Biological signal processing device, biological signal measurement device, biological signal processing method

Abstract

To provide a new technology for highly reliably determining the existence of a P wave from time series data on ECG signals using signal processing.SOLUTION: A biological signal processing device separates a plurality of heart rate waveforms extracted from time series data on ECG signals into a plurality of to a plurality of heart rate waveform groups that at least include a first heart rate waveform group and a second heart rate waveform group; from each of the first heart rate waveform group and the second heart rate waveform group, acquires a first representative P wave section waveform and a second representative P wave section waveform, which are waveforms of representative P wave sections; and determines that a P wave is included in the time series data on the ECG signals when similarity between the first representative P wave section waveform and the second representative P wave section waveform is higher than a threshold.SELECTED DRAWING: Figure 12

Description

[Technical field]

[0001] The present invention relates to a biological signal processing device, a biological signal measuring device, and a biological signal processing method. [Background technology]

[0002] Atrial fibrillation (AF) is a type of arrhythmia in which the atria contract rapidly due to abnormal electrical signals generated from a location other than the sinus node, resulting in an irregular pulse (heartbeat interval). Atrial fibrillation not only causes palpitations and shortness of breath, interfering with daily life, but can also lead to serious illnesses such as cerebral infarction and heart failure, so early detection and appropriate treatment of atrial fibrillation are desirable.

[0003] During an attack of atrial fibrillation, two characteristics appear on the electrocardiogram (ECG): "disturbance of the heartbeat interval" and "disappearance of P waves". Therefore, to realize automatic detection of atrial fibrillation by signal processing, it is required to accurately evaluate the disturbance of the heartbeat interval and detect the P waves (determination of the presence or absence of P waves) from the time series data of the ECG signal. Here, the disturbance of the heartbeat interval can be captured by the fluctuation of the RRI (RR interval; the time from the peak of the R wave to the peak of the next R wave) in the ECG signal, so it can be evaluated relatively easily and accurately. However, it is quite difficult to accurately determine the presence or absence of P waves. This is because the amplitude of the P wave is much smaller than that of the R wave, and it is easily contaminated by noise (it is difficult to distinguish it from noise). In addition, the waveform of the P wave (amplitude, shape, direction (polarity), etc.) varies from person to person, which increases the difficulty of detecting the P wave.

[0004] Patent Document 1 discloses an idea to generate a P wave template based on an ECG signal, extract only waveforms from each heartbeat waveform of the ECG signal that have a high cross-correlation with the template, and take the average of these to accurately detect P waves. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] U.S. Pat. No. 5,840,038 [Non-patent literature]

[0006] [Non-Patent Document 1] Saclova, L., Nemcova, A., Smisek, R. et al., "Reliable P wave detection in pathological ECG signals", Sci Rep 12, 6589 (2022). Summary of the Invention [Problem to be solved by the invention]

[0007] With conventional detection algorithms, there were cases where the system would erroneously detect "P waves present" when there were no P waves, or conversely, erroneously determine "no P waves" when P waves were present. Such erroneous detection or determination would affect subsequent atrial fibrillation determinations, significantly reducing the accuracy and reliability of atrial fibrillation detection.

[0008] The present invention has been made in consideration of the above-mentioned circumstances, and an object of the present invention is to provide a novel technique for reliably determining the presence of P waves from time-series data of an EGG signal by signal processing. [Means for solving the problem]

[0009] The present disclosure relates to an acquisition unit that acquires time series data of an ECG signal measured from a user, and a first P wave that is set based on a time point of a first R wave in the time series data of the ECG signal. and a P-wave determination unit that extracts a first P-wave section waveform from a first P-wave section, and extracts a second P-wave section waveform from a second P-wave section set based on a time point of a second R wave different from the first R wave, and determines that a P wave is included in the first P-wave section and the second P-wave section when the similarity between the first P-wave section waveform and the second P-wave section waveform is higher than a threshold.

[0010] The present disclosure includes a biological signal processing device having an acquisition unit that acquires time series data of an ECG signal measured from a user, and a P wave determination unit that extracts at least a first heartbeat waveform group and a second heartbeat waveform group from the time series data of the ECG signal, extracts a waveform of a P wave section set based on a time point of an R wave from each heartbeat waveform of the first heartbeat waveform group, and extracts a waveform of a P wave section set based on a time point of an R wave from each heartbeat waveform of the second heartbeat waveform group, generates a first representative P wave section waveform based on the waveform group of a plurality of P wave sections extracted from the first heartbeat waveform group, and generates a second representative P wave section waveform based on the waveform group of a plurality of P wave sections extracted from the second heartbeat waveform group, and determines that a P wave is included in the first heartbeat waveform group and the second heartbeat waveform group when a similarity between the first representative P wave section waveform and the second representative P wave section waveform is higher than a threshold value.

[0011] The present disclosure includes a biological signal processing device having an acquisition unit that acquires time series data of an ECG signal measured from a user, a candidate detection unit that detects candidates for P waves from the time series data of the ECG signal, and a P wave determination unit that, when a candidate for a P wave is detected by the candidate detection unit, divides a plurality of heartbeat waveforms extracted from the time series data of the ECG signal into a plurality of heartbeat waveform groups including at least a first heartbeat waveform group and a second heartbeat waveform group, acquires a first representative P wave section waveform and a second representative P wave section waveform, which are waveforms of representative P wave sections, from each of the first heartbeat waveform group and the second heartbeat waveform group, and determines that a P wave is included in the time series data of the ECG signal when a similarity between the first representative P wave section waveform and the second representative P wave section waveform is higher than a threshold value.

[0012] The P wave determination unit may determine that it is impossible to determine whether or not a P wave is included in the time series data of the ECG signal when the similarity between the first representative P wave section waveform and the second representative P wave section waveform is equal to or less than the threshold value.

[0013] The P wave determination unit may assign, among the multiple heartbeat waveforms extracted from the time series data of the ECG signal, those extracted in the first half to the first heartbeat waveform group and those extracted in the latter half to the second heartbeat waveform group. Alternatively, the P wave determination unit may alternately or randomly select, among the multiple heartbeat waveforms extracted from the time series data of the ECG signal, those to be assigned to the first heartbeat waveform group and those to be assigned to the second heartbeat waveform group.

[0014] The P wave determination unit may divide the multiple heartbeat waveforms extracted from the time series data of the ECG signal into the first heartbeat waveform group and the second heartbeat waveform group so that the number of heartbeat waveforms in the first heartbeat waveform group is equal to the number of heartbeat waveforms in the second heartbeat waveform group.

[0015] The P wave determination unit may extract a waveform of a P wave section set based on the time point of an R wave from each heartbeat waveform of the first heartbeat waveform group, obtain an average, median, or mode of the waveforms of the multiple P wave sections extracted from the first heartbeat waveform group as the first representative P wave section waveform, and extract a waveform of a P wave section set based on the time point of an R wave from each heartbeat waveform of the second heartbeat waveform group, and obtain an average, median, or mode of the waveforms of the multiple P wave sections extracted from the second heartbeat waveform group as the second representative P wave section waveform.

[0016] The P wave determination unit is configured to determine a correlation between the first representative P wave section waveform and the second representative P wave section waveform. The degree of similarity between the first representative P-wave section waveform and the second representative P-wave section waveform may be evaluated by correlation.

[0017] The candidate detection unit may generate a representative heartbeat waveform that is a representative heartbeat waveform from a plurality of heartbeat waveforms extracted from time-series data of the ECG signal, and detect candidates for P waves from the representative heartbeat waveform.

[0018] The candidate detection unit may extract a feature from a P wave section that is set based on a time point of an R wave in the representative heartbeat waveform, and determine that there is a candidate for a P wave when the feature satisfies a predetermined condition.

[0019] The candidate detection unit may select data from a low-noise section in which the noise level is lower than a predetermined level from the time-series data of the ECG signal, and generate the representative heartbeat waveform using a plurality of heartbeat waveforms extracted from the data from the low-noise section.

[0020] The P wave determination unit may determine that it is impossible to determine whether or not a P wave is included in the time-series data of the ECG signal when the number of heartbeat waveforms extracted from the data in the low-noise section does not reach a predetermined number.

[0021] When no P wave candidate is detected by the candidate detection unit, the P wave determination unit evaluates the degree of smoothness of a P wave section that is set based on the time point of an R wave in the representative heartbeat waveform, and when the degree of smoothness is equal to or greater than a predetermined value, determines that a P wave is not included in the time series data of the ECG signal, and otherwise determines that it is impossible to determine whether or not a P wave is included in the time series data of the ECG signal.

[0022] The present disclosure includes a biosignal measuring device having an ECG sensor attached to a user's limb, and a biosignal processing device that processes an ECG signal measured by the ECG sensor.

[0023] The present disclosure includes a biosignal processing method executed by a biosignal processing device, the biosignal processing method having the steps of acquiring time series data of an ECG signal measured from a user, extracting a first P-wave section waveform from a first P-wave section set based on the time point of a first R wave in the time series data of the ECG signal, and extracting a second P-wave section waveform from a second P-wave section set based on the time point of a second R wave different from the first R wave, and determining that P waves are included in the first P-wave section and the second P-wave section when the similarity between the first P-wave section waveform and the second P-wave section waveform is higher than a threshold value.

[0024] The present disclosure includes a biosignal processing method executed by a biosignal processing device, the biosignal processing method including the steps of: acquiring time series data of an ECG signal measured from a user; extracting at least a first heartbeat waveform group and a second heartbeat waveform group from the time series data of the ECG signal, extracting a waveform of a P-wave section set based on a time point of an R wave from each heartbeat waveform of the first heartbeat waveform group, and extracting a waveform of a P-wave section set based on a time point of an R wave from each heartbeat waveform of the second heartbeat waveform group, generating a first representative P-wave section waveform based on the waveform group of a plurality of P-wave sections extracted from the first heartbeat waveform group, and generating a second representative P-wave section waveform based on the waveform group of a plurality of P-wave sections extracted from the second heartbeat waveform group, and determining that a P wave is included in the first heartbeat waveform group and the second heartbeat waveform group when a similarity between the first representative P-wave section waveform and the second representative P-wave section waveform is higher than a threshold value.

[0025] The present disclosure includes a biosignal processing method executed by a biosignal processing device, the biosignal processing method including the steps of acquiring time series data of an ECG signal measured from a user, detecting candidates for P waves from the time series data of the ECG signal, and, when a candidate for P waves is detected, dividing a plurality of heartbeat waveforms extracted from the time series data of the ECG signal into a plurality of heartbeat waveform groups including at least a first heartbeat waveform group and a second heartbeat waveform group, acquiring a first representative P wave section waveform and a second representative P wave section waveform, which are waveforms of representative P wave sections, from each of the first heartbeat waveform group and the second heartbeat waveform group, and determining that a P wave is included in the time series data of the ECG signal if a similarity between the first representative P wave section waveform and the second representative P wave section waveform is higher than a threshold value.

[0026] The present invention may be understood as a biosignal processing device having at least a part of the above configuration, or as a biosignal measuring device or electrocardiogram measuring device having an ECG sensor and a biosignal processing device. Alternatively, the present invention may be understood as an atrial fibrillation detection device, an atrial fibrillation recording device, or an atrial fibrillation monitoring device. The present invention may also be understood as a biosignal processing method including at least a part of the above processing, a device control method, or a program for realizing such a method or a recording medium on which the program is non-temporarily recorded. Note that the above configurations and processing can be combined with each other as much as possible to constitute the present invention. Effect of the Invention

[0027] According to the present invention, it is possible to determine with high reliability the presence of P waves from time-series data of EGG signals by signal processing. [Brief description of the drawings]

[0028] [Figure 1] FIG. 1 shows a biosignal measuring device worn on the upper arm. [Diagram 2] FIG. 2 is a plan view of the biosignal measuring device. [Diagram 3] FIG. 3 is a perspective view of the biosignal measuring device. [Figure 4] FIG. 4 is a block diagram showing the hardware configuration of the biosignal measuring device. [Diagram 5] FIG. 5 is a block diagram showing the functional configuration of the biological signal measuring device. [Figure 6] FIG. 6 is a flowchart showing the flow of the measurement process. [Figure 7] FIG. 7 shows an example of an electrode pair and an ECG signal. [Figure 8] FIG. 8 is a diagram for explaining the waveform of an ECG signal and heart rate information. [Figure 9] FIG. 9 is a block diagram showing the functional configuration of the P wave detection unit. [Figure 10] FIG. 10 is a flowchart showing the flow of the P wave detection process. [Figure 11] FIG. 11 is a diagram for explaining P wave candidate detection. [Figure 12] FIG. 12 is a diagram for explaining the algorithm of the process for determining the presence of a P wave. [Figure 13] FIG. 13 is a diagram for explaining an algorithm for the process of determining the presence of a P wave according to the modified example. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0029] <Application Examples> An application example of the present invention will be described with reference to FIG.

[0030] The biosignal measuring device 1 is a portable measuring device that is attached to a limb and is capable of 24-hour monitoring of ECG signals (electrocardiogram signals). The biosignal measuring device 1 includes an ECG sensor that measures ECG signals, and a biosignal processing device that analyzes time-series data of the ECG signals measured by the ECG sensor and automatically detects atrial fibrillation.

[0031] During measurement, the user wraps the band 10 around the measurement site to wear the biosignal measuring device 1. Examples of measurement sites to which the band 10 is worn include upper limbs (upper arms, forearms, wrists, hands, fingers) and lower limbs (thighs, lower legs, ankles, feet, fingers). It is selected appropriately depending on the algorithm, etc.

[0032] As described above, in order to improve the accuracy and reliability of detection of atrial fibrillation, it is necessary to judge the presence or absence of P waves with high accuracy. Therefore, the biosignal measuring device 1 first detects candidates of P waves from the ECG signal using a simple algorithm, and then performs detailed P wave judgment processing to make a final judgment on the presence or absence of P waves. The P wave judgment processing may include a "P wave presence judgment" that is executed when a P wave candidate is detected, and a "P wave absence judgment" that is executed when a P wave candidate is not detected. In the "P wave presence judgment", two waveforms of the P wave section are obtained from the time series data of the ECG signal for the target period, and if the similarity between the two waveforms is higher than a threshold value, it is judged that "P waves are present", and otherwise it is judged that "the presence or absence of P waves cannot be determined". In the "P wave absence judgment", the smoothness degree of the P wave section is obtained from the time series data of the ECG signal for the target period, and if the smoothness degree is high, it is judged that "P waves are absent", and otherwise it is judged that "the presence or absence of P waves cannot be determined". In this specification, the "P wave section" refers to a time range that includes the time range in which a P wave occurs before an R wave when the heart rate is normal, but does not include other nearby electrocardiographic waveforms (e.g., Q waves and T waves).

[0033] Through this processing, if P waves are included with a high degree of certainty in the ECG signal for the target period, it is determined that "P waves are present," and if P waves are not included with a high degree of certainty in the ECG signal for the target period, it is determined that "P waves are not present." If it is difficult to determine with a high degree of certainty due to the effects of noise, etc., the determination result that "the presence or absence of P waves cannot be determined" is output.

[0034] For example, by excluding ECG signals labeled as "P wave presence or absence cannot be determined" and using only ECG signals labeled as "P wave present" or "P wave absent" for detecting atrial fibrillation, it is expected that the accuracy and reliability of atrial fibrillation detection can be improved compared to conventional methods.

[0035] In the following, a specific configuration example in which the present invention is applied to an upper arm electrocardiogram device will be described as one embodiment of the present invention.

[0036] <Embodiment> (Device configuration) An embodiment of the present invention will be described with reference to Fig. 1 to Fig. 4. Fig. 1 is a diagram showing a state in which a biosignal measuring device 1 is worn on the upper arm, Fig. 2 is a plan view of the biosignal measuring device 1, Fig. 3 is a perspective view of the biosignal measuring device 1, and Fig. 4 is a block diagram showing the hardware configuration of the biosignal measuring device 1.

[0037] The biosignal measuring device 1 of this embodiment is an upper arm electrocardiogram device that is worn on a user's upper arm (preferably the left upper arm close to the heart) and used to measure an ECG (Electrocardiogram) signal (electrocardiogram signal) as a biosignal.

[0038] The biosignal measuring device 1 mainly comprises a band 10, a plurality of electrodes 11 fixed to the band 10, and a control unit 12 fixed to the band 10.

[0039] The band 10 is a member (fixing member) for fixing the electrode 11 in a state where it is pressed against the living body. In this embodiment, a strip-shaped band 10 made of a flexible and malleable material (e.g., chemical fiber, silicone, leather, etc.) is used. A fixing mechanism 13 is provided at the longitudinal end of the band 10. As shown in Figs. 1 and 3, the band 10 is made into a loop and fastened with the fixing mechanism 13, so that the biosignal measuring device 1 can be worn on the upper arm. The fixing mechanism 13 may be any type of fastener, hook, connector, button, magnet, etc.

[0040] A plurality of electrodes 11 (also called an electrode array) are embedded and fixed in the band 10 such that the surface that comes into contact with the living body is exposed on the inside (living body side) of the band 10. The plurality of electrodes 11 are arranged in a row at equal intervals in the longitudinal direction of the band 10. In this way, when the band 10 is wrapped around the arm, the electrodes 11 come into contact with different positions around the arm. The number of electrodes 11 can be designed as desired. For the purpose of measuring an ECG signal, at least two electrodes 11 (one electrode pair) are sufficient, and three or more electrodes 11 may be provided to improve the reliability and robustness of the measurement. In this embodiment, a configuration in which six electrodes 11 (three electrode pairs) are provided is adopted.

[0041] The electrode 11 is a dry metal electrode. Wet electrodes (such as gel electrodes) have problems such as the possibility of causing skin rashes and itching when worn for a long time, and low durability and ease of maintenance, whereas the dry electrode 11 does not have such problems. The biosignal measuring device 1 of this embodiment is intended to be worn for a long time and to monitor ECG signals for 24 hours, so the dry electrode 11 is preferable.

[0042] The control body 12 is a processing unit that controls the biosignal measuring device 1 and performs signal processing. As shown in FIG. 4, the control body 12 has a structure in which a measurement circuit 120 for measuring an ECG signal, a biosignal processing device 121 that performs various signal processing on the ECG signal, a battery 122, and other circuits are mounted inside a case made of, for example, resin or metal. The measurement circuit 120 is an analog circuit composed of a differential amplifier, etc., and the ECG sensor 14 is composed of a plurality of electrodes 11 and the measurement circuit 120. The biosignal processing device 121 is composed of, for example, an AD converter, a filter, a processor, a memory, etc. The battery 122 is a rechargeable secondary battery. The control body 12 may be provided with an alarm device that performs alarms using a physical switch, a display, sound, light, vibration, electrical stimulation, etc.

[0043] (Control unit) FIG. 5 is a block diagram showing an example of the functional configuration of the control unit 12. As shown in FIG.

[0044] The control body 12 includes a measurement electrode selection unit 30, an ECG measurement unit 20, an ECG signal processing unit 21, an ECG heart rate information calculation unit 22, an AF determination unit 23, a storage unit 24, and a communication unit 25 as components related to the measurement of an ECG signal. The measurement electrode selection unit 30 selects an electrode pair to be used for measuring an ECG signal. The ECG measurement unit 20 amplifies the potential difference between the selected electrode pair using a differential amplifier and outputs it as an ECG signal. The ECG signal processing unit 21 performs AD conversion and filtering of the ECG signal, and includes an AD conversion unit 210 that performs AD conversion of the ECG signal, an electromagnetic noise removal unit 211 that removes electromagnetic noise from the ECG signal to increase the S / N ratio, and a baseline drift removal unit 212 that removes baseline drift (low-frequency drift) of the ECG signal. The ECG heartbeat information calculation unit 22 is a unit that extracts various heartbeat information from the ECG signal, and includes an R wave detection unit 220, an RRI calculation unit 221, a heartbeat variability calculation unit 222, and a P wave detection unit 223. The AF determination unit 23 is a unit that detects the occurrence of AF (Atrial Fibrillation) based on the heartbeat information and calculates an index related to AF. The storage unit 24 is a non-volatile memory that stores measured or calculated data. The communication unit 25 is a unit that performs wireless data communication with external devices (for example, the user's smartphone, other health devices, a home server, etc.). Among the functional units shown in FIG. 5, the ECG measurement unit 20 is a function provided by the measurement circuit 120 in FIG. 4, and the other functional units are functions provided by the biological signal processing device 121 in FIG. 4.

[0045] (ECG signal measurement and processing) 6 to 8, the measurement process of an ECG signal by the biosignal measurement device 1 will be described. FIG. 6 is a flow chart showing the flow of the measurement process of an ECG signal, and FIG. FIG. 8 is a diagram illustrating an example of an ECG signal and heart rate information.

[0046] When the user wraps the band 10 around the upper arm and fastens the band 10 with the fastening mechanism 13, and then performs an operation to instruct the start of measurement, the processor of the control unit 12 starts the measurement process of FIG.

[0047] In step S500, three-channel ECG signals are measured using three electrode pairs by the ECG measurement unit 20. Specifically, an electrode pair (two electrodes 11) to be used for measurement is selected by the measurement electrode selection unit 30, and the potential difference between the selected electrode pair is amplified by a differential amplifier and captured as an ECG signal (analog voltage signal). By switching the selected electrode pair in sequence, three-channel ECG signals are captured.

[0048] In this embodiment, as shown in FIG. 7, three electrode pairs are set so that two electrodes 11 that are exactly opposite each other when the band 10 is wrapped around the upper arm are paired. This is because the greater the distance between the two paired electrodes 11, the greater the potential difference (i.e., an ECG signal with a high S / N ratio) that can be measured. Note that the distance between the two electrodes 11 is, for example, the linear distance between the two electrodes 11 when measured in a state in which the band 10 is wrapped around the upper arm as shown in FIG. 3. However, the method of setting the electrode pairs is not limited to this. For example, a multiplexer may be used to freely switch the combination of the paired electrodes 11. In this case, it is also possible to measure ECG signals of four or more channels from six electrodes 11.

[0049] In step S501, the ECG signal processor 21 converts the ECG signal into an analog signal and removes electromagnetic noise and baseline fluctuations by digital signal processing. For removing the electromagnetic noise and baseline fluctuations, known noise reduction methods such as a band pass filter, a notch filter, and a moving average can be used. The ECG signal (digital data) obtained by the ECG signal processor 21 is stored in the memory unit 24 together with information on the measurement time.

[0050] In step S502, the ECG heart rate information calculation unit 22 retrieves the time series data of the ECG signal for a unit period from the storage unit 24, and calculates various pieces of heart rate information by analyzing the time series data of the ECG signal. The length of the unit period is arbitrary and is set to, for example, a length of several tens of seconds to several tens of minutes.

[0051] As shown in FIG. 8, the waveform of one heartbeat of an ECG signal mainly consists of a P wave, a QRS wave, and a T wave. The QRS wave refers to a waveform that combines an R wave, which is an upward peak, and Q and S waves, which appear before and after the R wave, which are downward. In a normal heart, an electrical impulse generated in the sinus node is transmitted from the atrium to the ventricle, and the atrial excitation (contraction) and the ventricular excitation (contraction) occur in sequence, thereby pumping out blood. The P wave is a waveform that corresponds to the excitation of the atrium, the QRS wave is a waveform that corresponds to the excitation of the ventricle, and the T wave is a waveform that corresponds to the process in which the ventricle recovers from the excitation. The ECG signal contains multiple heartbeat waveforms in the time axis direction. For an average adult, the heart rate is about 60 to 80 bpm, so for example, the time series data of an ECG signal for one minute contains about 60 to 80 heartbeat waveforms.

[0052] Examples of heart rate information include R amplitude (height of the R wave from the baseline), QRS amplitude (defined as the sum of the height of the R wave from the baseline and the depth of the S wave from the baseline), P amplitude (height of the P wave from the baseline), T amplitude (height of the T wave from the baseline), P width (time from the start of the P wave to the end of the P wave), QRS width (time from the start of the Q wave to the end of the S wave), T width (time from the start of the T wave to the end of the T wave), PQ time (time from the start of the P wave to the start of the Q wave), QT time (time from the start of the Q wave to the end of the T wave), RRI (RR interval; time from the peak of the R wave to the peak of the next R wave), PPI (PP interval; time from the start of the P wave to the end of the T wave) These heart rate information include heart rate fluctuations (time from the start of the P wave to the beginning of the next P wave), and heart rate variability (time fluctuations of RRI and PPI). It is not necessary to calculate all of these pieces of heart rate information, and it is sufficient to calculate only the necessary heart rate information. Heart rate information other than these may also be calculated. The calculated heart rate information is stored in the memory unit 24 together with information on the measurement time.

[0053] In step S503, the AF determination unit 23 detects the occurrence of AF (atrial fibrillation) based on the heart rate information and calculates an index related to AF. The AF detection result and the calculated index are stored in the storage unit 24 together with information on the measurement time.

[0054] The measurement process in FIG. 6 described above is repeatedly executed for each unit period, thereby performing 24-hour monitoring of the ECG signal and AF.

[0055] (Details of the P wave detector) The configuration of the P-wave detection unit 223 and the P-wave detection algorithm will be described in detail with reference to FIGS.

[0056] 9 is a functional block diagram of the P wave detection unit 223. The P wave detection unit 223 has, as its main functions, an acquisition unit 80, a candidate detection unit 81, and a P wave determination unit 82. The acquisition unit 80 has a function of acquiring time series data of the ECG signal from the storage unit 24, the candidate detection unit 81 has a function of detecting P wave candidates from the time series data of the ECG signal, and the P wave determination unit 82 has a function of making a detailed determination of the presence or absence of a P wave based on the detection result of the candidate detection unit 81, and outputting a final determination result.

[0057] FIG. 10 is a flowchart showing the flow of the P-wave detection process of the P-wave detector 223.

[0058] In step S900, the acquisition unit 80 acquires time series data of the ECG signal from the storage unit 24. Since P waves are easily contaminated by noise as described above, it is preferable to statistically process time series data of a certain length of time in order to ensure the accuracy and reliability of P wave detection. In this embodiment, 10 minutes of time series data is used for P wave detection. This period that is the target of P wave detection is referred to as the "target period" in this specification. The length of the target period can be set arbitrarily, for example, to a length of about several tens of seconds to several tens of minutes.

[0059] In step S901, the candidate detection unit 81 calculates the noise degree (noise level) of the time series data of the ECG signal for the target period. For example, the RMS (Root Mean Square) of the amplitude may be used as the noise degree. In step S902, the candidate detection unit 81 removes data from high noise intervals (intervals where the noise degree is equal to or higher than a predetermined level) from the time series data of the ECG signal for the target period, and selects only data from low noise intervals (intervals where the noise degree is lower than a predetermined level). By using only data from low noise intervals, i.e., intervals where the heartbeat waveform appears relatively cleanly, for subsequent processing, the reliability of candidate detection can be improved.

[0060] In step S903, the candidate detection unit 81 detects R waves from the data in the low noise section. In step S904, the candidate detection unit 81 checks whether or not a predetermined number of R waves (i.e., heartbeat waveforms) or more have been detected from the data in the low noise section. The "predetermined number" may be set arbitrarily, for example, to a value of about 20 to 100 (set to 25 in this embodiment). If the number of R waves (i.e., the number of heartbeat waveforms) extracted from the data in the low noise section does not reach the predetermined number (No in step S904), the P wave determination unit 82 outputs a result indicating that it is impossible to determine whether or not the ECG signal in the target period contains P waves (step S911). By classifying an ECG signal that is noisy to the extent that it is difficult to detect R waves (heartbeat waveforms) as impossible to determine, it is possible to prevent an erroneous determination result from being output in the subsequent P wave determination. This leads to improved reliability of the device as a whole.

[0061] If a predetermined number or more of R waves can be detected from the data of the low noise section (Yes in step S904), the process proceeds to step S905. In step S905, the candidate detection unit 81 extracts waveforms in a predetermined range before and after the peak position of each detected R wave as heartbeat waveforms, and generates a representative heartbeat waveform, which is a representative heartbeat waveform, from the extracted multiple heartbeat waveforms. In this specification, a "representative waveform" refers to a waveform that represents multiple waveforms as one waveform from the viewpoint of shape. It may be a waveform that has commonality from the viewpoint of waveform shape, may be a waveform that has commonality when focusing on a specific feature, may be an average shape of multiple waveforms, or may be a waveform obtained from waveforms excluding exceptional waveforms that are significantly different in shape. For example, it may be an arithmetic average of multiple waveforms, may be a waveform that is a median or mode when multiple waveforms are evaluated by a specific index, or may be a waveform generated from multiple waveforms using other statistical methods. By generating a "representative waveform", a waveform with further reduced noise can be estimated. As a result, the reliability of P wave determination can be improved. Similarly, accurate heart rate information can be obtained, improving the accuracy and reliability of atrial fibrillation detection.

[0062] In step S906, the candidate detection unit 81 detects candidates for P waves from the representative heartbeat waveform. Fig. 11 shows an example of P wave candidate detection. Specifically, the candidate detection unit 81 first sets a P wave section in the representative heartbeat waveform based on the peak position of the R wave in the representative heartbeat waveform. In this embodiment, as an example, the definition proposed in Non-Patent Document 1 is adopted, and the start point and end point of the P wave section are set as shown in the following formula.

[0063] Start point = R-(1-0.71) x RRmedian-40 [msec] End point = R-0.07 × RRmedian-20 [msec] R: Peak position of the R wave of the representative heart rate waveform RRmedian: Median RRI during the target period However, the method is not limited to the above formula, and the start and end points of the P wave section may be determined by other methods.

[0064] Next, the candidate detection unit 81 extracts a feature amount related to the amplitude from the waveform in the P-wave section. For example, as shown in Fig. 11, the candidate detection unit 81 selects the maximum maximal point or the minimum maximal point in the P-wave section as a candidate point cP of the P-wave peak, and calculates the feature amount of the candidate point cP by the following formula.

number

[0065] Here, amp1 is the amplitude (peak value) of the candidate point cP relative to the rising point onset of the peak of the candidate point cP, and amp2 is the amplitude (peak value) of the candidate point cP relative to the falling point offset of the peak of the candidate point cP (see FIG. 11). The rising point onset and the falling point offset can be detected by finding the inflection points of the waveform. Also, QRSamp is the amplitude from the minimum point to the maximum point of the QRS wave.

[0066] The above feature value uses the average of the rising amplitude amp1 and the falling amplitude amp2. This allows the amplitude of the P wave to be evaluated more accurately. In addition, by dividing the amplitude of the P wave by the QRS amplitude, normalization is performed based on the QRS amplitude, so that the magnitude of the amplitude of the P wave can be appropriately evaluated regardless of the strength of the signal level of the ECG signal.

[0067] If the feature amount thus obtained satisfies a predetermined condition, the candidate detection unit 81 detects the candidate point c The peak of P is adopted as a "P wave candidate", and a result of "P wave candidate present (P wave candidate detected)" is output. On the other hand, if the feature does not satisfy the predetermined condition, a result of "P wave candidate not present (P wave candidate not detected)" is output. Based on the knowledge that the amplitude of the P wave is generally about 1% to 5% of the QRS amplitude, in this embodiment, the judgment condition is set to "≧0.01". That is, when the value of the feature is 0.01 or more (in other words, when the amplitude of the P wave section is 1% or more of the QRS amplitude), it is judged that "P wave candidate present".

[0068] Since the detection algorithm of the candidate detection unit 81 is merely a simple detection of convex or concave peaks, there is a possibility that noise parts may be erroneously detected as P-wave candidates, or conversely, P waves buried in noise may be overlooked and erroneously determined as "no P-wave candidates". Therefore, a detailed determination is performed by the P-wave determination unit 82 to make a final determination as to whether or not a P wave exists. The P-wave determination unit 82 of this embodiment switches the detailed determination process according to the detection result of the candidate detection unit 81. That is, if a P-wave candidate is detected (Yes in step S907), a "P-wave presence determination" (steps S908 to S909) is performed, and if a P-wave candidate is not detected (No in step S907), a "P-wave absence determination" (steps S912 to S913) is performed. Specific examples of the detailed determination process will be described below.

[0069] (1) P wave presence determination The inventors have found, from the results of analyzing ECG signals obtained in subject experiments, that although there are individual differences in the waveforms of P waves, in the ECG signals of the same subject, the PR interval (the interval between P waves and R waves) is kept almost constant, and the waveforms of the P waves are reproducible (similar). Based on this finding, the inventors have come up with the idea that when a P wave section is set based on the time point (e.g., the peak position) of the R wave and the waveforms of the P wave section in two heartbeat waveforms are compared, if a P wave appears, the similarity between the two waveforms will be high, but if it is not a P wave but a noise undulation, the similarity between the two waveforms should be low. The P wave presence determination algorithm described below is an embodiment of this idea.

[0070] FIG. 12 shows an example of an algorithm for the P wave presence determination process in step S908. First, the P wave determination unit 82 extracts multiple heartbeat waveforms from the time series data of the ECG signal for the target period. In this case, similarly to steps S901 to S905, the heartbeat waveform may be extracted from the data in the low noise section. Alternatively, the data of the heartbeat waveform extracted in step S903 may be stored in the storage unit 24, and the P wave determination unit 82 may use the data to determine the presence of P waves. Next, the P wave determination unit 82 divides the extracted multiple heartbeat waveforms into a first heartbeat waveform group and a second heartbeat waveform group. Here, for example, when N heartbeat waveforms are extracted from the target period, the N / 2 heartbeat waveforms extracted in the first half are assigned to the first heartbeat waveform group, and the N / 2 heartbeat waveforms extracted in the latter half are assigned to the second heartbeat waveform group. By allocating the same number of heart rate waveforms to the first heart rate waveform group and the second heart rate waveform group, the conditions for the subsequent statistical processing can be made uniform.

[0071] Next, the P wave determination unit 82 acquires a representative P wave section waveform (referred to as a "first representative P wave section waveform") from the first heartbeat waveform group. For example, the P wave determination unit 82 may extract waveforms of P wave sections set based on the time point of the R wave from each heartbeat waveform of the first heartbeat waveform group, and may average them to obtain the first representative P wave section waveform. In this case, similar to the generation of the representative heartbeat waveform in step S905, the waveforms of multiple P wave sections may be averaged, or a median or mode may be obtained from the waveforms of multiple P wave sections. In addition, the P wave determination unit 82 acquires a representative P wave section waveform (referred to as a "second representative P wave section waveform") from the second heartbeat waveform group. Then, the P wave determination unit 82 calculates a cross-correlation coefficient r between the first representative P wave section waveform and the second representative P wave section waveform. This cross-correlation coefficient r is an example of an index that indicates the similarity between the first representative P wave section waveform and the second representative P wave section waveform. In this embodiment, the first representative P wave section waveform Although cross-correlation was used to evaluate the similarity between the first and second representative P-wave section waveforms, other methods or indices may be used to evaluate the similarity. For example, the similarity may be evaluated by comparing feature amounts (P amplitude, P width, relative positions of the onset, peak position, and offset, area of ​​the P wave, etc.) extracted from each representative P-wave section waveform.

[0072] In step S909, the P wave determination unit 82 compares the cross-correlation coefficient r with a predetermined threshold th1. If the cross-correlation coefficient r is equal to or greater than the threshold th1 (Yes in step S909), the P wave determination unit 82 determines that the first representative P wave section waveform and the second representative P wave section waveform are similar (reproducible), and outputs a result that the ECG signal for the target period contains a P wave (step S910). On the other hand, if the cross-correlation coefficient r is less than the threshold th1 (No in step S909), the P wave determination unit 82 determines that the first representative P wave section waveform and the second representative P wave section waveform are not similar, and that the P wave candidate is highly likely to be noise, and outputs a result that it is impossible to determine whether or not the ECG signal for the target period contains a P wave (step S911). The threshold th1 may be set arbitrarily, and may be set to a value that can appropriately separate P waves from noise based on the results of a subject experiment (the threshold th1 is set to 0.7 in this embodiment).

[0073] (2) Absence of P waves Even if the candidate detection unit 81 does not detect a P wave candidate, it is possible that the P wave was overlooked due to being buried in noise. Therefore, the P wave determination unit 82 evaluates the degree of smoothness of the P wave section of the heartbeat waveform to determine whether or not the detection result of the candidate detection unit 81 should be adopted. If the degree of smoothness is high, the noise level of the ECG signal can be considered to be low (the S / N ratio is high), and therefore the reliability of the detection result of the candidate detection unit 81 can be determined to be high. Conversely, if the degree of smoothness of the P wave section is low, the noise level of the ECG signal can be considered to be high (the S / N ratio is low), and therefore the reliability of the detection result of the candidate detection unit 81 can be determined to be low.

[0074] In this embodiment, the smoothness degree s of the P wave section is expressed as an index according to the following formula. Smoothness degree s = (QRS amplitude) ÷ (RMS value of the numerical derivative of the waveform within the P wave section)

[0075] The denominator of the above index (the RMS value of the numerical derivative of the waveform within the P wave section) represents the magnitude of change (unevenness) in the waveform during the P wave section. Therefore, the smaller (flatter) the change in the waveform during the P wave section, the smaller the degree of smoothness. In addition, since the above index is normalized based on the QRS amplitude, the degree of smoothness of the P wave section can be appropriately evaluated regardless of the strength of the signal level of the ECG signal.

[0076] Specifically, the P wave determination unit 82 calculates the smoothness degree s of the P wave section using the representative heartbeat waveform generated in step S905 (step S912). Then, the P wave determination unit 82 compares the smoothness degree s with a predetermined threshold th2. If the smoothness degree s is equal to or greater than the threshold th2 (Yes in step S913), the P wave determination unit 82 determines that the reliability of the detection result of the candidate detection unit 81 is high, and outputs a result that the ECG signal in the target period does not include a P wave (step S914). On the other hand, if the smoothness degree s is less than the threshold th2 (No in step S913), the P wave determination unit 82 determines that the reliability of the detection result of the candidate detection unit 81 is low, and outputs a result that it is impossible to determine whether or not the ECG signal in the target period includes a P wave (step S911). The threshold th2 may be set arbitrarily, and may be set to a value that can appropriately separate noise based on the results obtained from subject experiments (the threshold th2 is set to about 800 in this embodiment).

[0077] According to the P wave detection algorithm of the present embodiment described above, the presence or absence of P waves can be determined with high reliability from the time series data of the ECG signal. Therefore, it becomes possible to automatically and accurately detect diseases such as atrial fibrillation.

[0078] <Modification> In the above embodiment, as shown in FIG. 12, the target period is divided into two, and the first heartbeat waveform group is extracted from the first half and the second heartbeat waveform group is extracted from the second half. However, the method of dividing the first heartbeat waveform group and the second heartbeat waveform group is not limited to this method. For example, as shown in FIG. 13, a plurality of heartbeat waveforms extracted from the time series data of the ECG signal may be divided alternately into the first heartbeat waveform group and the second heartbeat waveform group (i.e., odd-numbered heartbeat waveforms are divided into the first heartbeat waveform group and even-numbered heartbeat waveforms are divided into the second heartbeat waveform group). Alternatively, instead of dividing the heartbeat waveforms alternately and regularly, the heartbeat waveforms may be divided randomly into the first heartbeat waveform group and the second heartbeat waveform group.

[0079] When time division is performed as shown in Figure 12, the extraction period for the first heart rate waveform group and the extraction period for the second heart rate waveform group do not overlap, whereas when they are divided alternately or randomly, both the first heart rate waveform group and the second heart rate waveform group are extracted from the entire target period.

[0080] For example, if the P-wave waveform changes over time, such as the direction (polarity) of the P-wave being inverted between the first and second halves of the target period, the cross-correlation coefficient between the first and second representative P-wave section waveforms may be small in the method of Fig. 12, and the P-wave may be determined to be "undetectable" even though it exists. In contrast, if the waveforms are alternately or randomly distributed, the first and second heartbeat waveform groups are both extracted from the entire target period, so that the change in the P-wave waveform over time affects both the first and second representative P-wave section waveforms in the same way, and the decrease in the cross-correlation coefficient can be suppressed. In other words, the P-wave presence determination according to the modified example has the advantage of being robust to the change in the P-wave waveform over time.

[0081] <Other> The above embodiment merely exemplifies the configuration of the present invention. The present invention is not limited to the above specific embodiment, and various modifications are possible within the scope of the technical concept. For example, as the fixing member for fixing the electrode 11 in a state where it is pressed against the living body, as in the above embodiment, a band-shaped (strip-shaped) member that wraps around the measurement part of the living body while the electrode 11 is in contact with the living body, as well as a wrap-shaped member or a ring-shaped member that covers and encases the measurement part of the living body may be used. In addition, the fixing member may have elasticity and deformability in order to correspond to the size (diameter) of the measurement part of the living body. Alternatively, if it is made of a non-elastic material, it may have a structure that allows the length to be adjusted. The number of electrodes 11 may be one or more. In the case of multiple electrodes 11, the arrangement does not need to be in a single row, and they may be arranged in a two-dimensional array. In addition, the arrangement intervals of the electrodes 11 may be biased rather than being equally spaced. The electrodes 11 may be integrated with the fixing member (band 10) or may have a structure separate from the fixing member. The control body 12 may not be fixed to the band 10. For example, the control body 12 and the band 10 may be separate structures, and the control body 12 and the band 10 (electrodes 11) may be connected by a cable.

[0082] In the above embodiment, the presence of P waves is judged by generating a representative P wave section waveform based on a group of waveforms of a plurality of P wave sections extracted from a plurality of heartbeat waveforms, and evaluating the similarity between the two representative P wave section waveforms. However, it is not essential to use a representative P wave section waveform in the presence of P waves judgment, and a P wave section waveform extracted from a single heartbeat waveform may be used for the evaluation of the similarity. In that case, it is sufficient to evaluate whether the similarity between the waveform of a first P wave section set based on the time point of a first R wave in the time series data of the ECG signal and the waveform of a second P wave section set based on the time point of a second R wave different from the first R wave is higher than a threshold value. In the above embodiment, the plurality of heartbeat waveforms extracted from the time series data of the ECG signal are divided into two groups, a first heartbeat waveform group and a second heartbeat waveform group, but they may be divided into three or more groups and compared with each other, for example. Alternatively, a plurality of heart rate waveforms may be divided into three groups, such as a first heart rate waveform group, a second heart rate waveform group, and a heart rate waveform group to be excluded from the similarity evaluation.

[0083] This specification includes the following disclosure.

[0084] [Appendix 1] An acquisition unit (80) for acquiring time series data of an ECG signal measured from a user; and a P wave determination unit (82) that extracts a first P wave section waveform from a first P wave section set based on a time point of a first R wave in the time series data of the ECG signal, and extracts a second P wave section waveform from a second P wave section set based on a time point of a second R wave different from the first R wave, and determines that a P wave is included in the first P wave section and the second P wave section when a similarity between the first P wave section waveform and the second P wave section waveform is higher than a threshold value. Biosignal processing device (121).

[0085] [Appendix 2] An acquisition unit (80) for acquiring time series data of an ECG signal measured from a user; and a P wave determination unit (82) that extracts at least a first heartbeat waveform group and a second heartbeat waveform group from the time series data of the ECG signal, extracts a waveform of a P wave section set based on a time point of an R wave from each heartbeat waveform of the first heartbeat waveform group, and extracts a waveform of a P wave section set based on a time point of an R wave from each heartbeat waveform of the second heartbeat waveform group, generates a first representative P wave section waveform based on the waveform group of a plurality of P wave sections extracted from the first heartbeat waveform group, and generates a second representative P wave section waveform based on the waveform group of a plurality of P wave sections extracted from the second heartbeat waveform group, and determines that a P wave is included in the first heartbeat waveform group and the second heartbeat waveform group when a similarity between the first representative P wave section waveform and the second representative P wave section waveform is higher than a threshold value. Biosignal processing device (121).

[0086] [Appendix 3] An acquisition unit (80) for acquiring time series data of an ECG signal measured from a user; a candidate detection unit (81) for detecting candidates of P waves from the time series data of the ECG signal; and a P wave determination unit (82) which, when a P wave candidate is detected by the candidate detection unit (81), divides a plurality of heartbeat waveforms extracted from the time series data of the ECG signal into a plurality of heartbeat waveform groups including at least a first heartbeat waveform group and a second heartbeat waveform group, obtains a first representative P wave section waveform and a second representative P wave section waveform which are waveforms of representative P wave sections from the first heartbeat waveform group and the second heartbeat waveform group, respectively, and determines that a P wave is included in the time series data of the ECG signal when a similarity between the first representative P wave section waveform and the second representative P wave section waveform is higher than a threshold value. Biosignal processing device (121).

[0087] [Appendix 4] The P wave determination unit determines that it is impossible to determine whether or not a P wave is included in the time series data of the ECG signal when the similarity between the first representative P wave section waveform and the second representative P wave section waveform is equal to or less than the threshold value. 4. The biological signal processing device according to claim 3.

[0088] [Appendix 5] the P wave determination unit assigns, among the multiple heartbeat waveforms extracted from the time series data of the ECG signal, those extracted in the first half to the first heartbeat waveform group, and those extracted in the latter half to the second heartbeat waveform group; 5. The biological signal processing device according to claim 3 or 4.

[0089] [Appendix 6] The P wave determination unit alternately or randomly selects one to be assigned to the first heartbeat waveform group and one to be assigned to the second heartbeat waveform group from among a plurality of heartbeat waveforms extracted from the time series data of the ECG signal. 5. The biological signal processing device according to claim 3 or 4.

[0090] [Appendix 7] the P wave determination unit divides the multiple heartbeat waveforms extracted from the time series data of the ECG signal into the first heartbeat waveform group and the second heartbeat waveform group such that the number of heartbeat waveforms in the first heartbeat waveform group is equal to the number of heartbeat waveforms in the second heartbeat waveform group; The biological signal processing device according to any one of Supplementary Note 3 to Supplementary Note 6.

[0091] [Appendix 8] The P wave determination unit A waveform of a P wave section set based on a time point of an R wave is extracted from each heartbeat waveform of the first heartbeat waveform group, and an average, median, or mode of the waveforms of the multiple P wave sections extracted from the first heartbeat waveform group is obtained as the first representative P wave section waveform; A waveform of a P wave section set based on a time point of an R wave is extracted from each heartbeat waveform of the second heartbeat waveform group, and an average, median, or mode of the waveforms of the multiple P wave sections extracted from the second heartbeat waveform group is obtained as the second representative P wave section waveform. The biological signal processing device according to any one of Supplementary Note 3 to Supplementary Note 7.

[0092] [Appendix 9] The P wave determination unit evaluates the similarity between the first representative P wave section waveform and the second representative P wave section waveform based on a cross-correlation between the first representative P wave section waveform and the second representative P wave section waveform. The biological signal processing device according to any one of Supplementary claims 3 to 8.

[0093] [Appendix 10] the candidate detection unit generates a representative heartbeat waveform, which is a representative heartbeat waveform, from a plurality of heartbeat waveforms extracted from the time series data of the ECG signal, and detects candidates of P waves from the representative heartbeat waveform; The biological signal processing device according to any one of Supplementary claims 3 to 9.

[0094] [Appendix 11] The candidate detection unit extracts a feature from a P wave section set based on a time point of an R wave in the representative heartbeat waveform, and determines that there is a P wave candidate when the feature satisfies a predetermined condition. 11. The biological signal processing device according to claim 10.

[0095] [Appendix 12] the candidate detection unit selects data in a low-noise section in which a noise level is lower than a predetermined level from the time-series data of the ECG signal, and generates the representative heartbeat waveform using a plurality of heartbeat waveforms extracted from the data in the low-noise section. 12. The biological signal processing device according to claim 10 or 11.

[0096] [Appendix 13] When the number of heartbeat waveforms extracted from the data in the low noise section does not reach a predetermined number, the P wave determination unit determines that it is impossible to determine whether or not a P wave is included in the time series data of the ECG signal. 13. The biological signal processing device according to claim 12.

[0097] [Appendix 14] When the candidate detection unit does not detect a P wave candidate, the P wave determination unit evaluates the degree of smoothness of a P wave section set based on the time point of an R wave in the representative heartbeat waveform, and when the degree of smoothness is equal to or greater than a predetermined value, determines that a P wave is not included in the time series data of the ECG signal, and otherwise determines that it is impossible to determine whether or not a P wave is included in the time series data of the ECG signal. The biological signal processing device according to any one of Supplementary claims 10 to 13.

[0098] [Appendix 15] an ECG sensor (14) attached to a user's limb; A biological signal processing device (12) according to any one of Supplementary Note 1 to Supplementary Note 14, which processes an ECG signal measured by the ECG sensor (14); A biosignal measuring device (1).

[0099] [Appendix 16] A biosignal processing method executed by a biosignal processing device (121), obtaining time series data of an ECG signal measured from a user; extracting a first P-wave section waveform from a first P-wave section set based on a time point of a first R wave in the time series data of the ECG signal, and extracting a second P-wave section waveform from a second P-wave section set based on a time point of a second R wave different from the first R wave, and determining that P waves are included in the first P-wave section and the second P-wave section when a similarity between the first P-wave section waveform and the second P-wave section waveform is higher than a threshold.

[0100] [Appendix 17] A biosignal processing method executed by a biosignal processing device (121), obtaining time series data of an ECG signal measured from a user; extracting at least a first heartbeat waveform group and a second heartbeat waveform group from the time series data of the ECG signal, extracting a waveform of a P-wave section set based on a time point of an R-wave from each heartbeat waveform of the first heartbeat waveform group, and extracting a waveform of a P-wave section set based on a time point of an R-wave from each heartbeat waveform of the second heartbeat waveform group, generating a first representative P-wave section waveform based on the waveform group of a plurality of P-wave sections extracted from the first heartbeat waveform group, and generating a second representative P-wave section waveform based on the waveform group of a plurality of P-wave sections extracted from the second heartbeat waveform group, and judging that a P-wave is included in the first heartbeat waveform group and the second heartbeat waveform group when a similarity between the first representative P-wave section waveform and the second representative P-wave section waveform is higher than a threshold value. A method for processing biological signals.

[0101] [Appendix 18] A biosignal processing method executed by a biosignal processing device (12), comprising: A step of acquiring time series data of an ECG signal measured from a user (S900); A step (S906) of detecting P wave candidates from the time series data of the ECG signal; When a P wave candidate is detected, a plurality of heartbeat waveforms extracted from the time series data of the ECG signal are divided into a plurality of heartbeat waveform groups including at least a first heartbeat waveform group and a second heartbeat waveform group, a first representative P wave section waveform and a second representative P wave section waveform, which are waveforms of representative P wave sections, are obtained from the first heartbeat waveform group and the second heartbeat waveform group, respectively, and when a similarity between the first representative P wave section waveform and the second representative P wave section waveform is higher than a threshold value, the ECG signal is and determining (S908 to S911) that the time series data includes a P wave. A method for processing biological signals. [Explanation of symbols]

[0102] 1: Biosignal measuring device 10: Band 11: Electrode 12: Control unit 14:ECG sensor 121: Biological signal processing device

Claims

1. An acquisition unit that acquires time series data of an ECG signal measured from a user; a P-wave determination unit that extracts a first P-wave section waveform from a first P-wave section set based on a time point of a first R-wave in the time series data of the ECG signal, and extracts a second P-wave section waveform from a second P-wave section set based on a time point of a second R-wave different from the first R-wave, and determines that a P-wave is included in the first P-wave section and the second P-wave section when a similarity between the first P-wave section waveform and the second P-wave section waveform is higher than a threshold value. Biosignal processing device.

2. An acquisition unit that acquires time series data of an ECG signal measured from a user; and a P-wave determination unit that extracts at least a first heartbeat waveform group and a second heartbeat waveform group from the time series data of the ECG signal, extracts a waveform of a P-wave section set based on a time point of an R-wave from each heartbeat waveform of the first heartbeat waveform group, and extracts a waveform of a P-wave section set based on a time point of an R-wave from each heartbeat waveform of the second heartbeat waveform group, generates a first representative P-wave section waveform based on the waveform group of a plurality of P-wave sections extracted from the first heartbeat waveform group, and generates a second representative P-wave section waveform based on the waveform group of a plurality of P-wave sections extracted from the second heartbeat waveform group, and determines that a P-wave is included in the first heartbeat waveform group and the second heartbeat waveform group when a similarity between the first representative P-wave section waveform and the second representative P-wave section waveform is higher than a threshold value. Biosignal processing device.

3. An acquisition unit that acquires time series data of an ECG signal measured from a user; a candidate detection unit for detecting P wave candidates from the time series data of the ECG signal; a P-wave determination unit that, when a P-wave candidate is detected by the candidate detection unit, divides a plurality of heartbeat waveforms extracted from the time series data of the ECG signal into a plurality of heartbeat waveform groups including at least a first heartbeat waveform group and a second heartbeat waveform group, obtains a first representative P-wave section waveform and a second representative P-wave section waveform which are waveforms of representative P-wave sections from the first heartbeat waveform group and the second heartbeat waveform group, respectively, and determines that a P-wave is included in the time series data of the ECG signal when a similarity between the first representative P-wave section waveform and the second representative P-wave section waveform is higher than a threshold value. Biosignal processing device.

4. The P wave determination unit determines that it is impossible to determine whether or not a P wave is included in the time series data of the ECG signal when a similarity between the first representative P wave section waveform and the second representative P wave section waveform is equal to or less than the threshold value. The biological signal processing device according to claim 3 .

5. the P wave determination unit assigns, among the multiple heartbeat waveforms extracted from the time series data of the ECG signal, those extracted in the first half to the first heartbeat waveform group, and those extracted in the latter half to the second heartbeat waveform group; The biological signal processing device according to claim 3 .

6. the P wave determination unit alternately or randomly selects, from among a plurality of heartbeat waveforms extracted from the time series data of the ECG signal, one to be assigned to the first heartbeat waveform group and one to be assigned to the second heartbeat waveform group; The biological signal processing device according to claim 3 .

7. The P wave determination unit determines the number of heartbeat waveforms in the first heartbeat waveform group and the number of heartbeat waveforms in the second heartbeat waveform group. dividing the plurality of heartbeat waveforms extracted from the time series data of the ECG signal into the first heartbeat waveform group and the second heartbeat waveform group so that the number of heartbeat waveforms in the first heartbeat waveform group and the number of heartbeat waveforms in the second heartbeat waveform group are equal to each other; The biological signal processing device according to claim 3 .

8. The P wave determination unit A waveform of a P-wave section set based on a time point of an R-wave is extracted from each heartbeat waveform of the first heartbeat waveform group, and an average, median, or mode of the waveforms of the plurality of P-wave sections extracted from the first heartbeat waveform group is obtained as the first representative P-wave section waveform; A waveform of a P-wave section set based on a time point of an R-wave is extracted from each heartbeat waveform of the second heartbeat waveform group, and an average, median, or mode of the waveforms of the multiple P-wave sections extracted from the second heartbeat waveform group is obtained as the second representative P-wave section waveform. The biological signal processing device according to claim 3 .

9. The P wave determination unit evaluates a similarity between the first representative P wave section waveform and the second representative P wave section waveform based on a cross-correlation between the first representative P wave section waveform and the second representative P wave section waveform. The biological signal processing device according to claim 3 .

10. the candidate detection unit generates a representative heartbeat waveform, which is a representative heartbeat waveform, from a plurality of heartbeat waveforms extracted from the time-series data of the ECG signal, and detects candidates of P waves from the representative heartbeat waveform; The biological signal processing device according to claim 3 .

11. The candidate detection unit extracts a feature from a P-wave section that is set based on a time point of an R-wave in the representative cardiac waveform, and determines that there is a P-wave candidate when the feature satisfies a predetermined condition. The biological signal processing device according to claim 10.

12. the candidate detection unit selects data of a low-noise section in which a noise level is lower than a predetermined level from the time-series data of the ECG signal, and generates the representative heartbeat waveform using a plurality of heartbeat waveforms extracted from the data of the low-noise section. The biological signal processing device according to claim 10.

13. When the number of heartbeat waveforms extracted from the data in the low noise section does not reach a predetermined number, the P wave determination unit determines that it is impossible to determine whether or not a P wave is included in the time series data of the ECG signal. The biological signal processing device according to claim 12 .

14. When no P-wave candidate is detected by the candidate detection unit, the P-wave determination unit evaluates the degree of smoothness of a P-wave section set based on the time point of an R-wave in the representative heartbeat waveform, and when the degree of smoothness is equal to or greater than a predetermined value, determines that a P-wave is not included in the time series data of the ECG signal, and otherwise determines that it is impossible to determine whether or not a P-wave is included in the time series data of the ECG signal. The biological signal processing device according to claim 10.

15. an ECG sensor attached to a user's limb; A biological signal processing device according to any one of claims 1 to 14, which processes an ECG signal measured by the ECG sensor; A biosignal measuring device having the same.

16. A biological signal processing method executed by a biological signal processing device, comprising: obtaining time series data of an ECG signal measured from a user; extracting a first P-wave section waveform from a first P-wave section set based on a time point of a first R wave in time series data of the ECG signal, and extracting a second P-wave section waveform from a second P-wave section set based on a time point of a second R wave different from the first R wave, and determining that a P wave is included in the first P-wave section and the second P-wave section if a similarity between the first P-wave section waveform and the second P-wave section waveform is higher than a threshold.

17. A biological signal processing method executed by a biological signal processing device, comprising: obtaining time series data of an ECG signal measured from a user; extracting at least a first heartbeat waveform group and a second heartbeat waveform group from time series data of the ECG signal, extracting a waveform of a P-wave section set based on a time point of an R-wave from each heartbeat waveform of the first heartbeat waveform group, and extracting a waveform of a P-wave section set based on a time point of an R-wave from each heartbeat waveform of the second heartbeat waveform group, generating a first representative P-wave section waveform based on the waveform group of the plurality of P-wave sections extracted from the first heartbeat waveform group, and generating a second representative P-wave section waveform based on the waveform group of the plurality of P-wave sections extracted from the second heartbeat waveform group, and determining that a P-wave is included in the first heartbeat waveform group and the second heartbeat waveform group when a similarity between the first representative P-wave section waveform and the second representative P-wave section waveform is higher than a threshold value. A method for processing biological signals.

18. A biological signal processing method executed by a biological signal processing device, comprising: obtaining time series data of an ECG signal measured from a user; detecting P wave candidates from the time series data of the ECG signal; and when a P-wave candidate is detected, dividing a plurality of heartbeat waveforms extracted from the time series data of the ECG signal into a plurality of heartbeat waveform groups including at least a first heartbeat waveform group and a second heartbeat waveform group, acquiring a first representative P-wave section waveform and a second representative P-wave section waveform which are waveforms of representative P-wave sections from the first heartbeat waveform group and the second heartbeat waveform group, respectively, and determining that a P-wave is included in the time series data of the ECG signal when a similarity between the first representative P-wave section waveform and the second representative P-wave section waveform is higher than a threshold value. A method for processing biological signals.

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