GB2602645A

GB2602645A - Method and system for detecting peaks in a heartrate signal

Info

Publication number: GB2602645A
Application number: GB2100173.0A
Authority: GB
Inventors: Robertaud David
Original assignee: Prevayl Innovations Ltd
Current assignee: Prevayl Innovations Ltd
Priority date: 2021-01-07
Filing date: 2021-01-07
Publication date: 2022-07-13
Also published as: GB202100173D0

Abstract

Obtain a series of heartrate values which represent a heartrate signal. Determine an upper percentile value (Upv) and a lower percentile value (Lpv) from the series of heartrate values. Identify segments 400 from the series of heartrate values where in each segment the heartrate signal first exceeds the upper percentile value and then goes below the lower percentile value, e.g. using a hysteresis comparator. A heartrate value within each segment is selected as a peak for the heartrate signal. For example, the first value in a segment is selected as a peak. The upper percentile value may be between the 80th and 99th percentile. The lower percentile value may be between the 70th and 50th percentile. An inter-beat interval (IBI) may be calculated from the peak values. The upper and lower percentile values may act as dynamically changing, adaptive thresholds because they depend on the heartrate values.

Description

METHOD AND SYSTEM FOR DETECTING PEAKS IN A HEARTRATE SIGNAL

The present invention is directed towards a method and system for detecting peaks in a heartrate signal such as an ECG signal and for correcting an array of candidate peaks identified from a heartrate signal.

Background

Wearable articles, such as garments, incorporating sensors are wearable electronics used to measure and collect information from a wearer. Such wearable articles are commonly referred to as 'smart clothing'. It is advantageous to measure biosignals of the wearer during exercise, or other scenarios.

It is known to provide a garment, or other wearable article, to which an electronic device (i.e. an electronics module, and/or related components) is attached in a prominent position, such as on the chest or between the shoulder blades. Advantageously, the electronic device is a detachable device. The electronic device is configured to process the incoming signals, and the output from the processing is stored and/or displayed to a user in a suitable way A sensor senses a biosignal such as electrocardiogram (ECG) signals and the biosignals are coupled to the electronic device, via an interface. The sensors may be coupled to the interface by means of conductors which are connected to terminals provided on the interface to enable coupling of the signals from the sensor to the interface.

Electronics modules for wearable articles such as garments are known to communicate with user electronic devices over wireless communication protocols such as Bluetooth 0 and Bluetooth Low Enemy. These electronics modules are typically removably attached to the wearable article, interface with internal electronics of the wearable article, and comprise a Bluetooth 0 antenna for communicating with the user electronic device.

The electronic device includes drive and sensing electronics comprising components and associated circuitry, to provide the required functionality. The drive and sensing electronics include a power source to power the electronic device and the associated components of the drive and sensing circuitry.

ECG sensing is used to provide a plethora of information about a person's heart. It is one of the simplest and oldest techniques used to perform cardiac investigations. In its most basic form, it provides an insight into the electrical activity generated within heart muscles that changes over time. By detecting and amplifying these differential biopotential signals, a lot of information can be gathered quickly, including the heart rate. Among professional medical staff, individual signals have names such as "the QRS complex," which is the largest part of an ECG signal and is a collection of Q, R, and S signals, including the P and T waves.

Typically, the detected ECG signals can be displayed as a trace to a user for information. The user may be a clinician who is looking to assess cardiac health or may be a lay user using the electronics module as a fitness or health and wellness assessment device. A typical ECG waveform or trace is illustrated in Figure 1 showing the QRS complex. Figure 2 shows an ECG waveform of two successive heartbeats. The time difference between the two R peaks in the ECG waveform is the inter-beat interval (IBI) also known as the R-R interval. This time is usually expressed in milliseconds. IBI values represent the time between successive heartbeats.

Calculating the IBI value requires that peaks are detected in the ECG waveform. Peak detection algorithms are known in the art. Example peak algorithms include the Pan Tomkins algorithm as described in Pan, Jiapu; Tompkins, Willis J. (March 1985). "A Real-Time QRS Detection Algorithm". IEEE Transactions on Biomedical Engineering. BME-32 (3): 230-236. Other example peak detection algorithms use operations such as wavelet transforms.

Another example algorithm for peak detection is the REWARD algorithm as described in Orlandic L, Giovanni E, Arza A, Yazdani S, Vesin JM, Atienza D. "REWARD: Design, Optimization, and Evaluation of a Real-Time Relative-Energy Wearable R-Peak Detection Algorithm." Annu Int Conf IEEE Eng Med Biol Soc. 2019 Jul; 2019:3341-3347. doi: 10.1109/EMBC.2019.8857226. PMID: 31946597. This algorithm uses a hysteresis comparator to identify possible peaks. The algorithm detects segments in the signal in which the signal goes above an upperthreshold and subsequently below a lower threshold. The maximum of the signal within each of the segments is found and selected as a peak for the heartrate signal.

An object of the present invention is to provide an improved method and system for identifying peaks in a heartrate signal such as an ECG signal.

A further object of the present invention is to provide an improved method and system for correcting an array of detected peaks obtained from a heartrate signal such as to remove spurious peaks and insert missed true peaks.

Summary

According to the present disclosure there is provided a method and system as set forth in the appended claims. Other features of the invention will be apparent from the dependent claims, and the description which follows.

According to a first aspect of the disclosure, there is provided a computer-implemented method of detecting peaks in a heartrate signal. The method comprises obtaining a series of heartrate values representative of a heartrate signal. The method comprises determining an upper percentile value and a lower percentile value from the series of heartrate values. The method comprises identifying, from the series of heartrate values, a plurality of segments in which the heartrate signal goes above the upper percentile value and subsequently below the lower percentile value. The method further comprises selecting a heartrate value within each of the segments as a peak for the heartrate signal.

Advantageously, the method uses a hysteresis comparator to identify peaks in the heartrate signal. This approach is computationally efficient and avoids the need for complicated filtering approaches and the use of wavelet transforms. Moreover, the method sets the upper and lower thresholds of the comparator using percentile values obtained from the series of heartrate values. That is, the upper threshold is an upper percentile value for the signal and the lower threshold is a lower percentile value for the signal. This approach means that the thresholds are adaptable based on the heartrate signal, is capable of more accurately detecting true peaks in the signal, and is robust against temporal changes in the heartrate signal such as due to increased activity or noise. The use of a fixed threshold value such as those empirically determined in the REWARD algorithm is avoided.

Selecting the heartrate value may comprise selecting a single heartrate value in each of the segments as a peak for the heart rate signal.

Selecting the heartrate value may comprise selecting the first heartrate value in a or each of the segments as a peak for the heartrate signal. This approach is computationally efficient as it avoids comparing individual values within a segment in order to determine the candidate peak.

The upper percentile value may be greater than or equal to the 80th percentile of the heartrate signal.

The upper percentile value is greater than or equal to the 85th percentile of the heartrate signal.

The upper percentile value may be greater than or equal to the 90111 percentile of the heartrate signal.

The upper percentile value may be greater than or equal to the 95th percentile of the heartrate signal.

The upper percentile vale may be greater than or equal to the 99111 percentile of the heartrate signal.

The lower percentile value may be less than or equal to the 70th percentile of the heartrate signal.

The lower percentile value may be less than or equal to the 651h percentile of the heartrate signal.

The lower percentile value may be less than or equal to the 60th percentile of the heartrate signal.

The lower percentile value may be less than or equal to the 55th percentile of the heartrate signal.

The lower percentile value may be less than or equal to the 50th percentile of the heartrate signal.

In preferred examples, the upper percentile value is the 99111 percentile of the heartrate signal and the lower percentile value is the 50' percentile of the heartrate signal.

The method may further comprise calculating inter-beat interval, IBI, values for the heartrate signal from the selected heartrate values.

The method may be performed by a controller for a user electronic device. The user electronics device may further include an interface, coupled to the controller, and arranged to receive signals from an electronics module for a wearable article. The signals may comprise the series of heartrate values.

The method may be performed by a controller for an electronics module for a wearable article.

The electronics module may be a component of a smartwatch or a garment for example. The electronics module may comprise an interface, coupled to the controller, and arranged to receive signals from sensor components such as electrodes of the wearable article.

According to a second aspect of the disclosure, there is provided a computer-readable medium having instructions recorded thereon which, when executed by a processor, cause the processor to perform the method of the first aspect of the disclosure.

According to a third aspect of the disclosure, there is provided an apparatus/system for detecting peaks in a heartrate signal, the apparatus/system comprising a processor and a memory, the memory storing instructions which when executed by the processor cause the processor to perform operations comprising: obtaining a series of heartrate values representative of a heartrate signal; determining an upper percentile value and a lower percentile value from the series of heartrate values; identifying, from the series of heartrate values, a plurality of segments in which the heartrate signal goes above the upper percentile value and subsequently below the lower percentile value; and selecting a heartrate value within each of the segments as a peak for the heartrate signal.

The apparatus/system may comprise a user electronics device. The user electronics device may comprise the processor and the memory. The user electronics device may comprise an interface, coupled to the controller, the controller being arranged to receive signals from an electronics module for a wearable article. The controller may be configured to obtain biosignal data for a wearer of the wearable article from the electronics module.

The apparatus/system may comprise the electronics module for the wearable article. The electronics module may provide biosignal data to a user electronics device comprising the processor and the memory. The electronics module may comprise the processor and the memory. The electronics module may be a component of a smartwatch or a garment for

example

According to a fourth aspect of the present disclosure, there is provided a computer-implemented method of correcting an array of candidate peaks identified from a heartrate signal. The method comprises obtaining an array of candidate peaks identified from a heartrate signal, the array of candidate peaks comprising fimestamp values representing the time at which the candidate peaks occur. The method comprises calculating inter-beat interval, IBI, values for the array of candidate peaks, the IBI values representing the time intervals between successive candidate peaks. The method comprises calculating one or more statistical measures for the IBI values. The method comprises determining, using the one or more statistical measures, whether a correction should be applied to the array of candidate peaks. If a correction should be applied, the method further comprises inserting and/or removing a peak into/from the array of candidate peaks.

Advantageously, the method uses a statistical approach to determine whether to insert or remove a peak from the array of candidate peaks. Adding a peak can compensate for a peak that was missed from the initial obtained array of candidate peaks. Removing a peak can compensate for a false peak that was incorrectly included in the initial obtained array of candidate peaks. This statistical approach is advantageously adaptable based on changes to the incoming heartrate signal. This enables the peak detection approach to automatically account for changes due to a change in a user's activity level or indeed a change to a different user. This approach contrasts and improves on the approach used in the REWARD algorithm which uses empirically determined values to determine whether to remove a peak.

A correction may be determined to be needed if the one or more statistical measures indicate that the IBI values have a high degree of variance. That is, if the IBI values are widely dispersed around an average value for the IBI values. A wide dispersion indicates that the IBI values do not accurately reflect the true heartrate of the subject. This may be due to false peaks caused by noise being included in the array of candidate peaks or true peaks being missed from the array of candidate peaks. Advantageously, a correction is applied if the IBI values are widely dispersed around the average value to remove any false peaks and insert any missed true peaks. In this way, the array of candidate peaks is corrected to better reflect the true heartrate of the subject.

Any statistical measures that can be used to determine the dispersion of the IBI values around the average may be used. The one or more statistical measures may comprise a measure of the standard deviation for the IBI values. The one or more statistical measures may comprise a measure of the average for the IBI values. Determining whether a correction should be applied to the array of candidate peaks may comprise determining if the measure of the standard deviation is more than a predetermined percentage of the measure of the average, and wherein a correction should be applied if the measure of the standard deviation is more that the predetermined percentage of the measure of the average.

Inserting a peak into the array of candidate peaks may comprise inserting a peak between two adjacent peaks in the array of candidate peaks. That is, the inserted peak is given a timestamp that is between the timestamps of two adjacent peaks in the array of candidate peaks. The inserted peak may have a timestamp that is located equidistantly between the two adjacent peaks.

The peak may be inserted between two adjacent peaks based on the IBI values. The peak may be inserted between two adjacent peaks according to the IBI value determined for the two adjacent peaks. The peak may be inserted according to a comparison between the IBI value and one or more statistical measures derived from the IBI values. The peak may be inserted between two adjacent peaks according to a comparison between the IBI value, a measure of the standard deviation of the IBI values and the measure of the average of the IBI values. The peak may be inserted between two adjacent peaks if the IBI value for the two adjacent peaks is greater than the measure of the average plus the measure of the standard deviation.

Advantageously, a peak is inserted if the time difference between two adjacent peaks is greater than the average plus the standard deviation. The IBI value being greater than the average plus the standard deviation indicates that a true peak has been missed.

A plurality or each of the IBI values may compared to one or more statistical measures so that a plurality of peaks may be inserted into the array of candidate peaks.

Removing a peak from the array of candidate peaks may comprise removing a peak based on the IBI value determined for the peak and an adjacent peak in the array of candidate peaks. A peak may be removed according to a comparison between the IBI value and one or more statistical measures derived from the IBI values. The peak may be removed according to a comparison between the IBI value, a measure of the standard deviation of the IBI values and a measure of the average of the IBI values. The peak may be removed if the IBI value is less than the measure of the average minus the measure of the standard deviation. Either of the peaks used to generate the IBI value may be removed. Each IBI value is determined according to the difference between a pair of consecutive peaks. One of the peaks in the pair may be removed. That is, either the earlier or the later peak in the pair may be removed.

Advantageously, a peak is removed if the time difference between two adjacent peaks is less than the average minus the standard deviation. The IBI value being less than the average minus the standard deviation indicates that one of the peaks is a false peak due to noise.

A plurality or each of the IBI values may compared to one or more statistical measures so that a plurality of peaks may be removed from the array of candidate peaks.

The method may further comprise calculating IBI values for the array of candidate peaks following the inserting and/or removing a peak into/from the array of candidate peaks; calculating one or more statistical measures for the IBI values; and determining, using the one or more statistical measures, whether a correction should be applied to the array of candidate peaks, wherein if a correction should be applied, the method further comprises inserting and/or removing a peak into/from the array of candidate peaks.

The method may be repeated until the one or more statistical measures indicate that corrections no longer need to be applied or an exit condition is reached. For example, the method may be repeated until the measure of the standard deviation is less than a predetermined percentage of the measure of the average.

While the one or more statistical measures indicate that a correction needs to be applied and an exit condition is not reached, the method may comprise: inserting and/or removing a peak into/from the array of candidate peaks; calculating inter-beat interval, IBI, values for the array of candidate peaks, the IBI values representing the time intervals between successive candidate peaks; and calculating one or more statistical measures for the IBI values. The re-calculating statistical measures are used to again determine whether a correction needs to be applied.

B

The obtained array of candidate peaks may be a first array of candidate peaks. The method may further comprise obtaining a second array of candidate peaks for the heartrate signal. The second array of candidate peaks for the heartrate signal may represent a time window that overlaps (partially) with the time window of the first array of candidate peaks.

The method may comprise calculating inter-beat interval, IBI, values for the second array of candidate peaks, the IBI values representing the time intervals between successive candidate peaks. The method may further comprise obtaining one or more timestamps of peaks that were added and/or removed from the first array of candidate peaks and whose timestamps fall within the time window of the second array of candidate peaks. The method may comprise using the one or more obtained timestamps to insert and/or remove a peak into/from the second array of candidate peaks to form a second corrected array of candidate peaks. In this way, a correction that was applied in the previous time window may be reapplied to the new time window.

The method may comprise calculating one or more statistical measures for the IBI values determined from the second array of candidate peaks. The statistical measures may comprise a measure of the standard deviation for the IBI values and a measure of the average for the IBI values. (Only) if the one or more statistical measures indicate that a correction should be applied (e.g. the measure of the standard deviation is more than a predetermined percentage of the measure of the average), then the method may further comprise the obtaining of the one or more timestamps of peaks that were added and/or removed from the first array of candidate peaks and whose timestamps fall within the time window of the second array of candidate peaks and the using the one or more obtained timestamps to insert and/or remove a peak into/from the second array of candidate peaks to form a second corrected array of candidate peaks. In this way, a correction that was applied in the previous time window may be reapplied to the new time window if the statistical measures for the new time window indicate that a correction needs to be applied.

The method may comprise calculating inter-beat interval, IBI, values for the second corrected array of candidate peaks. The method may comprise calculating one or more statistical measures for the IBI values. The statistical measures may comprise a measure of the standard deviation for the IBI values and a measure of the average for the IBI values. If the one or more statistical measures indicate that a correction should be applied (e.g. the measure of the standard deviation is more than a predetermined percentage of the measure of the average), the method may further comprise: inserting and/or removing a peak into/from the second corrected array of candidate peaks The method may be repeated until the one or more statistical measures indicate that corrections no longer need to be applied (e.g. the measure of the standard deviation is less than a predetermined percentage of the measure of the average) or an exit condition is reached. That is, while the one or more statistical measures indicate that a correction needs to be applied and an exit condition is not reached, the method may comprise: inserting and/or removing a peak into/from the second corrected array of candidate peaks; calculating inter-beat interval, IBI, values for the second corrected array of candidate peaks, the IBI values representing the time intervals between successive candidate peaks; and calculating one or more statistical measures for the IBI values.

The method may be repeated for a plurality of arrays of candidate peaks. Each of the arrays of candidate peaks may represent a time window that overlaps with the time window of a previously corrected array of candidate peaks.

The predetermined percentage may be between 2% and 30%. The predetermined percentage may be between 5% and 30%. The predetermined percentage may be between 10% and 30%. The predetermined percentage may be between 15% and 30%. The predetermined percentage may be between 20% and 30%. The predetermined percentage may be between 25% and 30%. The predetermined percentage may be between 2% and 25%. The predetermined percentage may be between 2% and 20%. The predetermined percentage may be between 2% and 15%.

The predetermined percentage may be between 2% and 10%. The predetermined percentage may be between 2% and 5%. The predetermined percentage may be between 5% and 25% or between 5% and 15%. In some examples, the predetermined percentage is 10%.

The obtained array of candidate peaks may be generated using the method of the first aspect

of the disclosure.

Obtaining the array of candidate peaks may comprise: obtaining a series of heartrate values representative of a heartrate signal; determining an upper percentile value and a lower percentile value from the series of heartrate values; identifying, from the series of heartrate values, a plurality of segments in which the heartrate signal goes above the upper percentile value and subsequently below the lower percentile value; and selecting a heartrate value within each of the segments as a peak for the heartrate signal.

The method may be performed by a controller for a user electronic device. The user electronics device may further include an interface, coupled to the controller, and arranged to receive signals from an electronics module for a wearable article. The signals may comprise the heartrate signal or the array of candidate peaks.

The method may be performed by a controller for an electronics module for a wearable article. The electronics module may be a component of a smartwatch or a garment for example. The electronics module may comprise an interface, coupled to the controller, and arranged to receive signals from sensor components such as electrodes of the wearable article.

According to a fifth aspect of the disclosure, there is provided a computer-readable medium having instructions recorded thereon which, when executed by a processor, cause the processor to perform the method of the fourth aspect of the disclosure.

According to a sixth aspect of the disclosure, there is provided a apparatus/system for correcting an array of candidate peaks identified from a heartrate signal, the apparatus/system comprising a processor and a memory, the memory storing instructions which when executed by the processor cause the processor to perform operations comprising: obtaining an array of candidate peaks identified from heartrate signal, the array of candidate peaks comprising timestamp values representing the time at which the candidate peaks occur; calculating inter-beat interval, IBI, values for the array of candidate peaks, the IBI values representing the time intervals between successive candidate peaks; calculating one or more statistical measures for the IBI values; determining, using the one or more statistical measures, whether a correction should be applied to the array of candidate peaks; and if a correction should be applied, inserting and/or removing a peak into/from the array of candidate peaks.

The apparatus/system may comprise the electronics module for the wearable article. The electronics module may provide biosignal data to a user electronics device comprising the processor and the memory. The electronics module may comprise the processor and the memory. The electronics module may be a component of a smartwatch or a garment for example.

In the above examples of the present disclosure, the heartrate signal may be an electrocardiogram, ECG, signal but this is not required in all examples and other signals indicative of the heartrate are within the scope of the present disclosure. Other signals indicative of the heartrate include photoplethysmography (PPG) signals, ballistocardiogram (BCG) signals, and electromagnetic cardiogram (EMCG) signals.

In the above examples, the peaks may be R-peaks in an ECG or similar signal. The IBI values may be R-R interval values. The peaks are not required to be R-peaks. The method may be for detecting other characteristic peaks of an ECG or similar signal such as S-peaks or other characteristic peaks in signals indicative of the heartrate of the subject.

Brief Description of the Drawings

Examples of the present disclosure will now be described with reference to the accompanying drawings, in which: Figure 1 illustrates a signal trace for an ECG signal; Figure 2 illustrates an ECG waveform that includes electrical signals for two successive heartbeats; Figure 3 shows a schematic diagram for an example system according to aspects of the present disclosure; Figure 4 shows a schematic diagram for an example electronics module according to aspects of

the present disclosure;

Figure 5 shows a schematic diagram for another example electronics module according to aspects of the present disclosure; Figure 6 shows a schematic diagram for an example analogue-to-digital converter used in the example electronics module of Figures 4 and 5 according to aspects of the present disclosure; Figure 7 shows a schematic diagram of the components of an example user electronics device according to aspects of the present disclosure; Figures 8 shows a flow diagram for an example method of detecting peaks in a heartrate signal according to aspects of the present disclosure; Figure 9 shows a signal trace for an ECG signal with annotations to explain how the method of Figure 8 is used to detect a peak; and Figure 10 shows a flow diagram for an example method of correcting an array of candidate peaks identified from a heartrate signal.

Detailed Description

The following description with reference to the accompanying drawings is provided to assist in a comprehensive understanding of various embodiments of the disclosure as defined by the claims and their equivalents. It includes various specific details to assist in that understanding but these are to be regarded as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the various embodiments described herein can be made without departing from the scope and spirit of the disclosure. In addition, descriptions of well-known functions and constructions may be omitted for clarity and conciseness The terms and words used in the following description and claims are not limited to the bibliographical meanings but are merely used by the inventor to enable a clear and consistent understanding of the disclosure. Accordingly, it should be apparent to those skilled in the art that the following description of various embodiments of the disclosure is provided for illustration purpose only and not for the purpose of limiting the disclosure as defined by the appended claims and their equivalents.

It is to be understood that the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise.

"Wearable article" as referred to throughout the present disclosure may refer to any form of device interface which may be wom by a user such as a smart watch, necklace, garment, bracelet, or glasses. The wearable article may be a textile article. The wearable article may be a garment. The garment may refer to an item of clothing or apparel. The garment may be a top.

The top may be a shirt, t-shirt, blouse, sweater, jacket/coat, or vest. The garment may be a dress, garment brassiere, shorts, pants, arm or leg sleeve, vest, jacket/coat, glove, armband, underwear, headband, hat/cap, collar, wristband, stocking, sock, or shoe, athletic clothing, personal protective equipment, including hard hats, swimwear, wetsuit or dry suit.

The term "wearer" includes a user who is wearing, or otherwise holding, the wearable article.

The type of wearable garment may dictate the type of biosignals to be detected. For example, a hat or cap may be used to detect eiectroencephalogram or magnetoencephalogram signals.

The wearable article/garment may be constructed from a woven or a non-woven material. The wearable article/garment may be constructed from natural fibres, synthetic fibres, or a natural fibre blended with one or more other materials which can be natural or synthetic. The yam may be cotton. The cotton may be blended with polyester and/or viscose and/or polyamide according to the application. Silk may also be used as the natural fibre. Cellulose, wool, hemp and jute are also natural fibres that may be used in the wearable article/garment. Polyester, polycotton, nylon and viscose are synthetic fibres that may be used in the wearable article/garment.

The garment may be a tight-fitting garment. Beneficially, a tight-fitting garment helps ensure that the sensor devices of the garment are held in contact with or in the proximity of a skin surface of the wearer. The garment may be a compression garment. The garment may be an athletic garment such as an elastomeric athletic garment.

The garment has sensing units provided on an inside surface which are held in close proximity to a skin surface of a wearer wearing the garment. This enables the sensing units to measure biosignals for the wearer wearing the garment.

The sensing units may be arranged to measure one or more biosignals of a wearer wearing the garment.

"Biosignal" as referred to throughout the present disclosure may refer to signals from living beings that can be continually measured or monitored. Biosignals may be electrical or nonelectrical signals. Signal variations can be time variant or spatially variant.

Sensing components may be used for measuring one or a combination of bioelectrical, bioimpedance, biochemical, biomechanical, bioacousfics, bioopfical or biothermal signals of the wearer 600. The bioelectrical measurements include electrocardiograms (ECG), electrogastrograms (EGG), electroencephalograms (EEG), and electromyography (EMG). The bioimpedance measurements include plethysmography (e.g., for respiration), body composition (e.g., hydration, fat, etc.), and electroimpedance tomography (Eli). The biomagnetic measurements include magnetoneurograms (MNG), magnetoencephalography (MEG), magnetogastrogram (MGG), magnetocardiogram (MCG). The biochemical measurements include glucose/lactose measurements which may be performed using chemical analysis of the wearer 600's sweat. The biomechanical measurements include blood pressure. The bioacoustics measurements include phonocardiograms (PCG). The bioopfical measurements include orthopantomogram (OPG). The biothermal measurements include skin temperature and core body temperature measurements.

Referring to Figures 3 to 7, there is shown an example system 10 according to aspects of the present disclosure. The system 10 comprises an electronics module 100, a wearable article in the form of a garment 200, and a user electronic device 300. The garment 200 is worn by a user who in this embodiment is the wearer 600 of the garment 200.

The electronics module 100 is arranged to integrate with sensing units 400 incorporated into the garment 200 to obtain signals from the sensing units 400. The electronics module 100 and the wearable article 200 and including the sensing units 400 comprise a wearable assembly 500.

The sensing units 400 comprise one or more sensors 209, 211 with associated conductors 203, 207 and other components and circuitry. The electronics module 100 is further arranged to wirelessly communicate data to the user electronic device 300. Various protocols enable wireless communication between the electronics module 100 and the user electronic device 300. Example communication protocols include Bluetooth 0, Bluetooth (P) Low Energy, and near-field communication (NEC).

The garment 200 has an electronics module holder in the form of a pocket 201. The pocket 201 is sized to receive the electronics module 100. When disposed in the pocket 201, the electronics module 100 is arranged to receive sensor data from the sensing units 400. The electronics module 100 is therefore removable from the garment 200.

The present disclosure is not limited to electronics module holders in the form pockets.

The electronics module 100 may be configured to be releasably mechanically coupled to the garment 200. The mechanical coupling of the electronic module 100 to the garment 200 may be provided by a mechanical interface such as a clip, a plug and socket arrangement, etc. The mechanical coupling or mechanical interface may be configured to maintain the electronic module 100 in a particular orientation with respect to the garment 200 when the electronic module 100 is coupled to the garment 200. This may be beneficial in ensuring that the electronic module 100 is securely held in place with respect to the garment 200 and/or that any electronic coupling of the electronic module 100 and the garment 200 (or a component of the garment 200) can be optimized. The mechanical coupling may be maintained using friction or using a positively engaging mechanism, for example.

Beneficially, the removable electronic module 100 may contain all the components required for data transmission and processing such that the garment 200 only comprises the sensing units 400 e.g. the sensors 209, 211 and communication pathways 203, 207. In this way, manufacture of the garment 200 may be simplified. In addition, it may be easier to clean a garment 200 which has fewer electronic components attached thereto or incorporated therein. Furthermore, the removable electronic module 100 may be easier to maintain and/or troubleshoot than embedded electronics. The electronic module 100 may comprise flexible electronics such as a flexible printed circuit (FPC).

The electronic module 100 may be configured to be electrically coupled to the garment 200.

Referring to Figure 4, there is shown a schematic diagram of an example of the electronics module 100 of Figure 1. A more detailed block diagram of the electronics components of electronics module 100 and garment are shown in Figure 5.

The electronics module 100 comprises an interface 101, a controller 103, a power source 105, and one or more communication devices which, in the exemplar embodiment comprises a first antenna 107, a second antenna 109 and a wireless communicator 159. The electronics module 100 also includes an input unit such as a proximity sensor or a motion sensor 111, for example in the form of an inertial measurement unit (IMU).

The electronics module 100 also includes additional peripheral devices that are used to perform specific functions as will be described in further detail herein.

The interface 101 is arranged to communicatively couple with the sensing unit 400 of the garment 200. The sensing unit 400 comprises -in this example -the two sensors 209, 211 coupled to respective first and second electrically conductive pathways 203, 207, each with respective termination points 213, 215. The interface 101 receives signals from the sensors 209, 211. The controller 103 is communicatively coupled to the interface 101 and is arranged to receive the signals from the interface 101 for further processing.

The interface 101 of the embodiment described herein comprises first and second contacts 163, 165 which are arranged to be communicatively coupled to the termination points 213, 215 the respective first and second electrically conductive pathways 203, 207. The coupling between the termination points 213, 215 and the respective first and second contacts 163, 165 may be conductive or a wireless (e.g. inductive) communication coupling.

In this example the sensors 209, 211 are used to measure electropotenfial signals such as electrocardiogram (ECG) signals, although the sensors 209, 211 could be configured to measure other biosignal types as also discussed above.

In this embodiment, the sensors 209, 211 are configured for so-called dry connection to the wearer's skin to measure ECG signals.

The power source 105 may comprise a plurality of power sources. The power source 105 may be a battery. The battery may be a rechargeable battery. The battery may be a rechargeable battery adapted to be charged wirelessly such as by inductive charging. The power source 105 may comprise an energy harvesting device. The energy harvesting device may be configured to generate electric power signals in response to kinetic events such as kinetic events 10 performed by the wearer 600 of the garment 200. The kinetic event could include walking, running, exercising or respiration of the wearer 600. The energy harvesting material may comprise a piezoelectric material which generates electricity in response to mechanical deformation of the converter. The energy harvesting device may harvest energy from body heat of the wearer 600 of the garment. The energy harvesting device may be a thermoelectric energy harvesting device. The power source 105 may be a super capacitor, or an energy cell.

The first antenna 107 is arranged to communicatively couple with the user electronic device 300 using a first communication protocol. In the example described herein, the first antenna 107 is a passive tag such as a passive Radio Frequency Identification (RFID) tag or Near Field Communication (NFC) tag. These tags comprise a communication module as well as a memory which stores the information, and a radio chip. The user electronic device 300 is powered to induce a magnetic field in an antenna of the user electronic device 300. When the user electronic device 300 is placed in the magnetic field of the communication module antenna 107, the user electronic device 300 induces current in the communication module antenna 107. This induced current triggers the electronics module 100 to retrieve the information from the memory of the tag and transmit the same back to the user electronic device 300.

In an example operation, the user electronic device 300 is brought into proximity with the electronics module 100. In response to this, the electronics module 100 is configured to energize the first antenna 107 to transmit information to the user electronic device 300 over the first wireless communication protocol. Beneficially, this means that the act of the user electronic device 300 approaching the electronics module 100 energizes the first antenna 107 to transmit the information to the user electronic device 300.

The information may comprise a unique identifier for the electronics module 100. The unique identifier for the electronics module 100 may be an address for the electronics module 100 such as a MAC address or Bluetooth ID address.

The information may comprise authentication information used to facilitate the pairing between the electronics module 100 and the user electronic device 300 over the second wireless communication protocol. This means that the transmitted information is used as part of an out of band (00B) pairing process.

The information may comprise application information which may be used by the user electronic device 300 to start an application on the user electronic device 300 or configure an application running on the user electronic device 300. The application may be started on the user electronic device 300 automatically (e.g. without wearer 600 input). Alternatively, the application information may cause the user electronic device 300 to prompt the wearer 600 to start the application on the user electronic device. The information may comprise a uniform resource identifier such as a uniform resource location to be accessed by the user electronic device, or text to be displayed on the user electronic device for example. It will be appreciated that the same electronics module 100 can transmit any of the above example information either alone or in combination. The electronics module 100 may transmit different types of information depending on the current operational state of the electronics module 100 and based on information it receives from other devices such as the user electronic device 300.

The second antenna 109 is arranged to communicatively couple with the user electronic device 300 over a second wireless communication protocol. The second wireless communication protocol may be a Bluetooth 0 protocol, Bluetooth 0 5 or a Bluetooth 0 Low Energy protocol but is not limited to any particular communication protocol. In the present embodiment, the second antenna 109 is integrated into controller 103. The second antenna 109 enables communication between the user electronic device 300 and the controller 100 for configuration and set up of the controller 103 and the peripheral devices as may be required. Configuration of the controller 103 and peripheral devices utilises the Bluetooth 0 protocol.

The wireless communicator 159 may be an alternative, or in addition to, the first and second antennas107, 109.

Other wireless communication protocols can also be used, such as used for communication over: a wireless wide area network ('AN), a wireless metro area network (VVMAN), a wireless local area network VLAN), a wireless personal area network (VVPAN), Bluetooth ® Low Energy, Bluetooth ® Mesh, Thread, Zigbee, IEEE 802.15.4, Ant, a Global Navigation Satellite System (GNSS), a cellular communication network, or any other electromagnetic RF communication protocol. The cellular communication network may be a fourth generation (4G) LTE, LTE Advanced (LTE-A), LTE Cat-M1, LTE Cat-M2, NB-loT, fifth generation (5G), sixth generation (6G), and/or any other present or future developed cellular wireless network.

The electronics module 100 includes configured a clock unit in the form of a real time clock (RTC) 153 coupled to the controller 103 and, for example, to be used for data logging, clock building, time stamping, timers, and alarms. As an example, the RTC 153 is driven by a low frequency clock source or crystal operated at 32.768 Hz.

The electronics module 100 also includes a location device 161 such as a GNSS (Global Navigation Satellite System) device which is arranged to provide location and position data for applications as required. In particular, the location device 161 provides geographical location data at least to a nation state level. Any device suitable for providing location, navigation or for tracking the position could be utilised. The GNSS device may include device may include Global Positioning System (GPS), BeiDou Navigation Satellite System (BDS) and the Galileo system devices The power source 105 in this example is a lithium polymer battery 105. The battery 105 is rechargeable and charged via a USB C input 131 of the electronics module 100. Of course, the present disclosure is not limited to recharging via USB and instead other forms of charging such as inductive of far field wireless charging are within the scope of the present disclosure. Additional battery management functionality is provided in terms of a charge controller 133, battery monitor 135 and regulator 147. These components may be provided through use of a 30 dedicated power management integrated circuit (PMIC).

The USB C input 131 is also coupled to the controller 131 to enable direct communication with the controller 103 with an external device if required.

The controller 103 is communicatively connected to a battery monitor 135 so that that the controller 103 may obtain information about the state of charge of the battery 105.

The controller 103 has an internal memory 167 and is also communicatively connected to an external memory 143 which in this example is a NAND Flash memory. The memory 143 is used to for the storage of data when no wireless connection is available between the electronics module 100 and a user electronic device 300. The memory 143 may have a storage capacity of at least 1GB and preferably at least 2 GB. The electronics module 100 also comprises a temperature sensor 145 and a light emitting diode 147 for conveying status information. The electronic module 100 also comprises conventional electronics components including a power-on-reset generator 149, a development connector 151, the real time clock 153 and a FROG header 155.

Additionally, the electronics module 100 may comprise a haptic feedback unit 157 for providing a haptic (vibrational) feedback to the wearer 600.

The wireless communicator 159 may provide wireless communication capabilities for the garment 200 and enables the garment to communicate via one or more wireless communication protocols to a remote server 700. Wireless communications may include: a wireless wide area network (WJAN), a wireless metro area network (WMAN), a wireless local area network (VVLAN), a wireless personal area network (WPAN), Bluetooth 0 Low Energy, Bluetooth 0 Mesh, Bluetooth 0 5, Thread, Zigbee, IEEE 802.15.4, Ant, a near field communication (NFC), Near Field Magnetic Induction, a Global Navigation Satellite System (GNSS), a cellular communication network, or any other electromagnetic RF communication protocol. The cellular communication network may be a fourth generation (4G) LTE, LTE Advanced (LTE-A), LTE Cat-M1, LTE Cat-M2, NB-IoT, fifth generation (5G), sixth generation (6G), and/or any other present or future developed cellular wireless network.

The electronics module 100 may additionally comprise a Universal Integrated Circuit Card (UICC) that enables the garment to access services provided by a mobile network operator (MNO) or virtual mobile network operator (VMNO). The UICC may include at least a read-only memory (ROM) configured to store an MNO or VMNO profile that the garment can utilize to register and interact with an MNO or VMNO. The UICC may be in the form of a Subscriber Identity Module (SIM) card. The electronics module 100 may have a receiving section arranged to receive the SIM card. In other examples, the UICC is embedded directly into a controller of the electronics module 100. That is, the UICC may be an electronic/embedded UICC (eUICC). A eUICC is beneficial as it removes the need to store a number of MNO profiles, i.e. electronic Subscriber Identity Modules (eSIMs). Moreover, eSIMs can be remotely provisioned to garments. The electronics module 100 may comprise a secure element that represents an 35 embedded Universal Integrated Circuit Card (eUICC). In the present disclosure, the electronics module may also be referred to as an electronics device or unit. These terms may be used interchangeably.

The controller 103 is connected to the interface 101 via an analog-to-digital converter (ADC) front end 139 and an electrostatic discharge (ESD) protection circuit 141.

Figure 6 is a schematic illustration of the component circuitry for the ADC front end 139.

In the example described herein, the ADC front end 139 is an integrated circuit (IC) chip which converts the raw analogue biosignal received from the sensors 209, 211 into a digital signal for further processing by the controller 103. ADC IC chips are known, and any suitable one can be utilised to provide this functionality. ADC IC chips for ECG applications include, for example, the MAX30003 chip produced by Maxim Integrated Products Inc. The ADC front end 139 includes an input 169 and an output 171.

Raw biosignals from the electrodes 209, 211 are input to the ADC front end 139, where received signals are processed in an ECG channel 175 and subject to appropriate filtering through high pass and low pass filters for static discharge and interference reduction as well as for reducing bandwidth prior to conversion to digital signals. The reduction in bandwidth is important to remove or reduce motion artefacts that give rise to noise in the signal due to movement of the sensors 209, 211.

The output digital signals may be decimated to reduce the sampling rate prior to being passed to a serial programmable interface (SPI) 173 of the ADC front end 139.

ADC front end IC chips suitable for ECG applications may be configured to determine information from the input biosignals such as heart rate and the ORS complex and including the R-R interval. Support circuitry 177 provides base voltages for the ECG channel 175. Although this is no required in all examples, as these determinations such as for identifying peaks in the heartrate signal may be performed by the controller 103 of the electronics module 100 or the user electronic device 300 as explained below.

Signals are output to the controller 103 via the SPI 173. The signals may be digital heartrate values obtained by the ADC front end 139.

The controller 103 can also be configured to apply digital signal processing (DSP) to the digital signal from the ADC front end 139.

The DSP may include noise filtering additional to that carried out in the ADC front end 139 and ay also include additional processing to determine further information about the signal from the ADC front end 139.

The controller 103 is configured to send the biosignals to the user electronic device 300 using either of the first antenna 107, second antenna 109, or wireless communicator 159. The biosignals sent to the user electronic device 300 in this example comprise digital heartrate values representative of the heartrate signal of the user.

The user electronic device 300 in the example of Figure 7 is in the form of a mobile phone or tablet and comprises a controller 305, a memory 304, a wireless communicator 307, a display 301, a user input unit 306, a capturing device in the form of a camera 303 and an inertial measurement unit (IMU) 309. The controller 305 provides overall control to the user electronic device 300.

The user input unit 306 receives inputs from the user such as a user credential.

The memory 304 stores information for the user electronic device 300.

The display 301 is arranged to display a user interface for applications operable on the user electronic device 300.

The IMU 309 provides motion and/or orientation detection and may comprise an accelerometer and optionally one or both of a gyroscope and a magnetometer.

The user electronic device 300 may also include a biometric sensor. The biometric sensor may be used to identify a user or users of device based on unique physiological features. The biometric sensor may be: a fingerprint sensor used to capture an image of a user's fingerprint; an iris scanner or a retina scanner configured to capture an image of a user's iris or retina; an ECG module used to measure the user's ECG; or the camera of the user electronic arranged to capture the face of the user. The biometric sensor may be an internal module of the user electronic device. The biometric module may be an external (stand-alone) device which may be coupled to the user electronic device by a wired or wireless link.

The controller 305 is configured to launch an application which is configured to display insights derived from the biosignal data processed by the ADC front end 139 of the electronics module 100, input to electronics module controller 103, and then transmitted from the electronics module 100. The transmitted data is received by the wireless communicator 307 of the user electronic device 300 and input to the controller 305.

Insights include, but are not limited to, an ECG signal trace i.e. the QRS complex, heart rate, respiration rate, core temperature but can also include identification data for the wearer 600 using the wearable assembly 500.

The display 301 may be a presence-sensitive display and therefore may comprise the user input unit 306. The presence-sensitive display may include a display component and a presence-sensitive input component. The presence sensitive display may be a touch-screen display arranged as part of the user interface.

User electronic devices in accordance with the present invention are not limited to mobile phones or tablets and may take the form of any electronic device which may be used by a user to perform the methods according to aspects of the present invention. The user electronic device 300 may be a electronics module such as a smaitphone, tablet personal computer (PC), mobile phone, smart phone, video telephone, laptop PC, netbook computer, personal digital assistant (PDA), mobile medical device, camera or wearable device. The user electronic device 300 may include a head-mounted device such as an Augmented Reality, Virtual Reality or Mixed Reality head-mounted device. The user electronic device 300 may be desktop PC, workstations, television apparatus or a projector, e.g. arranged to project a display onto a surface.

In use, the electronics module 100 is configured to receive raw biosignal data from the sensors 209, 211 and which are coupled to the controller 103 via the interface 101 and the ADC front end 139 for further processing and transmission to the user electronic device 300 as described above. The data transmitted to the user electronics device 300 includes raw or processed biosignal data such as ECG data, heart rate, respiration data, core temperature and other insights as determined.

The controller 305 of the user electronics device 300 is also operable to launch an application which is configured to detect candidate peaks from a series of heartrate values representative of a heartrate signal as received from the electronics module 100.

Referring to Figure 8, there is shown a flow diagram for a method of detecting peaks in the heartrate signal as performed by the application launched by the controller 305.

In step 8101, a series of heartrate values representative of a heartrate signal are obtained. The series of heartrate values each comprise a timestamp and an amplitude value representing the amplitude of the signal at the timestamp. The series of heartrate values cover a time window of N seconds where N is a number that may be selected by the skilled person as desired to ensure that there are likely to be a certain desired number of R-peaks in the series of heartrate values. In some examples, N is greater than or equal to 4 such that the series of heartrate values represent 4 seconds of the heartrate signal. 4 seconds is generally sufficient to ensure that there are at least 2 R-peaks in the time window. The number of heartrate values obtained to ensure that N seconds of data are present will depend on the sampling rate of the values provided by the electronics module 100. For example, if the sampling rate is 512Hz and at least 4 seconds of data are required, then 2048 heartrate values are obtained. Other values of N and other sampling rates are within the scope of the present disclosure.

In step 8102, an upper percentile value and a lower percentile value are determined from the obtained series of heartrate values. In this example, the upper percentile value is the 991h percentile which means that 99% of the heartrate values have an amplitude lowerthan the upper percentile value. In this example, the lower percentile value is the 50th percentile value which means that 50% of the heartrate values have an amplitude lower than the lower percentile value.

Other percentile values are within the scope of the present disclosure.

In step 8103, a plurality of segments are identified from the series of heartrate values. The plurality of segments are segments of the heartrate values where the heartrate goes above the upper percentile value and subsequently below the lower percentile value. Figure 9 shows an example of how one segment is identified from the series of heartrate values. The segment 400 begins with a heartrate value that first crosses the upper percentile value (Upv) and ends with a heartrate value that is first to fall below the lower percentile value (Lpv).

In step S104, a heartrate value within each of the segments is selected as a peak for the heartrate signal. In particular, the first heartrate value 401 (Figure 9) in each segment which crosses the upper percentile value is selected as the peak for the heartrate signal. The timestamp for the selected heartrate value for each segment is stored in an array of candidate peaks.

The method may be repeated for further series of heartrate values each representing a different time window. The time windows may partially overlap if a rolling window is used. The method may be performed in real time as the heartrate values are received from the electronics module 100.

The controller 305 of the user electronics device 300 is also operable to launch an application which is configured to correct an array of candidate peaks identified from a heartrate signal as received from the electronics module 100.

Referring to Figure 10, there is shown a flow diagram for a method of correcting an array of candidate peaks identified from a heartrate signal as performed by the application launched by the controller 305.

In step S201, an array of candidate peaks identified from the heartrate signal is obtained. The array of candidate peaks may be detected using the peak detection process described in relation to Figures Band 9.

In step S202, inter-beat interval, IBI, values are calculated for the array of candidate peaks. The IBI values representing the time intervals between successive candidate peaks. The IBI values are generally R-R values representing the time between successive R-peaks in the heartrate signal.

In step S203, statistical measures are calculated for the IBI values. The statistical measures include a measure of the standard deviation for the IBI values and a measure of the average for the IBI values. The measure of the average for the IBI values is generally the mean average.

In step S204, the statistical measure is used to determine whether a correction needs to be applied to the array of the candidate peaks. For example, the measure of the standard deviation may be compared to the measure of the average. If the measure of the standard deviation is more than a predetermined percentage of the measure of the average then it is determined that a correction needs to be applied. The predetermined percentage is 10% in this example but other percentage values are within the scope of the present disclosure.

If a correction is determined to be needed, the method proceeds to step 3205. In step 3205, a peak is inserted into and/or removed from the array of candidate peaks. If a correction is determined to not be needed, the method returns to step 3201 and repeats for another array of candidate peaks.

The IBI values are used to determine whether to add a peak to the array. The IBI values are compared to a statistical measure to determine whether a peak should be inserted into the array. For example, each IBI value is compared to the sum of the measure of the standard deviation and the measure of the average. If an IBI value is greater than the measure of the standard deviation and the measure of the average, then a peak is inserted between the two adjacent peaks responsible for the IBI value. The inserted peak generally has a timestamp that is located equidistantly between the two adjacent peaks although a different position may be used for the inserted peak if desired by the skilled person. Multiple peaks may be inserted into the array in this step if multiple IBI values are greater than the sum of the measure of the standard deviation and the measure of the average.

The IBI values are used to determine whether to remove a peak from the array. The IBI values are compared to a statistical measure to determine whether a peak should be removed from the array. For example, each IBI value is compared to a measure of the average minus the standard deviation. If an IBI value is less than the measure of the average minus the measure of the standard deviation, a peak is removed from the array. The removed peak is one of the peaks responsible for the IBI value. Each IBI value is determined according to the difference between a pair of consecutive peaks. One of the peaks in the pair may be removed. That is, either the earlier or the later peak in the pair may be removed. Multiple peaks may be removed from the array in this step if multiple IBI values are less than the measure of the average minus the measure of the standard deviation.

The inserting and/or removing of peaks may be repeated until the statistical measures indicate that a correction is no longer required, e.g. the measure of the standard deviation is less than a predetermined percentage of the measure of the average, or an exit condition is reached. The exit condition may be reached if the number of corrections applied exceeds a threshold value. A plurality of iterations of the corrections are performed. In each iteration, peak(s) may be added or removed to form an updated array of candidate peaks. IBI values for the updated array of candidate peaks are calculated along with statistical measures for the newly calculated IBI values. Other exit conditions such as those based on time or the number of iterations performed may be used.

After the statistical measure indicate that a correction is no longer required or the exit condition is reached, the method returns to step S201 and a second array of candidate peaks are obtained for correction. The second array of candidate peaks represent a different time window of the heartrate signal. The time window partially overlaps with the time window of the previous, first, array of candidate peaks.

Table 1 below shows a simplified example of the second array of candidate peaks. The second array of candidate peaks comprises a plurality of timestamp values 11 to T10 each representing a timestamp of a candidate peak. In this example, timestamps T1 to T3 were also present in the time window used to generate the first array of candidate peaks.

[Table 1]

The corrections applied to the first array of candidate peaks are used to determine whether to insert or remove peaks in the time region from Ti to 13. In particular, one or more timestamps of peaks that were added and/or removed from the first array of candidate peaks and whose timestamps fall within the time window of the second array of candidate peaks are obtained. These obtained timestamps are used to insert and/or remove a peak into/from the second array of candidate peaks to form a second corrected array of candidate peaks. That is, the same correction as used on the first array of candidate peaks may be applied to the part of the second array of candidate peaks that overlaps in time with the first array of candidate peaks. For example, if a peak with timestamp T2 was previously removed then the peak with timestamp T2 is removed again. For example, if a peak was previously added between the peaks at timestamp T2 and timestamp T3 then the same peak is added again.

Given that the timestamp values in the second array of candidate peaks may be slightly different to those in the first array of candidate peaks due to differences in the peak detection algorithm, an approximate correction may be performed. For example, a peak in the second array with a timestamp that is closest to a peak removed from the first array may be removed if there is no peak in the second array with the same timestamp as the peak removed from the first array.

The step of correcting the second array of candidate peaks using the corrections applied to the first array may only be performed if the statistical measures determined for the second array of candidate peaks indicate that a correction is required.

The second corrected array of candidate peaks then undergoes the same correction process as used on the first array of candidate peaks as described above. That is, IBI values are calculated for the second corrected array of candidate peaks. A measure of the standard deviation for the IBI values and a measure of the average for the IBI values is determined. If the measure of the 11 T2 13 T4 15 T6 T7 18 T9 110 standard deviation is more than a predetermined percentage of the measure of the average, one or more peaks are inserted into and/or removed from the second corrected array of candidate peaks. These corrections may be repeated until the measure of the standard deviation is less than a predetermined percentage of the measure of the average or an exit condition is reached.

In some embodiments, the described elements may be configured to reside on a tangible, persistent, addressable storage medium and may be configured to execute on one or more processors. These functional elements may in some embodiments include, by way of example, components, such as software components, object-oriented software components, class components and task components, processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, and variables.

Although the example embodiments have been described with reference to the components, modules and units discussed herein, such functional elements may be combined into fewer elements or separated into additional elements. Various combinations of optional features have been described herein, and it will be appreciated that described features may be combined in any suitable combination. In particular, the features of any one example embodiment may be combined with features of any other embodiment, as appropriate, except where such combinations are mutually exclusive. Throughout this specification, the term "comprising" or "comprises" means including the component(s) specified but not to the exclusion of the presence of others.

All of the features disclosed in this specification (including any accompanying claims, abstract 25 and drawings), and/or all of the steps of any method or process so disclosed, may be combined in any combination, except combinations where at least some of such features and/or steps are mutually exclusive.

Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise. Thus, unless expressly stated otherwise, each feature disclosed is one example only of a generic series of equivalent or similar features.

The invention is not restricted to the details of the foregoing embodiment(s). The invention 35 extends to any novel one, or any novel combination, of the features disclosed in this specification (including any accompanying claims, abstract and drawings), or to any novel one, or any novel combination, of the steps of any method or process so disclosed.

Claims

CLAIMS 1. 2. 3. 4. 5. 6. 7. 8. 9.A computer-implemented method of detecting peaks in a heartrate signal, the method comprises: obtaining a series of heartrate values representative of a heartrate signal; determining an upper percentile value and a lower percentile value from the series of heartrate values; identifying, from the series of heartrate values, a plurality of segments in which the heartrate signal goes above the upper percentile value and subsequently below the lower percentile value; and selecting a heartrate value within each of the segments as a peak for the heartrate signal.
A method as claimed in claim 1, wherein selecting the heartrate value comprises selecting the first heartrate value in a or each of the segments as a peak for the heartrate signal.
A method as claimed in claim 1 or 2, wherein the upper percentile value is greater than or equal to the 801h percentile of the heartrate signal.
A method as claimed in claim 3, wherein the upper percentile value is greater than or equal to the 85111 percentile of the heartrate signal.
A method as claimed in claim 4, wherein the upper percentile value is greater than or equal to the 901h percentile of the heartrate signal.
A method as claimed in claim 5, wherein the upper percentile value is greater than or equal to the 95th percentile of the heartrate signal.
A method as claimed in claim 6, wherein the upper percentile vale is greater than or equal to the 99111 percentile of the heartrate signal.
A method as claimed in any preceding claim, wherein the lower percentile value is less than or equal to the 70111 percentile of the heartrate signal.
A method as claimed in claim 8, wherein the lower percentile value is less than or equal to the 65' percentile of the heartrate signal.
10. A method as claimed in claim 9, wherein the lower percentile value is less than or equal to the 601h percentile of the heartrate signal.
11. A method as claimed in claim 10, wherein the lower percentile value is less than or equal to the 55' percentile of the heartrate signal.
12. A method as claimed in claim 11, wherein the lower percentile value is less than or equal to the 50111 percentile of the heartrate signal.
13. A method as claimed in any preceding claim, wherein the upper percentile value is the 99th percentile of the heartrate signal and the lower percentile value is the 501h percentile of the heartrate signal.
14. A method as claimed in any preceding claim, further comprising calculating inter-beat interval, IBI, values for the heartrate signal from the selected heartrate values.
15. A computer-readable medium having instructions recorded thereon which, when executed by a processor, cause the processor to perform the method of any preceding claim.
16. A system for detecting peaks in a heartrate signal, the system comprising a processor and a memory, the memory storing instructions which when executed by the processor cause the processor to perform operations comprising: obtaining a series of heartrate values representative of a heartrate signal; determining an upper percentile value and a lower percentile value from the series of heartrate values; identifying, from the series of heartrate values, a plurality of segments in which the heartrate signal goes above the upper percentile value and subsequently below the lower percentile value; and selecting a heartrate value within each of the segments as a peak for the heartrate signal.