leg-falling leg-trough or peak-falling leg-rising leg-peak. The rejection criteria of the rising and falling legs are tested and associated with the current analysis interval (xtrSlpRjc).

[0109] Larger magnitude extremes: If the current extremum (prmFid) and the previous extremum (last stored prmFid) are the same, the extremums they reached (current and stored ndrVal / apxVal) are compared to see if the current extremum has a greater magnitude. If the magnitude is greater, the current value overwrites or replaces the last value, otherwise the current value is discarded or discounted. If the current extremum (prmFid) and the previous extremum (last stored prmFid) (indication of) are different, then the current value is stored since it is the first. In an alternative embodiment, if the morphology is still monotonic, the prmFid value is incremented at each extremum of greater magnitude as a metric to characterize the smoothness of the decline and rise.

[0110] xngSlpRjc: This value of xtrSlpRjec is cached for approaching zero crossings.

[0111] Having described the zero crossings, extremes, and multiple analysis intervals across a physiological interval of the reference algorithm (Step D), the storage, calculation, and control aspects (Steps E and F) will now be described in more detail below with reference to Figures 7, 8, and 9.

[0112] strLst: The aforementioned tests depend on the relationship of the results achieved with respect to the current and previous samples, and therefore nxtVal, nxtSgn, and nxtSlp are stored by the storage unit in IstVal, IstSgn, and IstSlp, respectively, for the approaching analysis event (Figure 7).

[0113] Stored criteria analysis events: In a preferred embodiment, store* results in a cache of four types of fiducial analysis events and their corresponding collected information: two types of zero crossings (up and down), and two types of extrema (peak and trough). Associated data for each fiducial event is the sample number (sx), a fiducial value (xngFid / prmFid) identifying the type and direction, the maximum magnitude that occurred during the fiducial interval (nxtVal / ndrVal / apxVal), the duration of the previous and current fiducial interval (xngDur / prmDur), and zero crossing and extrema interval rejection flags for the noise floor (PrmRjc), non-monotonicity (SlpRjc), signal level bordering the clamp limits (RailRjc), and motion flags (MotnRjc).

[0114] [Table 1]

[0115] Observation Restrictions: Upon conclusion of the sample IRQ, it can be tested whether the observation window is completed by exceeding a time limit or whether the total number of analysis criteria events is sufficient to calculate post-observation parameters, statistics, and / or quality metrics. If the observation window is not completed, execution returns to the system. If not, the sample IRQ is disabled and program flow proceeds to the post-observation step (FIG. 7).

[0116] start: In a preferred embodiment, the workflow is initiated in the reference control unit on a time IRQ indicating the beginning of an expected quiet period (left hand side in FIG. 9). Alternatively, some physiological condition or external trigger (patient, clinician, auditory detection of disturbed breathing) may activate the initiation.

[0117] Disruption conditions: It is then checked that the actual heart rate does not exceed the resting limit. The exercise flag is also checked to ensure that there is no transient physical activity as it may result in a delayed chronotropic response falling during the approaching observation window. In an alternative embodiment, temperature may also be used to indicate the daily metabolic nadir.

[0118] stand-by: If the data does not meet the quiesce standard and standby is the enabled response, the gating inputs continue to be monitored as they refresh and restart the workflow when they stop.

[0119] stand-by: If standby is not an enabled response and is not to reduce the energy consumption of the IMD, it may be checked whether a configurable amount of time (e.g., 15 minutes) of waiting will be outside the end of the quiet period. If so, the reference control stops until the next scheduled quiet period begins. If not, the reference control resumes monitoring the gating input for refresh after the wait. In the latter case, some attempts may be made to measure the physiological signal during the quiet period.

[0120] choice: If the workflow passes the obstruction condition gate, an input sensor may be selected. The sensor may be configurable to a fixed, e.g., a single preferred axis of a three-axis accelerometer. Alternatively, the selection may be a predictive assessment of which of the three accelerometer axes gives the greatest magnitude physiological signal, e.g., the tidal amplitude of the respiratory signal. As a further alternative, multiple signals may be arithmetically combined for a single composite waveform for analysis. As a further alternative, multiple signals from different sensor types may be multiplexed and their separate results used to corroborate, condition or improve an estimate of a calculated value, e.g., respiration rate.

[0121] Observation window restrictions: There can be two observation window limits. First, a total number of events limit (lower bound) is provided, the value of which is calculated from the minimum number of samples required to estimate the respiration rate to a resolution of ±X Bpm (e.g. ±2 Bpm) with a confidence of YY% (e.g. 95%), distinguishing between 16 Bpm and 18 Bpm, in a preferred embodiment. Second, a time limit (upper bound) is provided to limit the reference analysis from continuing too long, since the signal attributes have not produced enough accepted reference events to satisfy the former. Thus, lowering the device energy consumption, the observation window may be completed if a sufficient number of accepted events occurs before the time expires. Alternatively, parameters may be calculated sequentially or incrementally and are partially or completely available for storage or reporting upon completion of observation. For example, the analysis reference interval may indicate a temporary effect outside the expected rate of change, such as sighing, shortness of breath, or other breathing irregularities or disturbed breathing patterns. In this case, an indicator will be set that the signal deviates from such expectations.

[0122] Example IRQ enable: This activates the sample-by-sample processing of the signal stream in the zero-crossing unit (Figure 3).

[0123] The determination of the physiological parameters in step G will now be described in more detail below with reference to FIGS.

[0124] Observation completed: The sample-by-sample analysis of the reference events is concluded with the completion of the observation (Figure 7) and the workflow moves to the post-observation window analysis (right hand side in Figure 9). The post-observation window analysis is performed on the stored reference analysis events for the entire observation window.

[0125] Observation window rate per minute: The total number of accepted and rejected analytical events configured to be included for analysis are counted. If the sensor was used to measure respiration rate, the durations in the sample are summed and then normalized to allow conversion to a per minute rate (FIG. 8). ax=i|[ZeroXingUp i ;ZeroXingDown i Apex i ;Nadir i ]∈Accepted rx=i|[ZeroXingUp i ,ZeroXingDown i ,Apex i ,Nadir i ]∈Rejected Counting 受け入れ済み = Count(ax) Counting 拒絶済み =Count(rx) Duration 合計 =Σ interval ax Duration 正規化済み = Duration 合計 / Counting 受け入れ済み rate 息 =60 / (lifetime 正規化済み / frequency サンプリング )

[0126] Alternatively, the respiration rate can be calculated by inferring the respiration frequency from the amplitude of the extrema and the slope of the breath signature at the zero crossings as follows: S0=(y2-y1) / T s , where S0 is the gradient at the zero crossing and T s is the sample rate. S0=A0·2·π·f0, where A0 is the breath amplitude and f0 is the breath frequency, given that T0=1 / (2·π·f0). rate 息 =60 / ((A0 2 π) / S0)

[0127] Sufficient observations for an observation window Quality metrics: The sufficient observations quality metric, in a preferred embodiment, is the required number of observations in a sample to be able to distinguish between two respiration rates with a resolution of ±2 Bpm with 95% confidence. In an alternative embodiment, the resolution is ±1 Bpm with 90% confidence. Additionally, the sufficient observations quality metric is constrained to values ​​within [0%...100%]. Counting 必要 =ceil(8 / (((60 / (rate 息 +2Bpm))-(60 / Rate 息 )) 2 )) Counting 必要 =max(count 必要 ,1) quality 十分 =min(1,count 受け入れ済み / Counting 必要 )

[0128] Observation window percentage quality metrics: The percentage quality metric is the ratio of accepted to all observations in the measurement window. In a preferred embodiment, this quality metric is constrained to a value within [0%...50%,100%], where if the accepted observations are the majority, the quality metric is a pass. ratio 受け入れ =Count 受け入れ済み / (Count 受け入れ済み +Counting 拒絶済み ) quality 受け入れ =max((50%<percentage 受け入れ ),ratio 受け入れ )

[0129] Satisfactory quality metrics: If the conjunction of the two quality metrics is equal to 100%, the respiration rate will be stored in the trending device and queued for uploading to the service center. In an alternative embodiment, one or more of the quality metrics, rejection standards, or other data in the stored criteria analysis event may accompany the upload.

[0130] Confidence interval for ventilation amplitude in the observation window: The average amplitude of accepted reference events configured to be included for analysis may be calculated. The amplitudes are summed and then normalized to allow conversion to an average over the observation window. Additionally, a preferred embodiment calculates the mean absolute deviation of the amplitude (AAD amplitude) from the magnitude of the accepted events. The standard deviation can be calculated by rescaling the AAD. These measures of difference around a central point may be used to identify disturbed breathing when the tidal magnitude is outside the confidence limits. The observation window for establishing the confidence interval may be required to have an uninterrupted length of the accepted analysis reference events to establish a reference (FIG. 8). amplitude 平均 =(Σ|amplitude ax |) / Count 受け入れ済み AAD 振幅 =(Σ|amplitude ax |-amplitude 平均 ) / Count 受け入れ済み

number

[0131] In view of the foregoing, embodiments of the present disclosure provide one or more of the following advantages: - The ability to resolve morphological features that provide clinicians with measures beyond simply event rate, e.g., tidal ventilation, minute ventilation, inhalation-exhalation (IE) ratio, etc. - Embodiments of the present disclosure have the ability to trade off multiple strategies to optimize space-time critical low power measurements against extended measurement times for more robust estimation of physiological measures by choosing between periodic or triggered initiation, early selection of priority sensors or assessment of all sensors, and selection by line of sight algorithms, measurement standby, pause / resume, or deferral, etc. - The disclosed embodiments reject events based on morphological standards and quality metrics, and overlay analysis criteria events to increase the accuracy and statistical power of the results. No parameter tuning is required. The embodiments of the present disclosure provide a beneficial approach to select the most significant and nearest extremes to prevent false peaks and troughs, i.e., by updating the trailing edge limits with the highest magnitude found for each analysis interval. -Embodiments of the present disclosure are less computationally intensive than spectral / frequency analysis techniques. - The embodiments of the present disclosure are robust to non-stationarity or autonomous differences in physiological processes. - The embodiments of the present disclosure are able to resolve events by event (instantaneous) changes in physiological processes. -Embodiments of the present disclosure may be applied to physiological detection from IMDs other than respiration, such as oxygen saturation changes or blood pressure changes from on-board sensors.

[0132] While the forgoing is directed to embodiments of the present disclosure, other and further embodiments of the present disclosure may be devised without departing from the basic scope thereof, which scope is determined by the following claims.

Claims

1. 1. A system for analyzing a physiological signal, comprising: an input configured to receive a discrete-time physiological signal; comparing an indication associated with a current sampled value of the physiological signal to an indication associated with a previous sampled value of the physiological signal; determining the presence of a reference event when the indication associated with the current sample value differs from the indication associated with the previous sample value; counting samples of the physiological signal within intervals defined between reference events to obtain a total number of samples for each of the intervals; determining at least one physiological parameter based on the total number of samples; At least one processor module configured as follows: A system comprising:

2. 10. The system of claim 1, wherein the physiological signal is selected from the group consisting of electrocardiogram, oxygen saturation, blood pressure, impedance, activity, accelerometry, fluid status, inspiratory and / or expiratory effort, and respiration.

3. The system of claim 1 or 2, wherein the reference event is a zero crossing or an extremum of the physiological signal.

4. 3. The system of claim 1, wherein the reference event includes or is a zero crossing, and the at least one processor module is configured to identify the zero crossing when the indication associated with the current sample value differs from the indication associated with the previous sample value.

5. The system of claim 4 , wherein the total number of samples in an interval between two consecutive zero crossings corresponds to the total number of uninterrupted runs of positive or negative indications of the sample values ​​within the interval.

6. 2. The system of claim 1, wherein the reference event includes or is an extreme value, and the at least one processor module is configured to identify the extreme value when the indication associated with a slope of the current sample value differs from the indication associated with a slope of the previous sample value.

7. The system of claim 6 , wherein the extreme value is a peak or a trough.

8. 8. The system of claim 6 or 7, wherein the interval of the total number of samples is defined between two consecutive extreme values ​​of the same type.

9. The system of claim 1 or 2, wherein the at least one processor module is configured to reject one or more criteria events if one or more rejection criteria are met.

10. 10. The system of claim 9, wherein the one or more rejection criteria relate to at least one of patient motion, operating limits of at least one signal processing element of the system, non-monotonicity, and noise.

11. The system of claim 1 or 2, wherein the system is configured to determine the at least one physiological parameter using a total number of samples associated with two or more reference events of different types.

12. 3. The system of claim 1, wherein the system is configured to determine the at least one physiological parameter for one or more observation windows, the length of each observation window being based on at least one of a set minimum number of samples and a maximum processing time.

13. 3. The system of claim 1 or 2, further comprising an implantable medical device configured to perform at least the following aspects: comparing signatures; determining the presence of a reference event; and counting samples.

14. 1. A method for analyzing a physiological signal, comprising: receiving a discrete-time physiological signal; comparing an indication associated with a current sampled value of the physiological signal to an indication associated with a previous sampled value of the physiological signal; determining the presence of a reference event when the indication associated with the current sample value differs from the indication associated with the previous sample value; counting samples of the physiological signal within intervals defined between reference events to obtain a total number of samples for each of the intervals; determining at least one physiological parameter based on the total number of samples; A method comprising:

15. 15. A machine-readable storage medium having stored thereon computer-executable instructions that, when executed, cause one or more processors to perform the method of claim 14.

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