GB2601178A - Method and system for adjusting a peak detection algorithm - Google Patents

Abstract

Factors such as activity level and sensor positioning can lead to varying results for peak detection algorithms, causing false peaks or causing true peaks to be missed, affecting calculation of Inter-beat interval (IBI). A wearable garment 200 has an associated electronics module 100 which receives ECG signal values indicative of a heart rate for a subject 600. These are input into an R-peak detection algorithm which generates, as an output, one or more R-R or IBI values. The output R-R values are analysed to identify the presence of an anomaly condition. A property of the R-peak detection algorithm is adjusted in response to identifying the presence of an anomaly condition. For example, this may involve adjusting a threshold level used in the R-peak detection algorithm for removing spurious R-peaks, or adjusting filtering coefficients.

Description

METHOD AND SYSTEM FOR ADJUSTING A PEAK DETECTION ALGORITHM

The present invention is directed towards a method and system for detecting peaks in an signal indicative of a heartrate of a subject such as an electrocardiogram signal. In particular, the present invention is directed towards methods and systems for improving the process for removing spurious detected peaks.

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 0 Low Energy. 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 heartrate. 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 0, R, and S signals, including the P and T waves.

Whilst lay persons may not be aware of the clinical aspects and significance of an ECG signal trace, lay persons would usually recognise the general form of such a signal trace, if only as a measure of heartrate.

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. IBI values can also be calculated based on the difference between other peaks such as between two successive S peaks. 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 detection 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. Generally, peak detection algorithms involve comparing candidate peaks against a threshold level. Candidate peaks that are below the threshold level are used to calculate the IBI i values.

Factors such as body type, activity level, sensor positioning and conductivity with the skin surface can lead to widely varying results for peak detection algorithms. This can cause false peaks to be treated as true peaks or cause true peaks to be missed. These situations affect the calculation of the IBI value which can lead to the generation of inaccurate heartrate and other heart related metrics such as heartrate variability measures. While a user could manually tweak the peak detection algorithm during each session to provide an optimal performance, this would be laborious and time intensive and may require special user knowledge to understand and interpret the heartrate signal data.

An object of the present invention is to provide an improved process for detecting peaks from a signal indicative of the heartrate such as an ECG signal.

Summary

According to the present disclosure there is provided a method and electronics module 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 adjusting a peak detection algorithm. The method comprises inputting signal values indicative of a heartrate for a subject into a peak detection algorithm which generates, as an output, one or more inter-beat interval, IBI, values. The method comprises analysing the output IBI values to identify the presence of an anomaly condition. The method comprises adjusting a property of the peak detection algorithm in response to identifying the presence of an anomaly condition.

Advantageously, the present disclosure analyses the IBI values output by the peak detection algorithm to analyse the effectiveness of the peak detection algorithm and automatically, without user input, adjust a property of the peak detection algorithm. This enables the peak detection algorithm to be optimised for the subject. The adjusting compensates for an increase in the noise in the signal due to, for example, physical properties of the subject or more contact between the sensor and a skin surface.

The updated property for the peak detection algorithm may be set as the default property during future iterations of the peak detection algorithm. This may mean that the updated peak detection algorithm is used in future sessions performed forthe subject such that the algorithm is optimised for the particular subject.

The signal indicative of the heartrate may be an 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.

The peak detection algorithm may comprise filtering the signal values; detecting one or more candidate peaks in the filtered signal values; and determining if a detected candidate peak has an amplitude less than a threshold level, and if the detected candidate peak has an amplitude less than the threshold level, removing the detected candidate peak. In other words, the peak detection algorithm may comprise removing one or more detected candidate peaks Of present) that have an amplitude less than a threshold level.

Adjusting a property of the peak detection algorithm may comprise adjusting the threshold level. The threshold level determines which of the candidate peaks are removed and which of the peaks remain and are used to calculate the IBI values. Ideally, the removed peaks are those that are spurious and caused by noise while the remaining peaks are true peaks. By adjusting the threshold level, the removal of spurious peaks can be improved and the likelihood of a true peak being accidentally removed can be reduced. In this way, the performance of the peak detection algorithm can be adjusted and improved as the noise content of the signal indicative of the heartrate changes due to parameters such as the body type of the subject, subject activity level, sensor positioning and conductivity with the skin surface.

The threshold level may be determined according to the product of an adjustable threshold value and a measure of the spectral power of the filtered signal values. Adjusting the threshold level may comprise adjusting the adjustable threshold value.

Adjusting the adjustable threshold value may comprises increasing or decreasing the adjustable threshold value by a predetermined amount. The adjustable threshold value may be increased or decreased in a predetermined way. For example, the method may cycle through different adjustable threshold values each time the peak detection algorithm is determined to need adjustment.

Adjusting a property of the peak detection algorithm may comprise adjusting one or more filtering coefficients used to filter the ECG signal values.

Analysing the output IBI values to identify the presence of an anomaly condition may comprise deriving a measure of the heartrate of the subject from the output IBI values, and analysing the measure of the heartrate to determine whether an anomaly condition is present.

An anomaly condition may be present if the measure of the heartrate is above a predetermined threshold value. The anomaly condition may be present if the measure of the heartrate is above 240 beats-per-minute. Other values of the heartrate are within the scope of the present disclosure. An anomaly condition may be present if the difference between successive IBI values indicates a heartrate variance greater than a predetermined threshold value. The anomaly condition may be present if the heartrate has a variance of more than 20 beats-per-minute.

An anomaly condition may be present if the measure of the heartrate is below a predetermined threshold value. The anomaly condition may be present if the measure of the heartrate is below 40 beats per minute. Other values of the heartrate are within the scope of the present disclosure.

The plurality of IBI values may represent at least a predetermined time interval of heartrate data for the subject. The IBI values may only be determined once at least N signal values are obtained. Here, 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 peaks within the window of filtered signal values. For example, N may be selected such that the signal values corresponding to at least 4 seconds of data are obtained to ensure that there are at least 2 peaks in any window.

If an anomaly condition is present, the method may comprise starting a timer, and, while the timer is running, repeatedly inputting signal values for the subject into a peak detection algorithm which generates, as an output, one or more IBI values, and wherein the output IBI values are analysed to identify the presence of an anomaly condition.

If more than a number P of anomaly conditions are identified from the IBI values output while the timer is running, the step of adjusting a property of the R-peak detection algorithm may be performed. A property of the peak detection algorithm may be adjusted if 1 or more anomaly conditions are identified from the IBI values.

Advantageously, the peak detection algorithm may not be adjusted if an initial anomaly condition is detected, and instead may only be adjusted if an anomaly condition is subsequently detected during the running of the timer. In this way, the method checks whether the anomaly condition is persistent and only adjusts the peak detection algorithm if so. This reduces unnecessary adjustment of the peak detection algorithm due to temporary fluctuations in the signal.

The adjusting of a property of the peak detection algorithm may be performed if less than a number Q of recent adjustments to the peak detection algorithm have been performed. Q may be a number greater than or equal to 5.

The method may be for detecting R-peaks in an ECG 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 signal such as S-peaks or other characteristic peaks in signals indicative of the heartrate of the subject.

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 electronics module for a wearable article, the electronics module comprising: an interface arranged to couple with sensing components to record signals indicative of a heartrate for a subject; and a controller operable to: input signal values for the signal into a peak detection algorithm which generates, as an output, a plurality of IBI values; analyse the output IBI values to identify the presence of an anomaly condition; and adjust a property of the peak detection algorithm in response to identifying the presence of an anomaly condition.

The peak detection algorithm may comprise: filtering the signal values; detecting one or more candidate peaks in the filtered signal values; and determining if a detected candidate peak has an amplitude less than a threshold level, and if the detected candidate peak has an amplitude less than the threshold level, removing the detected candidate peak. In other words, the peak detection algorithm may comprise removing one or more detected candidate peaks Of present) that have an amplitude less than a threshold level.

In the above example aspects of the disclosure, the property of the peak detection algorithm may be adjusted according to subject specific information. The threshold level may be determined at least in part according to the subject specific information. The subject specific information may indicate that the signal-to-noise ratio has changed. The subject specific information may comprise activity data for the subject. The activity data may indicate an activity level for the subject. The activity data may indicate that an activity level for the subject has changed. The subject specific information may comprise information indicating a characteristic of the subject. The characteristic of the subject may comprise one or more of the age, weight, body fat level, gender, ethnicity, fitness level, diet, medical history, or lifestyle of the subject.

Brief Description of the Drawinqs

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 flow diagram for an example method of detecting R-peaks according to aspects of the present disclosure; Figure 8 shows a flow diagram for another example method of detecting R-peaks according to

aspects of the present disclosure; and

Figure 9 shows a flow diagram for yet another example method of detecting R-peaks according to aspects of the present disclosure.

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

B

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 electroencephalogram or magnetoencephalogram s!gnals.

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. &osignals may be electrical or nonelectrical signals. Signal variations can be time variant or spatialiy variant.

Sensing components may be used for measuring one or a combination of bioelectrical, bioimpedance, biochemical, biomechanical, bioacoustics, biooptical 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 biooptical measurements include orthopantomogram (OPG). The biothermal measurements include skin temperature and core body temperature measurements.

The examples described below refer to algorithms for detecting P-peaks in electrocardiograms It will be appreciated that the present disclosure is not limited to this example and other peaks and other types of signals indicative of the heartrate may be used.

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 0 Low Energy, and near-field communication (NFC).

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. The sensors 209, 211 may be electrodes. 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 biosig nal 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 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 0 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 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 (IAN), a wireless metro area network (WMAN), a wireless local area network VLAN), a wireless personal area network (WPAN), Bluetooth OD Low Energy, Bluetooth e 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-IoT, 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 VAN), 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), a Global Navigation Satellite System (GNUS), 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 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. The ADC front end 139 forms part of the interface that couples the garment sensors 209, 211 (e.g. electrodes 209, 211) to the controller 103.

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 such as notch filters, high pass and low pass filters for mains noise, 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 and the effect of impact forces on the electrical components.

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. Signals are output to the controller 103 via the SPI 173.

The digital signal values output to the controller 103 are stored in a FIFO data buffer. The controller 103 performs operations to detect R-peaks from the digital signal values. The operations are performed in real-time while the ADC front end 139 are outputting new digital signals to the controller 103.

Figure 7 provides a flow diagram for an example method performed by the controller 103 for identifying R-peaks in the ECG signal using the ECG digital signal values for the subject obtained from the ADC front end 139. The R-peaks are used to calculate R-R interval values. The R-R intervals are used to adjust and improve the performance of the R-peak detection algorithm used to identify the R-peaks.

In step 8101 the controller 103 inputs the ECG signal values for the subject into the R-peak detection algorithm. The R-peak detection algorithm generates, as an output, one or a plurality of R-R values. R-R values represent the time (usually in milliseconds) between consecutive R-peaks and thus represent the time between consecutive heartbeats for the subject. The controller 103 thus obtains one or a plurality of R-R values. The ECG signal values are obtained from the ADC front end 139. The R-R values cover a time period of recorded ECG signal values.

Generally, the R-peak detection algorithm only determines the R-R values once R-peaks representing the predetermined time period are obtained. The time period is generally selected such that at least two R-peaks will be detected in the ECG signal values so as to enable an RR value to be determined. The time period may be 4 seconds or more.

In step 8102, the controller 103 analyses the output R-R values to identify the presence of an anomaly condition. The anomaly condition indicates that the R-R values have a property which is unexpected. Generally, a heartrate measure such as a measure of the heartrate in beats per minute is derived, by the controller 103, from the R-R values. The anomaly condition is determined to be present if the measured heartrate is abnormally high (e.g. above a first, maximum, predetermined threshold) or abnormally low (e.g. below a second, minimum, predetermined threshold). Heartrate values above or below these thresholds would be outside the range expected for human adults and thus indicate a problem with the R-peak detection.

In step 8103, the controller 103 determines whether an anomaly condition is present. If an anomaly condition is not present, the method returns to step S101. If an anomaly condition is present, the method proceeds to step S104.

In step 8104 the controller 103 adjusts a property of the R-peak detection algorithm in response to identifying the present of the anomaly condition. In this way, the controller analyses the R-R values output by the R-peak detection algorithm to analyse the effectiveness of the R-peak detection algorithm. The controller 103 automatically, without user input, adjusts a property of the R-peak detection algorithm. This enables the R-peak detection algorithm to be optimised for the subject.

The adjustment to the R-peak detection algorithm will generally depend on the type of R-peak detection algorithm used and/or the type of anomaly condition detected. R-peak detection algorithms typically comprise a filtering stage which filters the ECG signal values and a peak removal stage that removes detected candidate R-peaks that have an amplitude less than a threshold level. In some examples, the adjustment to the R-peak detection algorithm can involve adjusting one or both of filtering coefficients used in the filtering of the ECG signal values and the threshold level used to remove detected peaks The threshold level determines which candidate R-peaks detected by the R-peak detection algorithm are removed and which remain as true R-peaks used for the calculation of the R-R intervals.

If the R-R intervals obtained from previous ECG signal values indicate that the subject's heartrate is abnormally high (e.g. above 240 beats-per-minute), this indicates that the threshold level is incorrectly set, and that spurious peaks caused by noise are being treated as true R-peak values. Advantageously, the controller 103 implementing the techniques of the present disclosure adjusts the threshold level based on the obtained R-R intervals to correct for this too high threshold level. With a higher threshold level, more spurious peaks caused by noise are rejected, and thus the performance of the R-peak detection algorithm is improved. The controller 103 is therefore able to increase the threshold level if the heartrate is above the maximum threshold.

If the R-R intervals obtained from previous ECG signal values indicate that the subject's heartrate is abnormally low (e.g. below 40 beats-per-minute), this indicates that the threshold level is incorrectly set, and that true R-peaks are being incorrectly rejected as being caused by noise. Advantageously, the controller 103 implementing the techniques of the present disclosure adjusts the threshold level based on the obtained R-R intervals to correct for this too low threshold level. With a lower threshold level, fewer true R-peaks are rejected, and thus the performance of the R-peak detection algorithm is improved. The controller 103 is therefore able to decrease the threshold level if the heartrate is below the minimum threshold.

The controller 103 is not required to actively determine whether to increase or decrease the threshold level based on detecting the presence of the anomaly condition. The controller 103 may just change the threshold level in a predetermined way regardless of whether the measured heartrate of the subject is abnormally high or abnormally low.

The controller 103 may access a prestored list of threshold levels to use in the R-peak detection algorithm. The list may be stored on firmware. The controller 103 may cycle through a predetermined list of threshold levels until an optimum threshold level is found. The optimum threshold level may be a threshold level which does not result in an anomaly condition being detected for the output R-R values. That is, the controller 103 may repeat steps S101 to S104 a plurality of times until an optimum threshold level is found. In some examples, none of the predetermined threshold levels may be an optimum value. In this case, the controller 103 may select one of the threshold levels for use in subsequent iterations of the R-peak detection algorithm or may generate a prompt to be output to the userto indicate that the R-peak detection algorithm is not functioning optimally. The user may then try and remedy the issue by, for example, improving the contact between the skin surface of the subject and the sensors such as by repositioning the sensors or applying a conductive medium to the skin surface.

In a preferred implementation the R-peak detection algorithm comprises a stage of filtering the ECG signal values. Many different filtering operations can be used to aid in the detection of R-peaks. Example filters include notch filters, low pass filters, high pass filters, and bandpass filters. The R-peak detection algorithm further comprises a stage of detecting one or more candidate R-peaks in the filtered ECG signal values. Many peak detection approaches may be used. These peak detection approaches typically involve detecting local maxima in the ECG signal values. Further, the R-peak detection algorithm comprises a stage of removing detected candidate R-peaks that have an amplitude less than a threshold level. This stage is provided to remove spurious peaks such as those due to noise. The remaining one or more R-peaks are desired to be true R-peaks which can be used to calculate the one or more R-R intervals for the subject. Only one R-R interval may be determined if only one R-peak remains. The R-R interval will be determined using the timestamp of the last R-peak found in the previous window of data.

One or more additional steps may be performed prior to calculating the R-R intervals such as to further check the remaining peaks and remove or compensate for any remaining spurious peaks.

Although the number and extent of the spurious remaining speaks is at least reduced due to the adjustment of the R-peak detection algorithm using the R-R values. These spurious peaks may be due to noise spikes, ectopic beats or other ECG components. An example process for detecting spurious peaks is disclosed in Mateo J, Laguna P. Analysis of heartrate variability in the presence of ectopic beats using the heart timing signal. IEEE Trans Biomed Eng. 2003 Mar;50(3):334-43.

The calculation of the R-R values may only be performed when at least N filtered ECG signal values are obtained. Here, 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 peaks within the window of filtered signal values. For example, N may be selected such that filtered ECG signal values corresponding to at least 4 seconds of data are obtained to ensure that there are at least 2 peaks in any window. The number N will depend on the sampling rate of the signal values provided to the controller 103. For example, if the sampling rate is 512Hz and at least 4 seconds of data are required, then N = 2048. Other values of N are within the scope of the present disclosure.

If less than N samples of filtered signal values have been obtained, then additional samples are gathered and filtered and use to detect R-peaks as per the R-peak detection algorithm described above.

Figure 8 provides a flow diagram for another example method performed by the controller 103 for identifying R-peaks in the ECG signal using the ECG digital signal values for the subject obtained from the ADC front end 139. The R-peaks are used to calculate R-R interval values.

The R-R intervals are used to adjust and improve the performance of the R-peak detection algorithm used to identify the R-peaks. Steps S201, S202, S203, and S210 correspond to steps S101 to 5104 in Figure 7.

In step S201 the controller 103 inputs the ECG signal values for the subject into the R-peak detection algorithm. The R-peak detection algorithm generates, as an output; one or a plurality of R-R values. The controller 103 thus obtains one or a plurality of R-R values. The ECG signal values are obtained from the ADC front end 139. The R-R values cover a time period of recorded ECG signal values. Generally, the R-peak detection algorithm only determines the R-R values once R-peaks representing the predetermined time period are obtained. The R-peak detection algorithm may be the same as the R-peak detection algorithm described above in relation to Figure 7.

In step S202, the controller 103 analyses the output R-R values to identify the presence of an anomaly condition. The anomaly condition indicates that the R-R values have a property which is unexpected. Generally, a heartrate measure such as a measure of the heartrate in beats per minute is derived, by the controller 103, from the R-R values. The anomaly condition is determined to be present if the measured heartrate is abnormally high (e.g. above a first predetermined threshold) or abnormally low (e.g. below a second predetermined threshold).

In step S203, the controller 103 determines whether an anomaly condition is present. If an anomaly condition is not present, the method returns to step 8201. If an anomaly condition is present, the method proceeds to step 8204.

In step S204 the controller 103 starts a timer. The timer runs for a predetermined time period during which the controller 103 looks for anomaly conditions in newly generated R-R values.

The predetermined time period may be any period set by the skilled person based on, for example, the application of the R-peak detection algorithm. Generally, the predetermined time period may be 20 seconds or more, 30 seconds or more 40 seconds or more, 50 seconds or more, or 60 seconds or more. The predetermined time period may be 10 minutes or less, 5 minutes or less, 4 minutes or less, 3 minutes or less, or 2 minutes or less.

In step S205, the controller 103 obtains ECG signal values from the ADC front end 139 and inputs the ECG signal values for the subject into the R-peak detection algorithm. The R-peak detection algorithm generates, as an output, one or a plurality of R-R values. The controller 103 thus obtains one or a plurality of R-R values. The R-R values cover a time period of recorded ECG signal values. Generally, the R-peak detection algorithm only determines the R-R values once R-peaks representing the predetermined time period are obtained. The R-peak detection algorithm may be the same as the R-peak detection algorithm described above in relation to Figure 7.

In step S206, the controller 103 analyses the output R-R values to identify the presence of an anomaly condition. The anomaly condition indicates that the R-R values have a property which is unexpected. Generally, a heartrate measure such as a measure of the heartrate in beats per minute is derived, by the controller 103, from the R-R values. The anomaly condition is determined to be present if the measured heartrate is abnormally high (e.g. above a first predetermined threshold) or abnormally low (e.g. below a second predetermined threshold).

In step S207, the controller 103 determines whether the timer has elapsed. If not, the controller 103 returns to step S205. If the timer has elapsed, the controller 103 proceeds step S208. While the timer is running, the controller 103 determines the number of anomaly conditions present in the R-R values obtained during the predetermined time period set by the timer.

In step S208, the controller 103 determines whether P or more anomaly conditions have been identified during steps 5205 and S206. P is a number greater than or equal to 1. P may be selected as appropriate by the skilled person based on factors such as the desired frequency of adjustments to the R-peak detection algorithm and the length of the timer running in steps 3204 to S207. Determining the number of anomaly conditions detected while the timer is running is beneficial in reducing the number of unnecessary adjustments to the R-peak detection algorithm.

For example, an anomaly condition detected in step S203 may be caused by a temporary, nonrepeatable, issue such as sudden subject motion or a noise spike. By checking whether the anomaly condition persists, the controller 103 is able to confirm whether there is a recurring issue in the R-peak detection algorithm that needs correction. In this way, the R-peak detection algorithm is only adjusted when required.

If less than P anomaly conditions are identified then the controller 103 returns to step 3201 and the R-peak detection algorithm is not adjusted.

If P or more anomaly conditions are identified then the controller 103 proceeds to step 8209. In step S209 the controller 103 determines whether Q or more recent adjustments have been performed to the R-peak detection algorithm. Here, recent adjustments may refer to consecutive adjustments or adjustments performed over a time window (e.g. 10 minutes, 1 hour) or a session. Q is a number greater than or equal to 1. Q may be selected as appropriate by the skilled person based on factors such as the number of potential thresholds the controller 103 can cycle through. Q may be 5 or more, 10 or more, 20 or more, or 30 or more. Q may be less than 100, less than 75, or less than 50.

Determining the number of recent adjustments performed is beneficial in determining whether adjusting the R-peak detection algorithm is having a meaningful effect and provides a route for exiting the adjustment routine if the R-peak detection is unable to be improved. This stops the controller 103 from cycling through different threshold levels which do not improve or optimise the R-peak detection algorithm. This situation may occur if, for example, hardware issues are present or if there are problems with the coupling between the skin surface and the sensors used to measure the ECG signal. Adjusting the R-peak detection algorithm may not be beneficial in these circumstances and instead user intervention may be required.

If less than Q adjustments have been performed, the controller 103 proceeds to step 8210 and adjusts a property of the peak detection algorithm such as by modifying the threshold level using prestored threshold information. The controller 103 then returns to step S204 and a new timer is started for detecting the presence of anomaly conditions. The R-peak detection algorithm is adjusted by the controller 103 until either less than P anomaly conditions are identified in step S208 (indicating that the optimum R-peak detection algorithm has been selected) or more than Q recent adjustments to the R-peak detection algorithm have been performed (indicating that the R-peak detection algorithm cannot be optimised and user intervention is required).

If more than Q adjustments have been performed, the controller 103 proceeds to step 3211 and generates a prompt for notifying a user that the R-peak detection algorithm is not working correctly. The electronics module 100 may communicate with a user electronic device to cause the prompt to be displayed or otherwise output to the user. In other examples, the electronics module 100 may directly output the prompt to the user. The user may be the same as or different to the subject having their ECG signal measured. The prompt may trigger the user to try and remedy the issue by, for example, improving the contact between the skin surface of the subject and the sensors such as by repositioning the sensors or applying a conductive medium to the skin surface. Additionally, or separately, the controller 103 may select one of the threshold levels for use in subsequent iterations of the R-peak detection algorithm.

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 smartphone, 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.

Figure 9 provides a flow diagram for an example method performed by the controller 103 for detecting R-peaks from the digital signals stored in the FIFO data buffer. This R-peak detection algorithm is useable in the methods described above in reference to Figures 7 and 8, and can use the threshold level or other adjustable parameter such as a filter coefficient set according to the adjustment procedure described in the examples of Figures 7 and 8.

In step S301, the controller 103 reads signal values from the data buffer. Each of the signal values is a value that represents the amplitude of the ECG signal at a particular time point.

In step 5302, the controller 103 detrends the signal values so as to remove baseline wander and/or other low frequency components. In an example operation, the controller 103 calculates the trend in the signal values and then subtracts the calculated trend from each of the signal values.

Calculating the trend comprises identifying the maximum and minimum signal values read from the data buffer. The maximum signal value is added to a buffer that stores the maximum signal values obtained over time. The minimum signal value is added to a buffer that stores the minimum signal values obtained over time. The current trend is then calculated by calculating the average of the maximum value stored in the buffer of maximum signal values and the minimum value stored in the buffer of minimum signal values.

As explained above, the detrended signal values are calculated by subtracting the calculated current trend from each of the signal values. The detrended signal values are added to a FIFO detrended signal buffer.

In step 5303, the detrended signal values are filtered. The filtering is performed to remove components from the signal that do not resemble R-peaks. A bandpass filter centred around the frequency associated with the shape and width of the R-peak can be used to perform this task. Some filtering approaches use a bandpass filter with a central frequency in the range of 17 to 19 Hz. IIR or FIR filters may be used, however, they are generally not effective due to ripples and lobes that may be present around the R-peaks in the ECG signal. The interaction between these secondary peaks and other components of the ECG signal can lead to ambiguity in the identity of the actual main peak.

Preferred bandpass filtering approaches analyse a signal of the instantaneous amplitude associated with the R-peak frequency. These approaches exploit the fact that R-peaks are approximately symmetrical features which means that the location of the peak in the spectral amplitude is normally close to the location of the centre of the R-peak itself. The signal of instantaneous amplitude can be obtained using a complex filter and by calculating the absolute magnitude of the real and imaginary component for each filtered signal value.

In a preferred implementation, the complex filter used is a complex Morlet wavelet. The Morlet wavelet has optimal frequency resolution due to its Gaussian envelope. The Morlet wavelet is also useful because it is symmetrical across the y-axis which means that only half of the filter coefficients need to be stored in RAM.

The filtered signal values are added to a FIFO filtered signal buffer.

In step 8304, the controller 103 determines whether at least N filtered signal values have been obtained. Here, 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 peaks within the window of filtered signal values. For example, N may be selected such that the filtered signal buffer contains at least 4 seconds of data to ensure that there are at least 2 peaks in any window. The number N will depend on the sampling rate of the signal values provided to the controller 103. For example, if the sampling rate is 512Hz and at least 4 seconds of data are required, then N = 2048. Other values of N are within the scope of the present disclosure.

If less than N samples of filtered signal values have been obtained then the method returns to step S101 so that additional samples are gathered, filtered, and added to the filtered signal buffer. Steps S201 to 5204 are repeated until the N signal values are obtained.

If N or more samples of the filtered signal values have been obtained, the method proceeds to step S205.

In step S305, the controller 103 detects peaks in the filtered signal values. At this stage, the controller 103 is identifying any peaks, including small and spurious peaks, in the filtered signal values. The peak detection process identifies local maxima in the signal values. Peak detection can be performed by simply looking for negative gradients in the filtered signal values.

In step 8306, the controller 103 removes detected peaks that have an amplitude less than a threshold level. The thresholding process is intended to remove peaks that are not R-peaks in the ECG signal. The thresholding level is determined according to an adjustable threshold value multiplied by the average spectral power for the filtered signal values. Using the average spectral power enables the thresholding level to adapt based on the power of the signal. The adjustable threshold value can be set according to the method of Figure 7 or 8.

One or more additional steps may be performed such as to check the remaining peaks after step 8206 and remove or compensate for spurious remaining peaks. These spurious peaks may be due to noise spikes, ectopic beats or other ECG components. An example process for detecting spurious peaks is disclosed in Mateo J, Laguna P. Analysis of heartrate variability in the presence of ectopic beats using the heart timing signal. IEEE Trans Biomed Eng. 2003 Mar;50(3):334-43.

In summary, there is provided a method and system for adjusting an R-peak detection algorithm.

ECG signal values for a subject are input into an R-peak detection algorithm which generates, as an output, one or more R-R values (S101). The output R-R values are analysed to identify the presence of an anomaly condition (S102). A property of the R-peak detection algorithm is adjusted in response to identifying the presence of an anomaly condition (8104). This may involve adjusting a threshold level used in the R-peak detection algorithm for removing spurious R-peaks.

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 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 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 (24)

1. CLAIMS1. A computer-implemented method of adjusting a peak detection algorithm, the method comprising: inputting signal values indicative of a heartrate for a subject into a peak detection algorithm which generates, as an output, one or more inter-beat interval, IBI, values; analysing the output IBI values to identify the presence of an anomaly condition; and adjusting a property of the peak detection algorithm in response to identifying the presence of an anomaly condition.
2. 2. A method as claimed in claim 1, wherein the peak detection algorithm comprises: filtering the signal values; detecting one or more candidate peaks in the filtered signal values; and determining if a detected candidate peak has an amplitude less than a threshold level, and if the detected candidate peak has an amplitude less than the threshold level, removing the detected candidate peak.
3. 3. A method as claimed in claim 2, wherein adjusting a property of the peak detection algorithm comprises adjusting the threshold level.
4. 4. A method as claimed in claim 3, wherein the threshold level is determined according to the product of a adjustable threshold value and a measure of the spectral power of the filtered signal values, and wherein adjusting the threshold level comprises adjusting the adjustable threshold value.
5. 5. A method as claimed in claim 4, wherein adjusting the adjustable threshold value comprises increasing or decreasing the adjustable threshold value by a predetermined amount.
6. 6. A method as claimed in any of claims 2 to 5, wherein adjusting a property of the peak detection algorithm comprises adjusting one or more filtering coefficients used to filter the signal values.
7. 7. A method as claimed in any preceding claim, wherein analysing the output IBI values to identify the presence of an anomaly condition comprises deriving a measure of the heartrate of the subject from the output IBI values, and analysing the measure of the heartrate to determine whether an anomaly condition is present.
8. 8. A method as claimed in claim 7, wherein an anomaly condition is present if the measure of the heartrate is above a predetermined threshold value.
9. 9. A method as claimed in claim 8, wherein the anomaly condition is present if the measure of the heartrate is above 240 beats-per-minute.
10. 10. A method as claimed in in any of claims 7 to 9, wherein an anomaly condition is present if the measure of the heartrate is below a predetermined threshold value.
11. 11. A method as claimed in claim 10, wherein the anomaly condition is present if the measure of the heartrate is below 40 beats per minute.
12. 12. A method as claimed in any preceding claim, wherein the plurality of IBI values represent at least a predetermined time interval of heartrate data for the subject.
13. 13. A method as claimed in any preceding claim, wherein if an anomaly condition is present, the method comprises starting a timer, and, while the timer is running, repeatedly inputting signal values for the subject into an peak detection algorithm which generates, as an output, one or more IBI values, and wherein the output IBI values are analysed to identify the presence of an anomaly condition.
14. 14. A method as claimed in claim 13, wherein if more than a number P of anomaly conditions are identified from the IBI values output while the timer is running, the step of adjusting a property of the peak detection algorithm is performed.
15. 15. A method as claimed in claim 14, wherein a P is greater than or equal to 1.
16. 16. A method as claimed in any preceding claim, wherein the adjusting of a property of the R-peak detection algorithm is performed if less than a number Q of recent adjustments to the R-peak detection algorithm have been performed.
17. 17. A method as claimed in claim 16, wherein Q is greater than or equal to 5.
18. 18. A method as claimed in any preceding claim, wherein the signal is an electrocardiogram signal.
19. 19. A method as claimed in any of claims 1 to 17, wherein the signal is a photoplethysmography signal.
20. 20. A method as claimed in any of claims 1 to 17, wherein the signal is an electromagnetic cardiogram signal.
21. 21. A method as claimed in any of claims 1 to 17, wherein the signal is a ballistocardiogram signal.
22. 22. A computer readable medium having instructions recorded thereon which, when executed by a processor, cause the processor to perform the method as claimed in any of claims 1 to 21
23. 23. An electronics module for a wearable article, the electronics module comprising: an interface arranged to couple with sensing components to record signals indicative of a heartrate for a subject; and a controller operable to: input signal values for the signal into an peak detection algorithm which generates, as an output, a plurality of inter-beat interval, IBI, values; analyse the output IBI values to identify the presence of an anomaly condition; and adjust a property of the peak detection algorithm in response to identifying the presence of an anomaly condition.
24. 24. An electronics module as claimed in claim 23, wherein the peak detection algorithm 20 comprises: filtering the signal values; detecting one or more candidate peaks in the filtered signal values; and determining if a detected candidate peak has an amplitude less than a threshold level, and if the detected candidate peak has an amplitude less than the threshold level, removing the detected candidate peak.