Biological signal analyzer, computer program, and recording medium

Abstract

To allow a biological state, especially a state attributed to the movement of the heart to be grasped.SOLUTION: The present invention enables a border frequency between the apex beat and the cardiac sound to be obtained. It has become possible to know not only the cardiac sound but also the apex beat from biological signals, which are an aggregate of a biological sound and in-vivo vibration. The border frequency is expressed by a quadratic function with a cardiac rate, and using correlation data based on it, it has become possible to know the cardiac rate or the border frequency in real time. As a result, if the cardiac rate is known, for example, the size of the border frequency can be known. It has become easy to estimate a health condition from a wider point of view as compared with a conventional procedure, such as whether or not a load is imposed on the heart.SELECTED DRAWING: Figure 12

Description

[Technical field]

[0001] The present invention relates to a technique for estimating a biological state, and in particular to a biological signal analysis device using an index related to an apex beat of the heart, Computer Programs and recording media. [Background technology]

[0002] The apex beat reflects the movement of the left ventricular apex and is used to evaluate the heart, mainly the left ventricular function. The apex beat is generally objectively measured by the apex cardiogram (ACG) recorded by the mechanocardiogram (MCG) method, but the apex cardiogram is easily affected by the recording skill because it captures the movement of the chest wall through various tissues between the heart and the chest wall. In addition, most mechanocardiogram recording devices are relatively large, and there are restrictions such as the need to perform measurements in a soundproof room.

[0003] In consideration of this point, Patent Document 1 discloses a bioinformation measuring device for an apexcardiogram that is equipped with an ultrasound transmitter that transmits an ultrasound signal from the body surface toward the vicinity of the apex of the heart, and a receiving sensor that receives reflected ultrasound, and claims that the device can easily create an apexcardiogram even in special environments such as soundproof rooms.

[0004] Meanwhile, the present inventors have proposed a technique in Patent Documents 2 to 5 etc. for capturing vibrations occurring on the body surface of a person's back in an unconstrained manner and estimating the person's condition by analyzing the vibrations. The vibrations occurring on the body surface of a person's back are the result of the propagation of vibrations within the living body, such as the heart and aorta, and contain information on the systole and diastole of the atrium and ventricle, as well as elasticity information and reflected wave information of the blood vessel walls that act as auxiliary pumps for circulation.

[0005] In Patent Document 2, a predetermined time width is applied to the time series waveform of the back surface pulse wave of about 1 Hz extracted from the vibration (biological signal) propagating through the body surface, and a sliding calculation is performed to obtain a time series waveform of the frequency gradient, and the biological condition is estimated from the tendency of the change, for example, whether the amplitude is increasing or decreasing. It also discloses that the biological signal is frequency analyzed to obtain the power spectrum of each frequency corresponding to the function adjustment signal, fatigue acceptance signal, and activity adjustment signal belonging to a predetermined ULF band (very low frequency band) to VLF band (very low frequency band), and the human condition is judged from the time series change of each power spectrum.

[0006] Patent Documents 3 and 4 disclose a means for determining a homeostatic function level, and Patent Document 5 discloses a sound / vibration information collecting mechanism having a resonance layer equipped with a natural oscillator having a natural frequency corresponding to sound / vibration information of a biosignal. In addition, Non-Patent Document 1 shows that heart sounds are composed of irregular vibrations. Non-Patent Document 2 discloses that the waveforms of the above apexcardiogram are classified and used for diagnosis. Non-Patent Document 3 explains the fluctuation of the cardiac cycle. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] WO2012 / 165660 publication [Patent Document 2] JP 2011-167362 A [Patent Document 3] JP 2014-117425 A [Patent Document 4] JP 2014-223271 A [Patent Document 5] JP 2016-26516 A [Non-patent literature]

[0008] [Non-Patent Document 1] Nobuhiro, Y. et al. Development of Physical Condition Fluctuation Prediction Model Using Trunk Biosignals, Proceeding of Asia-Pacific Vibration Conference 2019 (2019) [Non-Patent Document 2] Yoshikawa, J. et al. Physical Examination in Cardiology, Bunkodo Shoten Co.,Ltd.,79-95 (2005) [Non-Patent Document 3] Kobayashi M., Musha T.: 1 / f fluctuation of heart beat period. IEEE Transactions on biomedical engineering. BME, 29, 45-457 (1982) Summary of the Invention [Problem to be solved by the invention]

[0009] The device disclosed in Patent Document 1 uses 192 ultrasonic transducers, and 12 transducers are grouped together and scanned by shifting them sequentially to create an apexcardiogram. Patent Document 1 also discloses a device using multiple rows of 192 ultrasonic transducers in order to create an apexcardiogram with higher accuracy. Patent Document 1 states that it is difficult to obtain a desired apexcardiogram without using such a large number of ultrasonic transducers. Therefore, the product is inevitably very expensive and not practical. In addition, in order to create a clear apexcardiogram, it is necessary to consider the detection of the position of the ribs, the transmission frequency is limited, and the position of the receiving element is also limited, and the device must satisfy these conditions, which also leads to a complex structure and increased manufacturing costs.

[0010] In addition, Patent Document 1 states that the advantage of this device is that it can be used by one person, whereas conventional electrocardiogram recording devices require multiple people to operate. However, the ultrasound transducer needs to be operated by a doctor or technician, which makes the measurement time-consuming, and only data can be obtained while scanning. In other words, this device is not suitable for capturing the apical pulsation of a subject over a long period of time.

[0011] On the other hand, the sensors disclosed in Patent Documents 2 to 5 that capture vibrations occurring on the body surface in a non-constrained manner consist of a three-dimensional knitted fabric, a film surrounding the three-dimensional knitted fabric, a microphone, etc., and can acquire biosignals via the body surface simply by placing them in contact with the human body. In other words, if the sensors are attached to the human body, biosignal data can be obtained without any operation by a doctor or the like. However, Patent Documents 2 to 5 mainly analyze autonomic nerve function and heart rate variability, and do not analyze apical pulsation, which is the movement of the left ventricular apex.

[0012] In addition, the sensors used in Patent Documents 2 to 5 are suitable for passively capturing acoustic vibrations of 20 to 100 Hz propagating from the surface of the human body on the back, but are not suitable for extracting apical pulsation, which is an internal vibration of 10 Hz or less. Furthermore, in order to make a diagnosis using the apexcardiogram of Non-Patent Document 3, professional knowledge is required to read the waveform of the apexcardiogram, and the judgment results may differ depending on the judge, leaving room for improvement in terms of judgment accuracy and speed. Therefore, diagnosis using the apexcardiogram is only used as a supplementary tool. Furthermore, although such an apexcardiogram is used as information related to the apex beat, albeit in a supplementary manner, for diagnosis, indices related to the apex beat other than the apexcardiogram are not used to estimate various health conditions of a person (presence or absence of diseases such as heart disease, identification of diseases, physical condition, etc.).

[0013] The present invention has been made in consideration of the above, and has an objective of providing a biosignal analysis device, a computer program, and a recording medium that can be easily and inexpensively manufactured, is capable of measuring over long periods of time, analyzes biosignals collected by a biosignal detection sensor to extract information related to the apical beat, and can also estimate various health conditions of a person. [Means for solving the problem]

[0014] In order to solve the above problems, the present inventors have developed a biosignal detection sensor that uses a three-dimensional knitted fabric and a microphone, etc., and can acquire biosignal data of 0.5 to 80 Hz. On the other hand, the biosignal detection sensor only passively captures acoustic vibrations propagated from the body surface, and the captured biosignal data includes various biosounds and internal vibrations. As a result of expanding the collectable frequency band to the low frequency band, data related to the apex beat is now included, but a new problem is encountered in that the data is hidden in the heart sound data, making it difficult to extract only the data of the apex beat. Therefore, the present inventors have conducted intensive research to solve such problems, and have completed the present invention.

[0015] That is, the biosignal analyzer of the present invention is a frequency analysis means for performing frequency analysis on biosignal data obtained by a biosignal detection sensor via a body surface; a boundary frequency identifying means for determining a boundary frequency between a vibration caused by an apex beat and a vibration caused by a heart sound in the biosignal data from a frequency analysis result of the biosignal data obtained from the frequency analysis means; The present invention is characterized by having the following.

[0016] The boundary frequency identification means It is preferable that the frequency analysis method further comprises means for determining a sudden change portion of a power spectrum that is a boundary between harmonic vibration and irregular vibration in the frequency analysis result, and for specifying the boundary frequency based on the sudden change portion. The boundary frequency identification means It is preferable to include a means for determining abrupt changes in the power spectrum by adding a result of frequency analysis of simultaneously measured heart sound data to the result of the frequency analysis.

[0017] The boundary frequency identification means a double logarithmic axis display means for displaying a waveform obtained by averaging the results of frequency analysis of the biosignal data and the heart sound data on a double logarithmic axis using a logarithmic difference method; and a sudden change portion identification means for determining a fluctuation change point from the waveform displayed on the double logarithmic axis and identifying the fluctuation change point as a sudden change portion of the power spectrum. It is preferred that the compound has the formula:

[0018] A short-time Fourier transform is applied as the frequency analysis means, The boundary frequency identifying means preferably includes means for determining abrupt change portions of the power spectrum from the analysis results of the short-time Fourier transform. the frequency analysis means includes means for outputting an analysis result of the short-time Fourier transform into image data indicating the degree of fluctuation in time, frequency, and power spectrum; The boundary frequency identifying means preferably includes means for determining an abrupt change portion of the power spectrum from the image data.

[0019] a correlation data storage unit in which correlation data relating to the boundary frequency is stored; a measurement time state estimation means for estimating the health state of the measurement subject at the time of measurement by collating the correlation data; It is preferable that the compound further comprises:

[0020] the correlation data is correlation data between the boundary frequency and a heart rate, It is preferable that the measurement-time state estimation means includes means for estimating the boundary frequency of the subject at the time of measurement by comparing the subject's heart rate at the time of measurement with correlation data between the boundary frequency and the heart rate. the correlation data is correlation data between the boundary frequency and a fluctuation characteristic of heart rate variability, It is preferable that the measurement-time state estimation means includes means for estimating the fluctuation characteristics of the heart rate fluctuation of the subject at the time of measurement by comparing the boundary frequency at the time of measurement of the subject with correlation data between the boundary frequency and the fluctuation characteristics of the heart rate fluctuation.

[0021] an apex pulsation waveform extraction means for filtering the biological signal data by setting the boundary frequency identified by the boundary frequency identification means as an upper limit value, and obtaining a waveform of vibration caused by the apex pulsation; A waveform classification means for classifying the waveform of the vibration generated by the apex beat of the heart; With It is preferable that the measurement-time condition estimation means includes means for classifying, by the waveform classification means, the waveform of vibration generated by the apex beat of the subject at the time of measurement, which is obtained by the apex beat waveform extraction means, and estimating the health condition of the subject at the time of measurement based on data of the waveform classification result at the time of measurement.

[0022] The correlation data storage unit stores correlation data between the waveform classification result and a health condition in advance, It is preferable that the measurement-time condition estimation means includes means for classifying, by the waveform classification means, the waveform of vibration generated by the apex beat of the subject at the time of measurement, which is obtained by the apex beat waveform extraction means, and for estimating the health condition of the subject at the time of measurement by comparing the waveform classification result at the time of measurement with pre-stored correlation data between the waveform classification result and health condition.

[0023] a model creation means for generating an estimation model by machine learning to estimate the health condition from the waveform information by using the waveform information output by the apex beat waveform extraction means and the health condition as teacher data, It is preferable that the measurement condition estimation means includes means for inputting waveform information of vibrations generated by the apex beat of the subject at the time of measurement, using an estimation model created by the model creation means, and outputting a value of the health condition at the time of measurement from the acquired waveform information at the time of measurement.

[0024] The waveform classification means includes the following means (A) and (B): (A) a means for classifying the waveform by a mathematical approach using a Fourier series expansion; (B) A means for classifying the waveform by combining one or more pieces of data obtained by the following engineering approaches (1) to (4): (1) data showing a shape of a time waveform of a waveform of vibration caused by the apex beat in a predetermined time range; (2) data of a graph in which the frequency and power spectrum obtained by the frequency analysis means of the time waveform are plotted on the horizontal and vertical axes, and (3) image data for a predetermined time range showing the degree of fluctuation in time, frequency and power spectrum, which are the analysis results of the short-time Fourier transform of the time waveform; (4) Data for a given time range regarding the type and period of the waveform extracted by the correlogram; Using The waveform of the vibration caused by the apex pulsation of a healthy person is classified into five types, It is preferable to estimate whether the subject's health condition at the time of measurement corresponds to any of the five waveform classification results.

[0025] The biological signal detection sensor includes: Three-dimensional knitting and A containing film that hermetically covers the periphery of the three-dimensional knitted fabric; A microphone disposed on the outer side of the containing film; a case for covering the microphone; and a disturbance intrusion suppression member for suppressing disturbances from entering the microphone within the case. It is preferable that the optical fiber 100 is configured as follows: The disturbance-mixing suppression member is preferably a gel.

[0026] The present invention also provides a computer program for processing biosignal data obtained by a biosignal detection sensor via a body surface and causing a computer to function as a biosignal analysis device, comprising: A step of subjecting the biological signal data to frequency analysis; a step of identifying a boundary frequency between vibrations caused by apex pulsation and vibrations caused by heart sounds in the biosignal data from the frequency analysis results; The present invention provides a computer program for causing the computer to execute the above steps.

[0027] In the step of identifying the boundary frequency, It is preferable to obtain a sudden change portion of the power spectrum that is a boundary between harmonic vibration and irregular vibration from the frequency analysis result, and to specify the boundary frequency based on this sudden change portion. In the step of identifying the boundary frequency, It is preferable to determine the sudden change part of the power spectrum by adding the result of frequency analysis of the heart sound data measured at the same time to the result of the frequency analysis.

[0028] In the step of identifying the boundary frequency, It is preferable to display waveforms obtained by averaging the results of frequency analysis of the biological signal data and the heart sound data on a double logarithmic axis using a logarithmic difference method, to find a fluctuation change point from the waveform displayed on the double logarithmic axis, and to identify this fluctuation change point as a sudden change part in the power spectrum.

[0029] In the step of performing the frequency analysis, a short-time Fourier transform is applied, In the step of identifying the boundary frequency, it is preferable to determine a sudden change portion of the power spectrum from the analysis result of the short-time Fourier transform. In the step of performing the frequency analysis, the analysis result of the short-time Fourier transform is outputted as image data indicating the degree of fluctuation in time, frequency and power spectrum; In the step of identifying the boundary frequency, it is preferable to obtain an abrupt change portion of the power spectrum from the image data. It is preferable to execute a procedure of estimating the health condition of the subject at the time of measurement by comparing the result with correlation data related to the boundary frequency stored in a correlation data storage unit.

[0030] the correlation data is correlation data between the boundary frequency and a heart rate, In the step of estimating the state at the time of measurement, it is preferable to compare the heart rate of the subject at the time of measurement with correlation data between the boundary frequency and the heart rate to estimate the boundary frequency of the subject at the time of measurement. the correlation data is correlation data between the boundary frequency and a fluctuation characteristic of heart rate variability, In the step of estimating the state during measurement, it is preferable to compare the boundary frequency during measurement of the subject with correlation data between the boundary frequency and the fluctuation characteristics of heart rate fluctuations to estimate the fluctuation characteristics of the heart rate fluctuations of the subject during measurement.

[0031] Further, a step of filtering the biological signal data by setting the boundary frequency identified by the boundary frequency identification means as an upper limit value, and obtaining a waveform of vibration caused by the apex beat of the heart; classifying the waveform of the vibration caused by the apex beat; on the computer, In the procedure for estimating the condition at the time of measurement, it is preferable that the waveform of the vibration generated by the apex beat of the subject at the time of measurement is classified by executing a procedure for classifying the waveform, and the health condition of the subject at the time of measurement is estimated based on the waveform classification result. the correlation data storage unit stores correlation data between the waveform classification result and a health condition, In the step of estimating the condition at the time of measurement, it is preferable to classify the waveform of the vibration caused by the apex beat of the subject at the time of measurement by executing a step of classifying the waveform of the vibration caused by the apex beat of the subject, and to compare the waveform classification result at the time of measurement with correlation data between the waveform classification result and health condition to estimate the health condition of the subject at the time of measurement.

[0032] generating an estimation model by machine learning that estimates the health condition from the waveform information, using waveform information that is output by executing the procedure of extracting the apex beat waveform and is to be classified in the procedure of classifying the waveform and the health condition as teacher data; It is preferable that the procedure for estimating the condition at the time of measurement uses waveform information of the vibration generated by the apex beat of the subject at the time of measurement as input, and uses the estimation model to output a value of the health condition at the time of measurement from the acquired waveform information at the time of measurement.

[0033] The procedure for classifying the waveform includes the following procedures (A) and (B): (A) classifying the waveform using a mathematical approach using Fourier series expansion; (B) classifying the waveform by combining one or more pieces of data obtained by the following engineering approaches (1) to (4): (1) data showing a shape of a time waveform of a waveform of vibration caused by the apex beat in a predetermined time range; (2) data of a graph in which the frequency and power spectrum obtained by the frequency analysis means of the time waveform are plotted on the horizontal and vertical axes, and (3) image data for a predetermined time range showing the degree of fluctuation in time, frequency and power spectrum, which are the analysis results of the short-time Fourier transform of the time waveform; (4) Data for a given time range regarding the type and period of the waveform extracted by the correlogram; Using The waveform of the vibration caused by the apex pulsation of a healthy person is classified into five types, It is preferable to estimate whether the subject's health condition at the time of measurement corresponds to any of the five waveform classification results.

[0034] The present invention also provides a recording medium on which the above computer program is recorded. Effect of the Invention

[0035] According to the present invention, the boundary frequency between the apex beat and the heart sound can be obtained. The boundary frequency, which is a new index related to the apex beat, correlates with the fluctuation characteristics of the heart rate variation, and the boundary frequency can be used to estimate the health condition (presence or absence of diseases such as heart disease, identification of diseases, physical condition, etc.). Furthermore, according to the present invention, the boundary frequency can be expressed as a function with the heart rate, and the correlation data based on this can be used to know the boundary frequency in real time.

[0036] In addition, in the present invention, the waveform of vibration generated by the apical pulsation obtained by using the above-mentioned boundary frequency is classified, and each waveform is associated with a person's health condition (presence or absence of a disease such as a heart disease, identification of the disease, physical condition, etc.), thereby making it possible to estimate the health condition. This makes it possible to automatically perform evaluation of the waveform of the apical cardiogram, which was previously performed by a specialist such as a doctor. In particular, by using the waveform information of the apical pulsation and the health condition as teacher data and configuring an estimation model for estimating the health condition from the waveform information by machine learning, it is possible to improve the accuracy of estimating the health condition. [Brief description of the drawings]

[0037] [Figure 1] FIG. 1(a) is an external perspective view of a biosignal detection sensor (4SR) according to a first embodiment of the present invention, FIG. 1(b) is an external perspective view showing an air pack and a gel pack separated, and FIG. 1(c) is a cross-sectional view. [Diagram 2] FIG. 2 is a diagram showing the structure of the biological signal detection sensor (4SR) according to the first embodiment of the present invention in comparison with a conventional biological signal detection sensor (3SR). [Diagram 3]Figure 3(a) is a diagram showing the mechanical replica sound (PCG replica input) of a phonocardiogram (PCG) output from a speaker and input to the embodiment of the biosignal detection sensor (4SR) and the conventional biosignal detection sensor (3SR), and the acoustic vibration data transmitted by the three-dimensional knitted fabric. Figure 3(b) is a diagram showing the resonant waveform of the PCG replica input and the frequency components that constitute the modulated PCG replica input. Figure 3(c) is a Lissajous figure showing the spring characteristics and damping characteristics of the embodiment of the biosignal detection sensor (4SR) and the conventional biosignal detection sensor (3SR). Figure 3(d) is a Bode plot. Figure 3(e) is a diagram showing the spring constant of a structure (sealed air pack) in which the three-dimensional knitted fabric in the embodiment of the biosignal detection sensor (4SR) is sealed within a containing film. [Figure 4] Figure 4 shows the amplification effect when a PCG waveform is input, where (a) shows the output waveforms of the embodiment's biosignal detection sensor (4SR) and a conventional biosignal detection sensor (3SR), (b) shows the frequency analysis results for each, and (c) shows the gain of 4SR / 3SR. [Diagram 5] FIG. 5 is a diagram showing the amplification effect of APW (Acoustic Pulse Wave), where (a) shows the output waveforms of the embodiment's biosignal detection sensor (4SR) and a conventional biosignal detection sensor (3SR), (b) shows the respective frequency analysis results, and (c) shows the gain of 4SR / 3SR. [Figure 6] FIG. 6 is a diagram for explaining a comparative experiment between the biological signal detection sensor (4SR) of the first embodiment and a structure including only an air pack without a gel pack. [Figure 7] Figures 7(a)-(c) show the experimental results for 4SR, and Figures 7(d)-(f) show the experimental results for a structure with only air pack 1A (4SR without gel pack). Figure 7(g) is an enlarged view of (d), and Figure 7(h) is an enlarged view of (e). Figure 7(i) shows a Bode plot, Figure 7(j) shows the load-deflection characteristics of 4SR and a structure with only air pack 1A, and Figure 7(k) shows the amplitude ratio between the two (4SR / 4SR without gel pack). [Figure 8] 8(a) to (d) show the results of 3-second time-series waveform measurements of the F-APW, R-APW, L-APW and PCG of a subject with an average heart rate of 58 / min. [Figure 9] 9(a) to (d) show the results of 3-second time-series waveform measurements of F-APW, R-APW, L-APW and PCG of a subject with an average heart rate of 71 / min. [Figure 10] 10(a) to (d) show the results of measuring the time series waveforms of F-APW, R-APW, L-APW and PCG for 3 seconds in a subject with an average heart rate of 79 / min. [Figure 11] 11(a) to (d) show the results of measuring the time-series waveforms of F-APW, R-APW, L-APW and PCG for 3 seconds in a subject with an average heart rate of 93 / min. [Figure 12] FIG. 12 is a diagram showing a schematic structure of the biosignal analyzer according to the first embodiment of the present invention. [Figure 13] FIG. 13 is a flowchart illustrating the process of extracting the boundary frequency. [Figure 14] 14(a) to (d) are diagrams showing the frequency analysis results of CAB (Cardiac Apex Beat) and CAS (Cardiac Acoustic Sound) for subjects with average heart rates of 58 / min, 71 / min, 79 / min, and 93 / min. [Figure 15] FIG. 15 shows the processed waveforms of Front CAB(0.5-BF), Front CAB(5-BF), Rear CAB(0.5-BF), Lumbar CAB(0.5-BF), PCG, Front CAS(BF-50), Rear CAS(BF-50), and Lumbar CAS(BF-50) obtained from a subject with an average heart rate of 58 / min. [Figure 16]FIG. 16 shows the processed waveforms of Front CAB(0.5-BF), Front CAB(5-BF), Rear CAB(0.5-BF), Lumbar CAB(0.5-BF), PCG, Front CAS(BF-50), Rear CAS(BF-50), and Lumbar CAS(BF-50) obtained from a subject with an average heart rate of 71 / min. [Figure 17] FIG. 17 shows the processed waveforms of Front CAB(0.5-BF), Front CAB(5-BF), Rear CAB(0.5-BF), Lumbar CAB(0.5-BF), PCG, Front CAS(BF-50), Rear CAS(BF-50), and Lumbar CAS(BF-50) obtained from a subject with an average heart rate of 79 / min. [Figure 18] FIG. 18 shows the processed waveforms of Front CAB(0.5-BF), Front CAB(5-BF), Rear CAB(0.5-BF), Lumbar CAB(0.5-BF), PCG, Front CAS(BF-50), Rear CAS(BF-50), and Lumbar CAS(BF-50) obtained from a subject with an average heart rate of 93 / min. [Figure 19] 19(a) to (d) are graphs showing the ECG, F-APW, and PCG for four subjects with average heart rates of 58 / min, 71 / min, 79 / min, and 93 / min. [Figure 20] 20(a) to (d) are diagrams showing ECG, Front CAB, and Front CAS for four subjects with average heart rates of 58 / min, 71 / min, 79 / min, and 93 / min. [Figure 21] 21(a) to (d) are diagrams showing the power spectral densities (PSD) of the Front CAB and the Front CAS for four subjects with average heart rates of 58 / min, 71 / min, 79 / min, and 93 / min. [Figure 22]FIG. 22 shows the results of STFT analysis of a subject with an average heart rate of 93 / min over a 10-second measurement period, where (a) shows the results of F-APW analysis and (b) shows the results of PCG analysis. [Figure 23] FIG. 23(a) shows the results of STFT analysis of F-APW×PCG-1 for a subject with an average heart rate of 93 / min over a 10-second measurement period, and FIG. 23(b) is an enlarged view of FIG. 23(a). [Figure 24] Figure 24(a) shows the results of STFT analysis of F-APW×PCG of a subject with an average heart rate of 93 / min over a 10-second measurement time, and Figure 24(b) shows the power spectra of F-APW×PCG-1, F-APW, PCG, and F-APW×PCG. [Diagram 25] Figure 25(a) shows the power spectra of F-APW×PCG-1, F-APW, PCG, and F-APW×PCG of a subject with an average heart rate of 58 / min, and Figure 25(b) shows the results of STFT analysis of F-APW×PCG-1. [Figure 26] Figure 26(a) shows the power spectra of F-APW×PCG-1, F-APW, PCG, and F-APW×PCG of a subject with an average heart rate of 71 / min, and Figure 26(b) shows the results of STFT analysis of F-APW×PCG-1. [Figure 27] Figure 27(a) shows the power spectra of F-APW×PCG-1, F-APW, PCG, and F-APW×PCG of a subject with an average heart rate of 79 / min, and Figure 27(b) shows the results of STFT analysis of F-APW×PCG-1. [Figure 28] FIG. 28(a) is a BF-HR correlation diagram for 50 subjects, and FIG. 28(b) is a correlation diagram between the harmonic disappearance order and HR. [Figure 29]Figure 29(a) is a graph showing the correlation between the fluctuation characteristics of heart rate variability and heart rate, Figure 29(b) is a graph showing the correlation between the fluctuation characteristics of heart rate variability and BF, Figure 29(c) is a graph showing the correlation between the heart rate and BF of a single subject, Figure 29(d) is a graph showing the correlation between the heart rate and BF of another single subject, and Figure 29(e) is a graph showing the correlation between the heart rate and BF of yet another single subject. [Diagram 30] FIG. 30 is a schematic diagram showing the configuration of a biosignal analyzer according to the second embodiment. [Diagram 31] FIG. 31 is a schematic diagram for explaining another aspect of the biosignal analyzer according to the second embodiment. [Diagram 32] 32(a) to (e) are diagrams illustrating typical waveform analysis results of Front CAB (0.5-BF) and are used to explain examples of pseudo sine waves. [Diagram 33] 33(a) to (e) are diagrams illustrating typical waveform analysis results of Front CAB (0.5-BF) and are used to explain an example of a triangular wave. [Diagram 34] 34(a) to (e) are diagrams illustrating typical waveform analysis results of Front CAB (0.5-BF) and are used to explain an example of a cosine wave. [Diagram 35] 35(a) to (e) are diagrams illustrating typical waveform analysis results of Front CAB (0.5-BF) and are used to explain examples of Gaussian waves. [Diagram 36] 36(a) to (e) are diagrams illustrating typical waveform analysis results of Front CAB (0.5-BF) and are used to explain examples of pseudo full-wave rectified waves. [Figure 37] FIG. 37 shows the results of waveform analysis using two types of STFT, power spectrum, and correlogram. [Figure 38] 38(a) to (e) are diagrams showing the results of frequency analysis for each measurement site of five subjects. [Figure 39] FIG. 39 is a diagram showing the amplitude ratio for each measurement site. [Diagram 40]FIG. 40 is a BF-HR correlation diagram when heart rate fluctuation occurs under respiratory inhibition at rest. [Diagram 41] FIG. 41(a) is a BF-HR correlation diagram in the supine position, FIG. 41(b) is a BF-HR correlation diagram in the left lateral position, and FIG. 41(c) is a BF-HR correlation diagram in the right lateral position. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0038] Hereinafter, the present invention will be described in more detail based on the embodiments of the present invention shown in the drawings. (First embodiment) -Biological signal detection sensor First, the configuration of the biosignal detection sensor 1 used in this embodiment will be described with reference to Figs. 1(a) to (c). The biosignal detection sensor 1 of this embodiment has a laminated structure of an air pack 1A and a gel pack 1B. The air pack 1A is configured to have a three-dimensional knitted fabric (3D net) 10 and a housing film 20 that hermetically houses the three-dimensional knitted fabric (3D net) 10. The gel pack 1B has a microphone 30 fixedly disposed within a case 40, and gel 50 is filled around the microphone 30.

[0039] The three-dimensional knitted fabric 10 is formed by connecting a pair of ground knitted fabrics arranged at a distance from each other with a connecting thread. Each ground knitted fabric can be formed, for example, from a yarn made of twisted fibers into a flat knitted fabric structure (fine stitches) that is continuous in both the wale direction and the course direction, or into a knitted fabric structure having a honeycomb-shaped (hexagonal) mesh. The connecting thread imparts a predetermined rigidity to the three-dimensional knitted fabric so that one ground knitted fabric and the other ground knitted fabric maintain a predetermined distance. Therefore, by applying tension in the surface direction, it is possible to string-vibrate the yarns of the opposing ground knitted fabrics that constitute the three-dimensional knitted fabric, or the connecting threads that connect the opposing ground knitted fabrics. As a result, string vibration is generated by the sound and vibration of the heart and blood vessels, which are biological signals, and is propagated in the surface direction of the three-dimensional knitted fabric.

[0040] Various materials can be used as the material of the yarn or connecting yarn forming the ground knit of the three-dimensional knitted fabric, for example, synthetic fibers such as polypropylene, polyester, polyamide, polyacrylonitrile, rayon, regenerated fibers, and natural fibers such as wool, silk, and cotton. The above materials may be used alone or in combination. Preferably, polyester fibers such as polyethylene terephthalate (PET) and polybutylene terephthalate (PBT), polyamide fibers such as nylon 6 and nylon 66, polyolefin fibers such as polyethylene and polypropylene, or a combination of two or more of these fibers. In addition, the shape of the ground yarn or connecting yarn is not limited, and may be any of round cross-section yarn, modified cross-section yarn, hollow yarn, etc. Furthermore, carbon yarn, metal yarn, etc. can also be used.

[0041] Examples of the three-dimensional knitted fabric that can be used include the following: (a) Product number: 49013D (manufactured by Suminoe Textile Co., Ltd.), thickness 10 mm Material: Front ground knit: 2 strands of 450dtex / 108f polyethylene terephthalate false twist yarn Back ground knit: 2 strands of 450dtex / 108f polyethylene terephthalate false twist yarn Connecting thread: 350dtex / 1f polytrimethylene terephthalate monofilament (b) Product number: AKE70042 (manufactured by Asahi Kasei Corporation), thickness 7 mm (c) Product number: T28019C8G (manufactured by Asahi Kasei Corporation), thickness 7 mm

[0042] The three-dimensional knitted fabric 10 is covered with a storage film 20. In this embodiment, the storage film 20 is made of two synthetic resin films 21 and 22, which are arranged to cover the front and back surfaces of the three-dimensional knitted fabric 10, and the peripheral portions of both films are fixed by welding or the like. As a result, the three-dimensional knitted fabric 10 is hermetically stored in the storage film 20. When fixing the peripheral portions of the films 21 and 22, it is preferable to fix the three-dimensional knitted fabric 10 so that the films 21 and 22 slightly press the three-dimensional knitted fabric 10 in the thickness direction. The tension of the three-dimensional knitted fabric 10 increases, and string vibration of the yarn constituting the three-dimensional knitted fabric 10 is more likely to occur.

[0043] A case 40 is attached to the outside of the containing film 20, and the microphone 30 is disposed inside the case 40. Inside the case 40, the area around the microphone 30 is filled with gel 50 as a disturbance mixing suppression member. The case 40 is made of synthetic resin and has a function of preventing acoustic vibrations propagated to the microphone 30 from diffusing to the outside, and the gel 50 suppresses external vibrations from being captured by the microphone 30. A cord 30a that carries detected acoustic vibration data is connected to the microphone 30.

[0044] The biosignal detection sensor 1 is used by contacting various parts of a person, such as the back, chest, waist, etc. The vibrations on the body surface are transmitted to the containing film 20 and the three-dimensional knitted fabric 10 and picked up by the microphone 30, but it is not limited to being directly attached to the skin surface, and can be attached to the surface of clothing.

[0045] Here, the detection performance of the novel biosignal detection sensor 1 employed in this embodiment will be explained in comparison with the conventional biosignal detection sensor disclosed by the present inventors in Patent Document 2 etc. In the following, the biosignal detection sensor 1 of this embodiment will be referred to as 4SR (Sound Sensing System using Stochastic Resonance), and the conventional biosignal detection sensor 1000 will be referred to as 3SR (Sound Sensing System using Resonance).

[0046] (4SR sensing performance evaluation in the heart sound frequency band) FIG. 2 shows an outline of the experimental equipment and its components for comparing the sensing performance of 4SR (biological signal detection sensor 1) and 3SR (conventional biological signal detection sensor 1000). The three photographs on the left and right of the experimental equipment in the center show the acceleration sensor (a-1) and the components (a-2 to a-6) of the 3SR and 4SR, respectively, and overview photographs of the 3SR (a-7) and 4SR (a-8) are shown at the bottom of the experimental equipment. Photograph (a-4) shows a cross section of the 10 mm thick three-dimensional knitted fabric (hereinafter sometimes referred to as 3D net) used in 3SR and 4SR. Photographs (Section: aa, Section: bb) are cut cross sections of the 3D net for 3SR and 4SR.

[0047] First, the basic principle of the 4SR's amplitude amplification will be explained using a cross-sectional photograph (a-4) of the 3D net. The top and bottom surfaces of the 3D net used in this experiment are fabric layers made of multifilament yarn, and the pile in the middle made of monofilament yarn is woven into the top and bottom fabric layers and is frictionally bonded to the multifilament yarn. When the subject's weight is added to the top surface of the 3D net, the pile woven into an X-shape of the 3D net built into the air pack (hereinafter referred to as the X-shaped pile) bends, and the pile rubs against each other due to body movement, generating sound and vibration. This sound and vibration cause the stochastic resonance phenomenon. The X-shaped pile bends, generating tension, forming a natural oscillator. The natural frequency of the X-shaped pile under tension is around 20 Hz. Therefore, it is thought that the 4SR, which is a sealed air pack, increases vibrations from 0.5 to 80 Hz through two resonance mechanisms: stochastic resonance and string vibration.

[0048] Next, the amplification ability for heart sounds will be examined. As shown in Figure 3(a), the heart sound used in the study was the I sound on the PCG with a split interval of 0.03 seconds. This heart sound was measured from a subject with a heart rate of 56 beats per minute, and after filtering, it was mechanically reproduced using a speaker. The speaker used was a FOSTEX P1000K without an enclosure. The P1000K is a cone-type full-range unit with a diameter of 10 cm. A 600g weight was placed on the diaphragm of the P1000K, making the weight of the diaphragm 1005g, and the lowest resonance frequency was lowered from 82Hz to 52Hz. This resulted in a playback frequency band of 52Hz to 16kHz. The lowest resonance frequency f0 was:

[0049]

number

[0050] The mechanically reproduced sound (hereafter referred to as Reproduction PCG) output from the speaker is measured by each microphone of the 3SR and 4SR after passing through the 3D net placed on the top surface of the acceleration sensor. The microphone 1010 of the 3SR is wrapped in the elastomer film 1030 and beads 1040 together with the 3D net 1020. The attenuation occurs when the air around the microphone flows due to the spring force of the built-in 3D net caused by the air flowing through the slit. The magnitude of the attenuation is determined by the width of the slit around the cord 1010a attached to the microphone as shown in photo (a-7). Since the microphone 1010 of the 3SR is placed inside the elastomer film 1030, the cord 1010a must be pulled out of the elastomer film 1030, and a gap is inevitably generated at that part. On the other hand, in the 4SR, the air pack 1A in which the three-dimensional knitted fabric (3D net) 10 is housed in the housing film 20 is separated from the gel pack 1B in which the microphone 30 is disposed, and the cord 30a of the microphone 30 is pulled out from the gel pack 1B, so that the airtightness of the air pack 1A is not impaired by it. That is, in the 4SR, the entire circumference of the three-dimensional knitted fabric (3D net) 10 is sealed with an elastomer film (housing film 20), and when a load is applied to the 4SR, a uniform air pressure is applied to the elastomer film, so that tension is generated in the elastomer film, creating an environment in which acoustic vibrations are easily propagated (see FIG. 3(c)).

[0051] As mentioned above, Fig. 3(a) shows the PCG replica input and the acoustic vibration data transmitted by the 3D net, but the acoustic vibration data transmitted by the 3D net consists of the modulated PCG replica input followed by a waveform due to the resonance effect of the 3D net's natural oscillator. The area of ​​the Lissajous figure in Fig. 3(c) indicates the energy consumed during one cycle of vibration, and the damping ratio of the 3SR calculated from the damping capacity was 0.53 and that of the 4SR was 0.20.

[0052] (4SR sensing performance evaluation in the apex beat frequency band) In the ACG of healthy subjects, the short duration pulsation observed only in the early contraction stage (the systolic wave consists of a short duration sharp positive wave followed by a dicrotic limb, and the A wave derived from the contraction of the left atrium is observed as a trace) is described as tapping, and it is difficult to obtain a good record with the ACG. Therefore, we selected a subject in a seated position (heart rate: 62 / min) who could palpate the apical pulsation described as tapping, and measured the F-APW (biological information measured from the front of the chest: Front Acoustic Pulse Wave) using 3SR and 4SR, recorded it on a data logger, and performed the following analysis after filtering.

[0053] The input waveform recorded in the data logger was frequency-analyzed, and the ratio of the power spectrum of 4SR to 3SR with respect to frequency (PSD 4SR / PSD 3SR ) is used as the gain, and sensing performance is evaluated using the gain. The 4SR is placed on the front of the chest, 6 cm to the left of the 5th intercostal space and the chest midline, and the 3SR is placed on the front of the chest, 10 cm to the left (equivalent to the vicinity of V4 lead on an electrocardiogram). Since the microphones for the PCG and 4SR sensing systems are placed in areas where alveolar respiratory sounds are likely to be mixed in, F-APW is measured for 10 seconds with breathing stopped. The measurement experiment was carried out by continuing breathing at rest for 5 minutes, inhaling for 2 seconds, and then stopping breathing. Healthy men in their 40s with no underlying cardiovascular diseases were selected as subjects.

[0054] (APW measurement) Next, the APW is measured. All measurements are performed in a seated position. The 4SR for capturing the F-APW, which is thought to include the apical pulsation, is placed on the front of the chest, 10 cm to the left of the chest midline, at the left 5th intercostal space and on the left midclavicular line (near V4). The 4SR for the R-APW (biological information measured from the posterior chest: Rear Acoustic Pulse Wave) is placed on the posterior chest, 6 cm to the left of the back midline, at the same height as the 4SR for the F-APW on the front of the chest. The 4SR for capturing the lumbar L-APW (biological information measured from the lumbar: Lumber Acoustic Pulse Wave) is placed in the midline of the 3rd to 4th lumbar vertebrae, directly behind the navel, and the microphone for PCG is placed at the apex of the heart. In addition, the ECG (Electrocardiogram) is acquired using lead II.

[0055] The placement of the microphone sensor at the apex of the heart on the anterior chest is the optimal location for recording the first, third, and fourth hearts, but it is difficult to obtain a good recording of the second heart sound. The second heart sound is said to be generated when blood in the arteries tries to flow back into the ventricle at the end of systole, causing excessive stretching of the semilunar valves and then recoil on the arterial wall (cardiohemic system), and is attenuated at the apex of the heart.

[0056] The microphones for the PCG and 4SR sensing systems are installed in locations where alveolar breathing sounds are likely to be mixed in, so calculations are performed using the results of measurements taken over 10 seconds while breathing is stopped. Note that the APW measurement experiment at rest is performed under four conditions: a) natural breathing continued for 5 minutes, b) breathing stopped for 30 seconds after 2 seconds of inhalation, c) natural breathing for 30 seconds, and d) breathing stopped for 30 seconds after 2 seconds of inhalation. The subjects were 70 healthy men and women with different characteristics, from a group with relatively thin chest walls and a group with relatively thick chest walls. The 50 subjects were healthy individuals with relatively thin chest walls, ranging in age from 20 to 60 years old, with 78% being male. A thin chest wall makes it easier to observe the sine wave or triangular wave that is characteristic of the apical pulsation. Using these subjects, we defined the five waveform types described below and determined the boundary frequency (BF) between the vibration caused by the apical pulsation and the vibration caused by the heart sound. Using 20 muscular subjects in their 20s with relatively thick chest walls, we evaluated the suitability and scalability of the analysis method based on the five waveform types previously determined. The clinical profile of the 50 healthy subjects with relatively thin chest walls was as follows: mean age 38.2 (±10.3) years, 39 men, 11 women, mean BMI 23 (±3.4), of which 4 had hypertension and 10 were smokers. On the other hand, the clinical profile of the 20 patients with relatively thick chest walls was as follows: mean age 23.4 (±3.6) years, 20 men, mean BMI 20.8 (±2.4), one patient with hypertension, and five smokers. The data to be analyzed was 10 seconds from the first 30 seconds after breathing was stopped during which RR fluctuation was within 15%, and the heart rate was calculated as the average value of the 10 seconds of data.

[0057] (4SR sensing system performance evaluation results and APW measurement results) In Fig. 3(a), the waveform shown by the thin black line is the Reproduction PCG, and the waveform shown by the thick gray line is the Acceleration PCG measured by the acceleration sensor. The Reproduction PCG is composed of the I sound (A and B waves), the waveform around 30 Hz (C wave), and the II sound (D, E, and F waves), while the Acceleration PCG is composed of the resonance waveforms of the Reproduction PCG (a and b waves, and d and e waves) and the subsequent waveforms with changed amplitude and frequency (c and f waves). As shown in the frequency analysis results in Fig. 3(b), the A and B waves and the D and E waves in the frequency band above 60 Hz have increased in amplitude and become a and b waves, and d and e waves, and the C and F waves in the vicinity of 30 Hz have become c and f waves whose amplitude and frequency are modulated by heterodyning. As a result, the power spectrum of the Acceleration PCG is smaller than that of the Reproduction PCG in the vicinity of 30 Hz.

[0058] Next, we will consider the damping performance contained in the mechanical vibration system that constitutes the sensing system. Figure 3(c) is a Lissajous figure showing the damping characteristics of the 3SR and 4SR sensors. The Lissajous figure was drawn using a servo pulsar with a pressure plate of 110 x 110 mm, an amplitude of ±1.0 mm, and an input of a vibration frequency of 1.34 Hz. The precompression force is 652 N. The area enclosed by the Lissajous figure is the energy consumed during one cycle of vibration, and is the damping capacity. If this is expressed as W, then W is:

[0059]

number

[0060] In addition, the amplitude magnification Z / Y used in evaluating the vibration displacement is expressed as follows, where Z is the relative displacement and Y is the displacement of the apex beat.

[0061]

number

[0062] Figure 3(d) shows the amplitude magnification curves of 4SR and 3SR calculated using equation (3), comparing the spring constants of each sensor of 4SR.

[0063] The value of TIFF0007637408000006.tif13170 was substituted.

[0064] Figure 3(e) shows the load-deflection characteristics obtained by measuring the sensor (4SR) shown in Figure 2(a-8) with a digital force gauge (RZ-20) using a pressure plate with a diameter of 15 mm. The spring constant was calculated assuming measurement of the apex pulsation. When measuring from the front of the chest, the pressing force of the sensor is small, so the spring constant when the deflection is 1 mm was used. When measuring from the back of the chest, the body weight is applied to the sensor, so the spring constant when the deflection is 4 to 5 mm, which is the design value of the 3SR, was used.

[0065] Because the natural frequency of the sensing system of the 4SR is set low, when measurements are taken in the range of Z / Y ≒ 1, the 4SR can measure frequencies above 0.45 Hz, while the 3SR can measure frequencies above 2.10 Hz. When the damping ratio is set to ζ = 0.7, the 4SR can measure frequencies above 0.99 Hz, while the 3SR can measure frequencies above 3.15 Hz. In this way, changing the damping ratio makes it possible to adjust the characteristics of the mechanical filter.

[0066] Cardiac auscultation is to grasp, interpret, and confirm the characteristics of heart sounds in the high frequency range of around 50 to 100 Hz, so it is important to record high frequency acoustic vibrations. On the other hand, the apical beat is a short-lasting beat that is only observed in the early systole and is in a lower frequency range than the heart sound, so if the emphasis is placed on measuring high frequencies, it becomes difficult to record a good apical cardiogram. As the required specifications for the sensing system for heart sounds and apical beats are different, the quantitative evaluation of the sensing systems was evaluated separately. Figure 4(a) shows a comparison of the acoustic vibration information captured by the 3SR and 4SR sensing systems (hereinafter referred to as 3SR data and 4SR data) using the Acceleration PCG as input. Figure 4(b) shows the frequency analysis results. The 3SR data and 4SR data are acoustic vibration information affected by stochastic resonance and resonance due to the natural oscillator, and when comparing the two, increases and decreases can be seen in the power spectrum in the same frequency band. Figure 4(c) shows the gain (PSD) of 4SR / 3SR 4SR / PSD 3SR ) is shown. The effect of stochastic resonance near 10 Hz, the resonance effect of the natural oscillator near 20 Hz, and the difference in the mechanical vibration characteristics of the sensor from 20 to 80 Hz are manifested as changes in gain (maximum 11.6 dB and minimum 8.0 dB), and the maximum amplitude difference of the waveform shown in Figure 4(a) was 4.3 times. The sudden drop in gain above 80 Hz is thought to be due to the damping effect of the gel attached to the 4SR sensing system.

[0067] Figure 5(a) shows the time series waveforms of 3SR data and 4SR data when a person is used as the sound source instead of a speaker, and Figure 5(b) shows the frequency analysis results. Figure 5(c) shows the gain (PSD 4SR / PSD 3SR ) The attenuation inherent in the 3SR sensing system works above 1.34 Hz, and while the gain around 1 Hz is 13.4 dB, the gain around 1.34 to 10 Hz drops to 4.9 dB. When viewed on the waveform, the effect of attenuation is noticeable, and as shown in Figure 5(a), the maximum amplitude of the systolic wave is 6.8 times larger.

[0068] Although the air damping of the 3SR only had a small effect on the irregular vibrations related to heart sounds, it did affect the power spectrum of the apical pulsation wave below 13 Hz, decreasing its value, as shown in Figure 5(b). The damping effect shown in Figure 3(c) was most noticeable mainly in the fundamental and second harmonics. This is consistent with the simulation results shown in Figure 3(d), and it was found that the apical pulsation wave could not be recorded with the 3SR.

[0069] Considering the vibration transmission mechanism, tension is generated in the elastomer film (the containing film with the reference number 20 in 4SR, and the elastomer film with the reference number 1030 in 3SR) itself, so that in 4SR, low-frequency vibrations around 1 Hz are transmitted to the internal 3D net through tension fluctuations. As a result, vibrations of 13 Hz or more were measured in 3SR and 4SR as almost the same power spectrum between 13 and 30 Hz shown in Figure 5 (b) due to the effect of the natural oscillator occurring around 20 Hz. It is considered that the vibration components are transmitted through the piles of the 3D net. In addition, in Figure 5 (b), it was found that the minimum frequency of the heart sound and the frequency band where the harmonic components of the apical pulsation wave are mixed into the heart sound are around 10 Hz. Therefore, it was found that 4SR has higher sensitivity than 3SR over the entire frequency range considered. Previously, the analysis target frequency of the 3SR was set to 10-30Hz, but based on the above experimental results, the frequency of the F-APW that the 4SR measures is set to 0.5-80Hz, taking into consideration that the performance guarantee range of the microphone is 0.1Hz or more, and for heart sounds, the analysis target frequency is set to 25-45Hz or 40-80Hz, rather than 100Hz or more, because most sounds are concentrated in the low audible range. In addition, the sampling frequency was set to 200Hz for the 3SR, but to 1000Hz for the 4SR.

[0070] Here, a comparative experiment was carried out between the biosignal detection sensor 1 (4SR) of this embodiment and a structure (4SR without gel pack) in which the gel pack 1B was removed from the 4SR and only the air pack 1A was used to measure acoustic vibration. Figure 6 shows the state of the experiment, in which the biosignal detection sensor 1 (4SR) was placed on the right side of the figure on a urethane base, and the structure (4SR without gel pack) with only the air pack 1A used was placed on the left side. The other experimental conditions were the same as those of the comparative experiment between 3SR and 4SR described above. The results are shown in Figure 7. Fig. 7(a)-(c) show the experimental results of 4SR. Fig. 7(d)-(f) show the experimental results of a structure with only the air pack 1A (4SR without gel pack). (a) and (d) show the time series waveforms of the acoustic vibration information captured when a person is the sound source, (b) and (e) are time series waveforms in which the center of the amplitude of the waveforms in (a) and (d) is set as the zero point, and (c) and (f) show the time series waveforms of the acoustic vibration information captured by inputting the Acceleration PCG. Fig. 7(g) is an enlarged view of (d), and Fig. 7(h) is an enlarged view of (e). As is clear from these figures, 4SR has a higher detection sensitivity for acoustic vibration information than a structure with only air pack 1A (4SR without gel pack). In particular, when a person was used as the sound source, 4SR caused an amplitude change of about ±0.2 [V], and was able to clearly capture acoustic vibration information, whereas it was very difficult to capture the amplitude change with a structure with only air pack 1A. Also, as shown in Figure 7 (j), 4SR had a smaller hysteresis loss in the load-deflection characteristics compared to a structure with only air pack 1A. The microphone 30 is placed on a thin, disk-shaped support member (member 30b in FIG. 6) on the bottom surface of a synthetic resin case 40, but the case 40 is about 0.1 to 0.2 mm thick and has lower rigidity than the disk-shaped support member 30b. Therefore, the case 40 easily vibrates in response to acoustic vibrations, resonates with the acoustic vibrations, and plays a role similar to the diaphragm and enclosure of a speaker, increasing the detection sensitivity of the microphone 30 and increasing the amplitude. FIG. 7(k) shows the amplitude ratio of the two (4SR / 4SR without gel pack). From this figure, it can be seen that 4SR not only has high detection sensitivity for extremely low-frequency acoustic vibrations such as apical pulsation, but also has high detection sensitivity in the range of heart sounds (25 to 80 Hz). In addition, the time waveforms of the microacoustic vibration (MAV) output from the trunk measured by a microphone directly attached to the chest and the F-APW measured from the 4SR (the response of the mechanical vibration system of the above-mentioned components that make up the 4SR, and the acoustic vibration information affected by resonance due to the natural oscillator and stochastic resonance) were frequency-analyzed, and the amplitude magnification was calculated to obtain the gain of the F-APW / MAV. The gain of the F-APW / MAV was 42 dB in the frequency band of the fundamental harmonic of the apex beat, slightly more than 30 dB in the frequency band of the harmonics, and 20 dB in the minimum frequency band of the heart sound near 20 to 30 Hz, which is the frequency band of the natural oscillator. The response of the mechanical vibration system and the effect of stochastic resonance were expressed as a maximum of more than 40 dB and a minimum of 10 dB, and the amplitude difference was just under 200 times. The waveform of the F-APW was observed in the 1 to 1.5 Hz band, and included information on the heart sound and the apex beat. The time lag between the MAV and the F-APW was 0.12 seconds.

[0071] Figures 8 to 11 show the results of 3-second time-series waveform measurements of F-APW, R-APW, L-APW and PCG for four subjects with average heart rates of 58, 71, 79 and 93 / min. These three types of APW contain information on heart sounds and apex beats. Waveforms in the 1 to 1.5 Hz band were observed for F-APW, and waveforms in the 5 to 7 Hz band for R-APW and L-APW.

[0072] Biosignal analysis equipment Next, a biosignal analyzer 100 having a computer function in which a computer program for processing data obtained from the biosignal detection sensor 1 of this embodiment is set will be described with reference to FIG.

[0073] The biosignal analyzer 100 processes time-series data of a biosignal acquired by the biosignal detection sensor 1 to obtain a waveform of an apical pulsation, and further estimates a biocondition, i.e., various health conditions of a person (presence or absence of diseases such as heart disease, identification of diseases, physical condition, etc.). The biosignal analyzer 100 is made up of a computer (including a personal computer and a microcomputer incorporated in a device), and receives time-series data of a biosignal transmitted from the microphone 30 of the biosignal detection sensor 1. It has a frequency analysis means 110 and a boundary frequency identification means 120 that perform predetermined processing using the received time-series data. The storage unit also has a correlation data storage unit 150 that stores correlation data between the boundary frequency between vibrations caused by apex beats and vibrations caused by heart sounds, which has been measured and constructed in advance, and the heart rate.

[0074] More specifically, in the biosignal analysis device 100, a computer program for executing procedures that function as the frequency analysis means 110 and the boundary frequency identification means 120 is stored in a storage unit (including storage media such as a built-in hard disk of the computer (biological signal analysis device 100), as well as various removable storage media and storage media of other computers connected via communication means). The biosignal analysis device 100 can also be realized using an electronic circuit having one or more storage circuits in which computer programs for realizing the frequency analysis means 110 and the boundary frequency identification means 120 are embedded.

[0075] The computer program can be provided by storing it in a recording medium. The recording medium storing the computer program may be a non-transient recording medium. There is no particular limitation on the non-transient recording medium, and examples of the non-transient recording medium include a flexible disk, a hard disk, a CD-ROM, an MO (magneto-optical disk), a DVD-ROM, and a memory card. It is also possible to transmit the computer program to a computer via a communication line and install it therein.

[0076] Here, since the vital sign detection sensor 1 is capable of capturing heart sounds as described above, upon receiving the obtained vital sign data, the frequency analysis means 110 performs frequency analysis on the vital sign data obtained by the vital sign detection sensor 1 via the body surface.

[0077] The boundary frequency identifying means 120 determines a boundary frequency (BF) between vibrations caused by apex beat and vibrations caused by heart sounds in the biosignal data from the frequency analysis result of the biosignal data obtained from the frequency analysis means 110. The boundary frequency identifying means 120 includes means for determining a sudden change part of the power spectrum that is the boundary between harmonic vibration and irregular vibration in the frequency analysis result obtained by the frequency analysis means 110, and identifying the boundary frequency based on this sudden change part.

[0078] According to this embodiment, by determining the boundary frequency, it becomes possible to obtain the waveform of the apex beat from the biological signal separately from the waveform of the heart sounds.

[0079] In addition, the biosignal analyzer 100 of this embodiment uses the boundary frequency itself as a judgment index. That is, as will be clear from the experiment described later, the boundary frequency is expressed by a quadratic function of the heart rate, and the boundary frequency can be known from the correlation data between the two based on the heart rate. Looking at the correlation between the boundary frequency and the heart rate, the heart rate of a heart in an efficient state is about 70 to 80 / min. This is a state in which the exercise load of the heart is small, and in this case the boundary frequency is the minimum. Therefore, the state of the heart at that time can be estimated from the boundary frequency at the time of measurement obtained by measuring the heart rate and using the correlation data.

[0080] As shown by imaginary lines in FIG. 12, the biosignal analysis device 100 of this embodiment preferably has a correlation data storage unit 150 in which correlation data between boundary frequency and heart rate is stored, and a measurement state estimation means 130 that determines a boundary frequency based on the heart rate of the subject in light of the correlation data stored in the correlation data storage unit 150, and estimates the boundary frequency of the subject at the time of measurement.

[0081] When estimating the state by the measurement state estimation means 130, information on the heart rate is required, and the heart rate can be calculated by the heart rate calculation means 140 shown by imaginary lines in FIG. 12. In this case, the biosignal detection sensor 1 and the heart rate calculation means 140 that processes the biosignal data to calculate the heart rate function together as a heart rate measurement unit. Note that the heart rate can be measured by electrocardiogram, phonocardiogram, photoplethysmography, sphygmomanometer, etc., and these measuring devices can also be used as the heart rate measurement unit. However, the biosignal detection sensor 1 of this embodiment is excellent in terms of ease of measurement, since it can measure without restraint just by contacting it with the surface of the human body.

[0082] The correlation data stored in the correlation data storage unit 150 consists of correlation data between the boundary frequency between the vibration caused by the apex beat and the vibration caused by the heart sound, which have been measured in advance, and the heart rate. This correlation data is constructed using a quadratic function that shows the correlation between the boundary frequency and the heart rate when the boundary frequency was obtained. Therefore, the measured heart rate can be found by comparing it with the correlation data stored in the storage unit of the correlation data storage unit 150.

[0083] The boundary frequency stored in the correlation data storage unit 150 is obtained in advance by analyzing a large number of pieces of biological signal data. The boundary frequency is obtained as the above-mentioned sudden change portion identified by the boundary frequency identification means 120.

[0084] The method for determining the areas of sudden change is to first perform frequency analysis on the biosignal data obtained by the biosignal detection sensor 1 via the body surface, and then add the frequency analysis results of the heart sound data measured at the same time to the frequency analysis results, and extract and determine the boundary frequency between the vibrations caused by the apex beat and the vibrations caused by the heart sounds in the biosignal data.

[0085] Specifically, the frequency analysis results of the biosignal data obtained by the frequency analysis means 110 are averaged and the resulting waveform is displayed on a double logarithmic axis using the frequency and power spectrum, and the frequency analysis results of the heart sound data are averaged and the resulting waveform is displayed on a double logarithmic axis using the frequency and power spectrum. A fluctuation change point is obtained from the logarithmic difference between the two waveforms displayed on the double logarithmic axis, and this fluctuation change point is regarded as the substantial vanishing point where the harmonic components of the apex beat become extremely small. The frequency of the vanishing point corresponds to the above-mentioned sudden change part, making it possible to obtain the boundary frequency.

[0086] Also, the frequency analysis means 110 may employ a Short Time Fourier Transform (STFT) technique. In this case, the boundary frequency identification means 120 determines the sudden change part of the power spectrum from the analysis result of the Short Time Fourier Transform. The Short Time Fourier Transform preferably outputs the analysis result as image data indicating the time, frequency, and degree of fluctuation of the power spectrum. This allows the boundary frequency identification means 120 to determine the sudden change part of the power spectrum from the image data of the Short Time Fourier Transform. A specific method for identifying the sudden change part, i.e., a method for determining the boundary frequency, will be described later.

[0087] Here, the biosignal detection sensor 1 is attached to the back, chest, waist, etc. of a human body to capture sounds and vibrations within the body, and the obtained biosignal is a collection of various biosounds and internal vibrations. On the other hand, the apex pulsation is a vibration hidden in the waveform of the heart sounds, and it is difficult to separate them. However, in this embodiment, the above-mentioned boundary frequency (BF) that distinguishes the waveform of the apex pulsation from the waveform of the heart sounds is found, making it possible to estimate the biological state related to the apex pulsation based on the heart rate.

[0088] A detailed description will be given below of a method for performing frequency analysis using the frequency analysis means 110 and identifying the boundary frequency using the boundary frequency identification means 120. In this embodiment, there are two methods: a method for using a waveform displayed on both logarithmic axes as described above, determining a fluctuation change point based on a frequency at which harmonic oscillations in the waveform substantially disappear, and treating this fluctuation change point as a sudden change part in the power spectrum; and a method for determining a sudden change part in the power spectrum from the analysis results of a short-time Fourier transform.

[0089] [Boundary frequency (BF) extraction method] (BF extraction method based on harmonic loss frequency of APW) As shown in Non-Patent Document 1, the heart sound is an irregular vibration, but the experimental results described below have revealed that the apex beat wave is mainly composed of harmonic vibrations consisting of fundamental and higher harmonics. Therefore, we focused on frequency analysis of the APW waveform obtained from a 4SR sensing system attached to the front of the chest, performing arithmetic averaging, and finding the change point of the power spectrum of the harmonic harmonic vibration and the irregular vibration, that is, the sudden change part of the power spectrum. The power spectrum of the harmonic components of the CAB (Cardiac Apex Beat) of the apex beat wave becomes smaller as the frequency increases. On the other hand, the power spectrum of the irregular vibration system of the CAS (Cardiac Acoustic Sound) of the heart sound does not depend on the frequency, and the magnitude of the power spectrum changes. Here, the frequency at which the power spectrum of the harmonic components of the CAB becomes smaller and the fluctuation behavior of the CAS changes is called the boundary frequency, and hereinafter this is called BF. In addition, when heart rate or blood pressure fluctuations occur, the APW waveform changes instantaneously. One of the factors that causes the instantaneous fluctuations in the APW waveform is thought to be the fluctuation of the dominant frequency of the PCG.

[0090] Due to individual differences between subjects, impedances also differ with respect to the transmission characteristics of the acoustic vibration of the trunk, and it may be difficult to find the BF. Therefore, the logarithmic difference method is applied to find the frequency at which the harmonic components of the apical pulsation wave disappear (the frequency at which the power spectrum of the harmonic components becomes very small and can be practically ignored). The arithmetic average processing of the APW is performed with a time window of 8.2 seconds and a Fourier transform with 90% overlap. The arithmetic average processing waveform is displayed on both logarithmic axes. The power spectrum waveform displayed on both logarithmic axes is because the additive theorem can be used. The PCG waveform is used for the heart sounds. By applying the logarithmic difference method, the absolute value of the power spectrum of the PCG in the 0.5 to 20 Hz frequency band is increased, and the power spectrum waveform can be made in opposite phase. This reduces the power spectrum of the apical pulsation harmonic components and the irregular vibration of the heart sounds, making it easier to find the BF.

[0091] (BF extraction method using short-time Fourier transform) F-APW, PCG, APW×PCG, APW×PCG -1 The short-time Fourier transform (STFT) of the power spectrum is applied to visualize the change points of the fluctuations. The formula for the short-time Fourier transform used in this analysis is:

[0092]

number

[0093] x(t) is the measured signal data, and W(t-τ) is the window function. To achieve a frequency resolution of 0.5 Hz or less, the number of points in the window function is 2. 12 = 4096. If the sampling frequency is 1000 Hz, then 4096 points equals 4.096 seconds, and 4.096 seconds equals 0.244 Hz. This allows the frequency resolution to be set to 0.244 Hz, which is less than 0.5 Hz. The window function movement width is set to 50 points. t indicates the measurement time, and τ indicates the time series data based on the window function movement time. By multiplying the measurement signal data by the window function, it is possible to create frequency information that changes over time. Note that frequency information is generated every 50 / 1000 = 0.05 seconds.

[0094] Next, using the BF determined based on the above two BF extraction methods, the apical beat waveform (Front CAB) and heart sounds (Front CAS) are plotted from the F-APW, and similarly, the Rear CAB, Rear CAS, Lumbar CAB, and Lumbar CAS are plotted from the R-APW and L-APW.In addition, since it was found in the preliminary experiment and preliminary study that HR is involved in BF, a correlation diagram between BF and HR is created using data from 50 subjects to examine the correlation between the two. The BF extraction method based on the frequency at which harmonic components disappear is a method for determining the BF from the frequency, while the BF extraction method using the short-time Fourier transform is a method for determining the BF using the power spectrum as a threshold. Specifically, the former is a method for determining the frequency at which the power spectrum height changes from decreasing to increasing as the BF by changing the frequency, while the latter is a method for determining the BF by setting 70% of the average value of the power spectrum group near the BF as the threshold. 19 to 21 show a comparison of ECG, F-APW, PCG, Front CAB, and Front CAS for four subjects with HR (heart rates): 58 / min, 71 / min, 79 / min, and 93 / min. In the case of a subject with a HR of 58 / min, the systolic wave measured by Front CAB showed a late systolic bulge in ACG, but the ECG and PCG (Front CAS) made dilated cardiomyopathy unlikely, and it was thought that the apical beat approached a sustained pattern due to the influence of gravity. In the case of a subject with a HR of 71 / min, the P wave was unclear and small and not visible on the ECG. The A wave (atrial upsurge wave) due to the left atrial contraction confirmed by the ACG was confirmed on the front CAB, and the front CAS also had a low-frequency wave earlier than the I sound of the PCG, so it was not an ectopic rhythm. In the subject case with a HR of 79 / min, the waveform measured by Front CAB exhibited a sustained pattern, but the waveforms of the A waves and Rapid Filling Wave (RFW) and Front CAS suggested that this was not a case of left ventricular hypertrophy or leakage from the left ventricle during systole (mitral valve regurgitation, etc.). In the case of a subject with a HR of 93 / min, the ECG showed low voltage and a high heart rate at rest, but the Front CAB had low and high frequency components, and the First sound was neither accentuated nor attenuated, eliminating the suspicion of underlying heart disease.

[0095] (Fluctuation characteristics of heart rate variability and correlation with heart rate and BF) The slope of the power spectrum displayed on both logarithmic axes is called the fluctuation coefficient here, and the point where the fluctuation coefficient changes sharply is hereafter called the fluctuation change point. At the fluctuation change point, a loss of complexity or a breakpoint occurs. The fluctuation coefficient is expressed by the power spectrum P(f) and frequency (f), and is log P(f) and log(f) on both logarithmic axes. If there is a linear relationship between log P(f) and log(f), then

[0096]

number

[0097] In order to evaluate the fluctuation characteristics of the heart rate fluctuation obtained from the ECG, experimental data measured by the APW measurement experimental protocol is used. The fluctuation characteristics are calculated using formula (5). The obtained value of n is a negative number (approximately -0.5 to -1.5), but according to Non-Patent Document 3, it is defined as the 1 / f fluctuation of the cardiac cycle, so it is expressed as an absolute value.

[0098] To evaluate the fluctuation of heart rate variability, data from 30 seconds of apnea, natural breathing, and apnea for a total of 90 seconds is used. The heart rate here is the average value for the 90 seconds.

[0099] [BF extraction results for each extraction method] (Results of BF extraction from harmonic loss frequency of F-APW) Figure 13 shows the F-APW×PCG -1 The following steps (1) to (10) correspond to the circled numbers in Fig. 13. The calculation steps and points of interest are explained below.

[0100] (1) Calculate the F-APW from the 4SR data without filtering and perform frequency analysis. Hereinafter, this is referred to as F-APW. (2) Perform averaging on the F-APW (time window of 8.2 seconds, 90% overlap). (3) Extract the waveform from the PCG and perform frequency analysis. Hereinafter, this is referred to as PCG. (4) Perform the same averaging process as in (2) (time window 8.2 seconds, 90% overlap). (5) Using the power spectra of F-APW and PCG obtained in (2) and (4), F-APW×PCG -1 Generate and find the breakpoint. (6) F-APW×PCG -1 From the breakpoints on the waveform, the frequency at which the harmonics of the fluctuating apical pulsation wave disappear and the frequency at which the irregular vibration components of the heart sounds that become white noise appear are identified. (7) F-APW×PCG -1 Draw a BF line from the upper harmonic vanishing point, and let BF be the intersection of the BF line and F-APW. (8) 0.5Hz to BF is the CAB band (hereinafter, CAB (0.5-BF)), and BF to 50Hz is the CAS band (hereinafter, CAS (BF-50)). (9) Apply a bandpass filter of 0.5 Hz to BF to the F-APW and confirm that the harmonic components of the CAB are reduced near BF. (10) Apply a bandpass filter of BF~50Hz to the F-APW and confirm that the power spectrum changes regardless of frequency near the BF.

[0101] Figure 14 shows the frequency analysis results of CAB and CAS, summarized by heart rate (HR) (58-93 / min) in a biaxial linear display. From this figure, it is possible to understand the disappearance of the harmonic components of CAB in BF, the appearance of irregular oscillations of CAS near BF, and the HR dependency of BF. It is clear that the method of the present invention can separate CAB and CAS and visualize their characteristics.

[0102] Next, Figures 15 to 18 show CAB and CAS waveforms traced by the BF extraction method based on the harmonic loss frequency of APW. The processed waveforms of Front CAB (0.5-BF), Front CAS (5-BF), Rear CAB (0.5-BF), Lumbar CAB (0.5-BF), and PCG, as well as Front CAS (BF-50), Rear CAS (BF-50), and Lumbar CAS (BF-50), extracted from F-APW, R-APW, and L-APW obtained from subjects with average heart rates of 58, 71, 79, and 93 / min, are shown. The tracing time is 1.5 seconds. The frequency band of the applied filter is shown in parentheses. The range of the vertical and horizontal axes of each CAB and CAS waveform is unified for each measurement site. It can be seen that the front CAB (0.5-BF) of the four subjects depicts apical pulsation waves dominated by the fundamental harmonic.

[0103] From the analysis of the results, it was found that the Front CAB (5-BF) was formed by a combination of multiple 5-10 Hz waveforms. On the other hand, the Rear CAB (0.5-BF) and Lumbar CAB (0.5-BF) were harmonic vibration waveforms that were dominated by waveforms with higher frequencies than the waveform shown in the Front CAB (0.5-BF), and were waveforms different from the Front CAB (0.5-BF). In addition, the heart sounds were irregular vibrations consisting of high-frequency waveforms that were a combination of multiple vibration components and multiple vibration waveforms.

[0104] (Results of BF extraction using short-time Fourier transform) 22 to 27 show the results of STFT analysis of power spectra by frequency analysis of F-APW and PCG for subjects whose average heart rates over a 10-second measurement period were 93 / min, 58 / min, 71 / min, and 79 / min. In the STFT diagram, the vertical axis represents frequency and the horizontal axis represents the STFT output time. The shade of color indicates the degree of fluctuation in the power spectrum, and the magnitude of the fluctuation coefficient expressed by equation (5) can also be expressed by changing the range of the shade of color. The magnitude of the fluctuation in the power spectrum is shown by the shade of red, green, and blue (RGB). In areas where there is little fluctuation in the power spectrum, areas where the fluctuation coefficient does not change suddenly, or small-amplitude irregular vibration, planar red, green, and blue appear, while conversely, in areas where there is large fluctuation in the power spectrum or areas where the fluctuation coefficient changes suddenly, red, green, and blue appear as lines.

[0105] The logarithmic difference (F-APW × PCG -1 ) are shown in Figures 23(a) and (b). Figure 24(b) shows the frequency analysis result, and the BF corresponding to the breakpoint is 15 Hz.

[0106] In Fig. 24(a), the logarithmic sum of APW and PCG (APW × PCG) was calculated to emphasize the harmonic components and irregular vibrations in the frequency band of 0.5 to 30 Hz. The STFT emphasizes both the apical pulsation wave and the irregular vibration of the heart sound at the boundary of BF. As a result, BF appears in the band of 14 to 17 Hz, which is dominated by blue and yellow.

[0107] APW×PCG -1 From these two linear tracing lines, the fluctuating behavior of the apical beat harmonics and the irregular vibration of the heart sounds were detected, and the frequency of disappearance of the apical beat harmonics, the frequency of appearance of the heart sounds, and the BF were identified.

[0108] Next, points of interest for considering each of FIGS. 22 to 27 will be described. 22 to 24 show the results of STFT analysis of a subject whose average heart rate during a 10-second measurement period was 93 / min. Figure 22(a) shows that the APW has components of 9th harmonic with 1.6Hz as the fundamental harmonic. The BF in this figure is the breakpoint at the sudden change in the power spectrum, so the BF exists between 14 and 17Hz, as shown in blue. Figure 22(b) shows that the PCG has vibration components with large changes in the power spectrum between 10 and 50 Hz. When viewed together with Figure 22(a), it can be seen that the PCG captures everything from the fifth harmonic to irregular vibrations. Figure 23(a) shows the APW×PCG -1 Since the BF breakpoint is a sudden change in the band with little fluctuation in the power spectrum, the BF exists between 14 and 17 Hz, as shown by Red, Blue, and Red. -1 In the enlarged image, a red line indicating a breakpoint appeared near 15 Hz. In the APW×PCG of Fig. 24(a), the fundamental harmonic of 1.49Hz is the blue line, the second harmonic is the green line, and the harmonics from the third harmonic to 15Hz are the red line, and it can be seen that the power spectrum of the harmonics is larger than that of the fundamental harmonic. There is also a breakpoint in the blue range shown by Red-Blue-Red near 15Hz, indicating that irregular vibrations occur from 15 to 20Hz where Red begins. Figure 24(b) shows the APW×PCG -1 , APW, PCG, and APW×PCG. -1 There was a breakpoint at 15Hz, and BF occurred at 15Hz.

[0109] 25 to 28 show the above-described APW×PCG -1 and the BF obtained by the BF extraction method using STFT. The subjects' heart rates were 58, 71, and 79 / min. Figure 25(a) shows a case in which the breakpoint appears in a mountain-shaped power spectrum, which is the opposite of the case in Figure 22(a), while in Figure 25(b) the BF appears as a linear Red in the band sandwiched between Red-Green-Red-Blue. Figure 26(a) shows an example in which the breakpoint shown in Figure 22(a) appears on the ridge of the mountain-shaped power spectrum, while in Figure 26(b) the BF appears as a linear Red sandwiched between Red-Blue-Red-Blue. Figures 27(a) and (b) show examples of changes in the fluctuation of the power spectrum. It is difficult to judge from the frequency analysis results in Figure 27(a), but it is possible to judge the BF using the STFT method in Figure 27(b). In Figure 27(b), the BF appears as a red line on the Red-Blue boundary.

[0110] Figures 28(a) and (b) show the correlation diagrams (BF-HR correlation diagrams) between BF and heart rate of 50 subjects, obtained by the BF extraction method from harmonic disappearance frequency and also confirmed by the BF extraction method using STFT. Figure 28(a) shows the BF frequency on the vertical axis and HR on the horizontal axis, and the relationship between the two is approximated by a quadratic function, as follows: y = 0.0173x 2 It is expressed as - 2.5847x + 107.6111, and takes the minimum value near 10Hz when HR is 75 / min. In this case, R2 = 0.8242. By using this chart, you can know BF without having to measure PCG. Moreover, BF was not correlated with age, SBP, DBP, or BMI. Furthermore, in a study of BF in a group of 20 young controls, a strong correlation was observed between BF obtained by STFT and BF determined by the BF-HR curve (R2=0.85). That is, since the microacoustic vibration (MAV) output from the trunk is collected using the biosignal detection sensor 1 (4SR) of this embodiment, by analyzing it with the biosignal analysis device 100, it is possible to identify BF and apical pulsation waves and classify waveforms (sine wave or triangular wave) even from subjects in the "group with thick chest walls," which was previously difficult to identify.

[0111] Figure 28(b) shows the correlation between the harmonic loss order and HR. If the order is plotted on the vertical axis and HR on the horizontal axis, and the relationship between the two is approximated by a quadratic function, the relationship is y = 0.0203x 2It is expressed as -3.2258x + 135.4587, and takes the minimum value near 10 Hz when HR is 79 / min. In this case, R2 = 0.9479. Note that Figures 28(a) and (b) are composed of data measured for 10 seconds in a resting state with RR fluctuations of 15% or less.

[0112] Therefore, by storing the correlation diagrams of Figures 28(a) and (b) and their quadratic functions in advance in the correlation data storage unit 150, the measurement state estimation means 130 can quickly determine the boundary frequency at the time of measurement from the heart rate, and can quickly determine and output the state of the person's heart and health at the time of measurement according to the boundary frequency.

[0113] (The relationship between heart rate fluctuation characteristics and heart rate and BF) The correlation between the fluctuation characteristics of heart rate variability obtained from ECG in the APW measurement experiment and the heart rate was examined for 50 subjects. As shown in Figure 29(a), the relationship between the two was approximated by a quadratic function, and when the HR was 72 / min, the fluctuation was 1 / f (|n|=1). On the other hand, when the HR was around 50 or 90 / min, the fluctuation was 1 / f 2 There was a fluctuation (|n|=2). FIG. 29(b) shows the correlation between the fluctuation characteristics of heart rate variability and BF. The relationship between the two is approximated by a linear function. When BF is in the range of 8 to 12 Hz, it becomes 1 / f fluctuation (|n|=1), and when BF is about 20 Hz or more, it becomes 1 / f fluctuation. 2 There was a fluctuation (|n|=2). Figures 29(c)-(e) show the experimental data for 90 seconds for the same subject, which was divided into 9 equal parts, and the BF fluctuations when heart rate fluctuations occurred during respiratory inhibition at rest were calculated and plotted on the regression curve of the BF-HR correlation diagram. For the three subjects, BF moved along the regression curve in accordance with heart rate fluctuations.

[0114] When the heart rate is 70-80 / min in a resting state, the fluctuation of the heart rate fluctuation is 1 / f. In light of this, when the BF is around 8-12 Hz, the fluctuation of the heart rate fluctuation is 1 / f. When the BF shows a value around this range, it can be estimated that the heart is moving efficiently. Therefore, by storing the correlation diagram of FIG. 29(b) in the correlation data storage unit 150, the measurement state estimation means 130 can quickly estimate the fluctuation characteristics of the heart rate fluctuation during measurement from the BF obtained by the boundary frequency identification means 120. At this time, if the fluctuation characteristics can be estimated as 1 / f, it can be estimated that the heart is moving well, that is, the heart is healthy in terms of its movement. The further the BF value deviates from the range of 8-12 Hz where the 1 / f fluctuation is observed, the greater the influence of aging, disease, etc. can be estimated.

[0115] Second embodiment Next, a second embodiment of the present invention will be described with reference to Fig. 30. In this embodiment, a biosignal analyzer 100 has an apex beat waveform extracting means 160 and a waveform classifying means 170 in addition to the configuration of the first embodiment described above.

[0116] The apex pulsation waveform extraction means 160 filters the biosignal data by setting the boundary frequency (BF) identified by the boundary frequency identification means 120 as the upper limit value, and obtains the waveform of the vibration caused by the apex pulsation. In this embodiment, filtering is performed using a band pass filter of 0.5 Hz to BF as described above. Note that the apex pulsation waveform (CAB) and the phonocardial waveform (CAS) are depicted in Figs. 15 to 18 of the first embodiment. The apex pulsation waveform in this case is obtained using a band pass filter of 0.5 Hz to BF by the same method as the apex pulsation waveform extraction means 160 of this embodiment. Also, the phonocardial waveform is obtained by applying a band pass filter of BF to 50 Hz.

[0117] The waveform classification means 170 classifies the waveform of vibrations caused by the apex beat (apex beat waveform) output by the apex beat waveform extraction means 160. In this embodiment, classification is performed by combining (A) a means for classifying the waveform by a mathematical approach using Fourier series expansion, and (B) a means for classifying the waveform from data obtained by an engineering approach. Although details will be described later, in this embodiment, the apex beat waveforms of healthy subjects are classified into five types by combining the two approaches. In addition, in the means for classifying waveforms from data obtained by an engineering approach, the waveforms are classified using at least one of the following four judgment factors. In order to improve the classification accuracy, it is preferable to classify using a combination of two or more judgment factors.

[0118] (1) Data showing the shape of the time waveform of the vibration generated by the apex beat in a predetermined time range (the diagrams (b) in each of Figures 32 to 36) (2) Graph data in which the horizontal and vertical axes represent the frequency and power spectrum obtained by the frequency analysis means of the time waveform (Figures 32 to 36 (c)) (3) Image data in a predetermined time range showing the degree of fluctuation in time, frequency and power spectrum, which are the analysis results of the short-time Fourier transform of the time waveform (Figures 32 to 36 (d)) (4) Data on the type and period of waveforms extracted by correlograms over a specified time range (Figures (e) in Figures 32 to 36)

[0119] First, the time waveform of a healthy subject is classified into five waveforms by a mathematical approach using Fourier series expansion, and as a proof of this, the amplitude, frequency, and time phase are analyzed by an engineering approach, and data corresponding to the five waveforms are obtained from the shape data of the time waveform of Front CAB (0.5-BF) (Figures 32 to 36 (b)), the graph data with the power spectrum on the horizontal and vertical axes (Figures 32 to 36 (c)), the diagram data of the analysis result of STFT (Figures 32 to 36 (d)), and the data extracted by the correlogram (Figures 32 to 36 (e)). The evaluation method of this waveform will be described later.

[0120] Information on the waveform classification results classified by the waveform classification means 170 is stored in the correlation data storage unit 150. Here, the information on the waveform classification results is stored in association with information on health status (presence or absence of illness such as heart disease, identification of illness, physical condition, etc.). This information is obtained by measuring the apex beat waveforms of as many people as possible and digitizing them in accordance with their health status at that time.

[0121] The measurement condition estimation means 130 classifies the waveform of vibration caused by the apex pulsation of the subject at the time of measurement, which is obtained by the apex pulsation waveform extraction means 160, using the waveform classification means 170, and accesses the correlation data storage unit 150 to compare the waveform classification result at the time of measurement with information of the waveform classification result stored in advance. As a result, the health condition corresponding to the waveform classification result at the time of measurement is output.

[0122] 31, a configuration may also be provided in which model creation means 180 is provided that uses waveform information of the apex pulsation waveform output by the apex pulsation waveform extraction means 160 and to be classified by the waveform classification means 170, and the health condition, as teacher data, to generate an estimation model that estimates the health condition from the waveform information by machine learning. Also, by performing machine learning while taking into account information on the classification result by the waveform classification means 170 (classification information), the accuracy of the estimation model is improved.

[0123] In this case, the measurement state estimation means 130 receives waveform information of vibrations caused by the apex pulsation of the subject at the time of measurement as input, and uses the estimation model created by the model creation means 180 to output the value of the health state at the time of measurement from the acquired waveform information at the time of measurement. By adopting such a configuration incorporating machine learning, the accuracy of estimating the health state from the waveform information is improved. Note that the feature amount of the waveform information of the learning object is the amplitude, frequency, time phase, etc. of each waveform shown in Figs. 32 to 36, but the image data as the analysis result of STFT shown in each (c) of Figs. 32 to 36 has a large amount of information, and it is easy to extract the feature amount of the learning object. Therefore, in order to grasp the waveform information, it is preferable to include the image data of STFT in the learning object. Also, the upper and lower diagrams of each (c) of Figs. 32 to 36 show the apex pulsation waveform of 0.5 Hz to BF divided into two, 1.95 Hz and 7.81 Hz, which further increases the feature amount of the learning object and contributes to improving the estimation accuracy. Other configurations of the second embodiment, including the biological signal detection sensor 1, are similar to those of the first embodiment.

[0124] The classification and evaluation methods of the waveforms will be explained in more detail below. [Evaluation method for time waveform of Front CAB] (Front CAB time waveform analysis) Next, the method for evaluating the Front CAB waveform will be described. The apex cardiogram (ACG) is known as one of the physical diagnostic tools that non-invasively evaluates the dynamics of the cardiovascular system and serves as an indicator of pathology and treatment, but the position for examination is not necessarily supine, and the patient may be in a sitting or left lateral position. Therefore, if there is information on the normal apex cardiogram pattern by measurement position, covering the supine, left lateral, and sitting positions, it would be possible to examine the relationship with waveform analysis and time phase analysis of PCG and ECG.

[0125] Therefore, the F-APW waveform measured by a microphone placed on the chest wall is frequency-analyzed, and the frequency band from 0.5 Hz to BF is extracted, i.e., Front CAB (0.5-BF), and compared with the clinically known apical pulse wave. The focus of the waveform analysis is on the tapping of the E wave, which is a short-lasting beat that can only be seen in early contraction by both visual inspection and palpation, and the sustained beat, which is a long-lasting and powerful beat. Here, the waveform analysis is performed using the shape of the Front CAB (0.5-BF) waveform and the time phase at which the ACG peak appears.

[0126] The amplitude of each time waveform in the Front CAB (0.5-BF) is evaluated during the systolic phase. The A wave and traces of the A wave resulting from the contraction of the left atrium are confirmed in the Front CAB (0.5-BF). The bottom value after the A wave is set as the C point of the ACG, and the total amplitude up to the peak value corresponding to the E wave of the ACG is measured and regarded as the amplitude of the systolic wave. The total amplitude from the C point of the ACG to the E wave is also calculated for the time waveforms of the Front CAB (5-BF), Rear CAB (0.5-BF), and Lumbar CAB (0.5-BF). When comparing the amplitude of time waveforms at different measurement sites, the Front CAB (0.5-BF) is used as the reference value. The ratio of each waveform is regarded as the amplitude ratio. The bottom value of the amplitude of the time waveform exists between the P wave and the R wave of the electrocardiogram. The peak value of the amplitude of the time waveform exists after the R wave and up to the start of the second sound of the PCG. Next, we classify the time waveforms of the Front CAB (0.5-BF) of 50 subjects.

[0127] (Harmonic component analysis using short-time Fourier transform) Next, the waveform gradient analysis method and the harmonic component analysis method using the short-time Fourier transform will be explained. Using equation (4), we investigated which parts of the time waveform the harmonic components are superimposed on, focusing on the harmonics that compose the time waveform of the Front CAB (0.5-BF). The frequency is divided into 1 to 4 Hz and 4 to 16 Hz for analysis. The number of window points for the 1 to 4 Hz waveform analysis is 2. 9= 512. With a sampling frequency of 1000 Hz, 512 points corresponds to 0.512 seconds, and in the calculations, a power spectrum component of 1.95 Hz appears. The shift width of the Hanning window function is set to 51 points. In this case, frequency information is generated every 51 / 1000 = 0.051 seconds.

[0128] The number of windows to capture E waves composed of harmonics is 2 7 =128. With a sampling frequency of 1000Hz, 128 points equals 0.128 seconds, and power spectrum components appear at 7.8Hz. The reason for setting the resolution at 0.12 seconds (7.8Hz) is that the duration of systole at a heart rate of 90 beats per minute is assumed to be 0.26 seconds, and the required resolution of the detection frequency to capture the peak of the systolic duration is set to 0.13 seconds. In this case, the movement width of the Hanning window function was set to 12 points. At that time, frequency information is generated every 12 / 1000=0.012 seconds.

[0129] (Distinguishing vibrations using the autocorrelation function) A correlogram is drawn using the autocorrelation function (ACF) to distinguish between waveforms. The vertical axis of the correlogram is ACF, and the horizontal axis is time. The ACF is calculated for each time interval T, and the type and period of the waveform are identified from the correlogram. X (τ) is

[0130]

number

[0131] The normalized autocorrelation function has a maximum value of 1 at τ = 0, and its value is the mean square value. When the ACF value is 0.2 or less, it is in the 95% or higher confidence interval for no autocorrelation. Vibration type analysis is determined based on 10 seconds of data.

[0132] [Front CAB time waveform evaluation results] Classification and testing were performed using Front CAB (0.5-BF) measured from 50 subjects for normal patterns in the sitting position. Figures 32 to 36 show the results. The waveforms of Front CAB (0.5-BF) are shown in each of (b) of Figures 32 to 36, and correspond to the time waveforms of ECG for 1 second shown in each of (a) of Figures 32 to 36. The waveforms of Front CAB (0.5-BF) are classified into five waveforms. Waveforms similar to ventricular systolic waves starting from the A wave (atrial contraction wave: arrowhead) and E wave (marked with *) of the apical cardiogram are observed. The classification method for the five waveform patterns is based on Fourier series expansion. However, mathematical expressions are not suitable for intuitive understanding. Therefore, when examining the Front CAB waveform, we classified the characteristics of the Front CAB waveform by using PSD (power spectral density) when focusing on the amplitude, STFT when focusing on the amplitude and phase, and Correlogram when examining whether it has periodicity. In this specification, the five classified waveforms are named pseudo-sine waves (S waves), pseudo-triangular waves (T waves (triangular waves), C waves (cosine waves), G waves (Gaussian waves)), and pseudo-full-wave rectified waves (R waves). Of these, S waves and G waves correspond to sustained waves, as seen in visual and palpation, based on the duration of the systolic wave, while T waves, C waves, and R waves correspond to tapping waves. Although these waveforms are not perfect sine waves, triangular waves, or full-wave rectified waves, the waveform shapes are similar, so the word "pseudo" is added to the prefix.

[0133] What is needed for waveform analysis is the fundamental harmonic waveform and the amplitude, frequency, and phase of each waveform in the vicinity of the bottom and peak.

[0134] In each of (c) of Figures 32 to 36, the power spectrum density (PSD) was obtained by frequency analysis and the relationship between amplitude and frequency was classified, and in each of (e) of Figures 32 to 36, the type of vibration was classified by the correlogram. Using these parameters, the time waveform of the Front CAB (0.5-BF) was classified into five representative waveforms.

[0135] More specifically, PSD allows classification of waveform patterns into the following types: First, the systolic E wave shown by the S wave of the Front CAB has a small contribution of high frequency components and is close to a sine wave. In other words, only the fundamental frequency component is dominant (the "X" pattern shown in the figure). Next, the systolic E wave shown by the Front CAB T, C, and G waves is in the process of converging into a triangular wave, and can be classified as a triangular wave. In other words, odd-order frequency components are dominant, and their amplitude decreases in proportion to the inverse of the square of the order as the order increases (the "Y" pattern shown in the figure). The R wave of the Front CAB has a small amplitude of the fundamental frequency component and a small difference with the amplitude of the integral multiple frequency component, so the triangular wave part is replaced by a full-wave rectified sine wave. The corresponding PSD has even-order frequency components that are dominant, and the amplitude becomes smaller as the order increases (the "Z" pattern shown in the figure). STFT is suitable for investigating the influence of phase information, and the accuracy of waveform classification is improved by superimposing PSD and STFT. By examining the periodicity between the A and E waves using Correlogram, any waveform can be classified as a variant of the normal waveform in terms of amplitude, phase, and periodicity.

[0136] The STFT diagrams in (d) of each of Figures 32 to 36 are obtained by dividing the power spectrum information from the fundamental harmonic to 5 Hz appearing in (c) of each of Figures 32 to 36 into two frequencies with center frequencies of 1.95 Hz and 7.81 Hz, doubling the amount of information on the power spectrum and making pattern matching easier (the upper diagram in (d) is 1.95 Hz, and the lower diagram is 7.8 Hz). The fundamental harmonic and harmonics between 1 and 5 Hz information in (c) of each of Figures 32 to 36 are plotted between 1 and 5 Hz in the STFT. The time waveform information is shown as red, green, and blue contour lines.

[0137] Waveform I (S wave) has three time periods where the fundamental harmonic appears, and two time periods where 5-10 Hz components appear. The interval between the atrial bump and the systolic wave is short, indicating that it is a square wave. Waveform II (T wave) has STFTs that indicate the appearance of components below 5 Hz concentrated at 2.3-2.5 seconds, and the 5-10 Hz components concentrated at 2.5-2.7 seconds, indicating that these components are approximately contained in the systolic phase, indicating that it is a triangular wave. Similarly, waveform III (C wave) also falls within the systolic phase, and the interval between the atrial bump and the systolic wave is short, indicating that it is a triangular wave. Waveform IV (G wave) has a peak value interval of 5-10 Hz close to that of waveform I (S wave), and has a shape that resembles a combination of S waves and T waves, and overall shows the characteristics of a triangular wave. Waveform V (R wave) had STFT peak values ​​below 5 Hz that showed a downward sloping contour line, indicating that the power spectrum from the fundamental harmonic to the higher harmonics was the same magnitude. Furthermore, there were three or more peak values ​​between 5 Hz and 10 Hz, and the time phase of their appearance was the same as that of the components below 5 Hz, showing the characteristics of a full-wave rectified wave.

[0138] The type of vibration (periodicity between A and E waves) was classified by the appearance of sine wave notches, with the autocorrelation coefficient set at 0.5 or more and 0.2 or less as the standard value, based on the correlograms in Figures 32 to 36 (e). From the correlogram waveforms of the Front CAB (0.5-BF), i, which is a sine wave, ii, which is a sine wave with one notch, and v, which is a sine wave with two or more notches, were extracted. The remaining waveforms were divided into iii, which has a single attenuation envelope, and iv, which has a double attenuation envelope.

[0139] [Theoretical basis and objective classification criteria for waveform classification] Here, we will explain in detail the mathematical approach to the apex wave using Fourier series expansion. These mathematical approaches show that the apex wave of a healthy person can be classified into five types, and the above PSD, STFT, and Correlogram support this classification into five types with an engineering approach. First, if any periodic function is f(t), f(t) can be expanded into the following Fourier series. Here, ω = 2π / T, where T is the period.

[0140]

number

[0141] Since the T wave is close to a triangular wave, it can be thought of as being able to be expanded into a Fourier series as follows:

[0142]

number

[0143] The waveform of the C wave resulting from ventricular contraction can be considered in the same way as the T wave. The difference between the C wave and the T wave is that in the Fourier series expansion of equation (8), the C wave has a higher dominance of the first-order component than the T wave. The results of the STFT analysis show that the waveform resulting from ventricular contraction is dominated by the fundamental frequency and the second harmonic, with frequency components up to the seventh harmonic. The correlogram matches the findings of tapping, in which the duration of the early contraction is short, similar to the T wave.

[0144] From STFT analysis, G waves can be considered as a combination of S waves and T waves, with the first-order component being dominant. Because they are closer to S waves than T waves, the duration of the systole is longer, which is consistent with the findings of Sustained.

[0145] The waveform of the R wave resulting from ventricular contraction is not unimodal but multimodal, but if we hypothesize that it is similar to a full-wave rectified waveform, it can be expanded into the following Fourier series.

[0146]

number

[0147] Using each of (b) to (d) in Figures 32 to 36, 50 cases were pattern matched to examine the waveforms. As a result, the occurrence rates of pseudo-sine waves (S waves), triangular waves (T waves), cosine waves (C waves), Gaussian waves (G waves), and pseudo-full-wave rectified waves (R waves) were 4 / 50=8%, 8 / 50=16%, 15 / 50=30%, 11 / 50=22%, and 12 / 50=24%, respectively.

[0148] Figure 37 shows the results of a waveform study using the power spectrum, two types of STFT, and correlogram shown in each of Figures 32 to 36 (b) to (e), without using the waveform testing determined in each of Figures 32 to 36 (a). The bold numbers in the figure indicate values ​​for which waveform testing was possible. With STFT and correlogram, waveform testing was possible in 43 of the 50 cases, with the exception of one triangular wave. For power spectra, waveform testing was possible in 38 of the 50 cases. The P value for the STFT test was P = 2.2 x 10 -16 , P = 7.9 × 10 by power spectrum test -5 , Correlogram test P = 2.2 × 10 -16According to the Wiener-Khintchine theorem, the STFT and the Correlogram (ACF) are related by the inverse Fourier transform, so the fact that the P values ​​of the two were the same supports the validity of the analysis method. Therefore, by combining these three tests, it is possible to improve the accuracy of the Front CAB (0.5-BF) waveform test, and it was found that the evaluation that was previously performed based on the shape of the ACG waveform can be classified with high accuracy. It was found that the 4SR sensing system has the waveform resolution necessary to find abnormal findings such as distinguishing between normal hearts and hypertrophied / enlarged hearts.

[0149] Therefore, in the present embodiment, if the classification result of the apical pulsation wave of the subject obtained by the waveform classification means 170 corresponds to the five patterns of the waveform classification of healthy subjects described above, the measurement time condition estimation means 130 judges the subject to be healthy and outputs the result, and if it does not fit any of these, it judges the subject to have an abnormal finding related to cardiac disease and outputs the result.

[0150] Figures 38(a)-(e) show the frequency analysis results of Front CAB(0.5-BF), Front CAB(5-BF), Rear CAB(0.5-BF), and Lumbar CAB(0.5-BF) of five subjects as representative examples of the five waveforms defined in each of Figures 32-36(a). Figure 39 shows the average value and standard deviation of the amplitude ratio of CAB by measurement site and the frequency analysis results. The amplitude ratio is an index that indicates the ease of measurement based on the difference in the amplitude of CAB by measurement site relative to Front CAB(0.5-BF), and is calculated using the maximum amplitude value of the ventricular contraction wave from the start point of the left ventricular pressure rise of the systolic wave shown in the above section (Front CAB time waveform analysis). The amplitude ratio of Front CAB(5-BF) is calculated from the ratio of Front CAB(5-BF) to Front CAB(0.5-BF). Similarly, the amplitude ratio of the Rear CAB(0.5-BF) was calculated from the ratio of the Rear CAB(0.5-BF) to the Front CAB(0.5-BF), and the amplitude ratio of the Lumbar CAB(0.5-BF) was calculated from the ratio of the Lumbar CAB(0.5-BF) to the Front CAB(0.5-BF). The amplitude ratio of the Front CAB(5-BF) to the Front CAB(0.5-BF) was 0.2 because it does not contain frequency components between 0.5Hz and 5Hz. The amplitude ratio of the Rear CAB(0.5-BF) was 1.1, and the amplitude ratio of the Lumbar CAB(0.5-BF) was 2.6. The average and standard deviation were calculated using the amplitude ratios of 50 subjects.

[0151] Next, we will consider the power spectra in Figures 38(a) to (e) and the amplitude in Figure 39. In the Rear CAB (0.5-BF) and Lumbar CAB (0.5-BF), the power spectrum near 5 Hz increased. Specifically, the Rear CAB (0.5-BF) was 1.8 to 4.8 times larger near 5 Hz, and the Lumbar CAB (0.5-BF) was 3.7 to 7.9 times larger near 5 Hz. The difference in the power spectrum of the harmonics near 5 Hz was due to the length of the aorta, suggesting that a liquid column resonance phenomenon occurred due to the aorta and pressure pulsation.

[0152] As described above, according to the present invention, the following specific effects were obtained. The conventional 3SR is a sensing system developed for mounting on vehicles, which attenuates external vibrations by the air damping effect of the air pack, and extracts the periodic components of the first and second heart sounds and heart rate fluctuations, which contain frequency components of 20 Hz or higher. On the other hand, the 4SR of the present invention was developed for indoor use, and was found to be suitable as a sensor for capturing the period and amplitude of the apex beat, which is in the vicinity of 1 to 2 Hz.

[0153] The F-APW produced a waveform similar to the apex pulse wave. The waveforms of the apex pulse wave and heart sounds could be generated from the F-APW. It is believed that the R-APW and L-APW produced different waveforms due to a resonance phenomenon caused by pressure pulsation. However, even for the R-APW and L-APW, which produced waveforms different from the F-APW, vibration information (CAB) related to cardiac motion, including the apex pulse, and heart sounds (CAS) could be extracted by applying vibration analysis, demonstrating the possibility that this could be of clinical significance.

[0154] By combining the BF extraction method from the harmonic disappearance frequency of F-APW and the BF extraction method using the STFT method, a correlation diagram of BF-heart rate was created, and from the actual measurement results, BF could be expressed as a quadratic function of heart rate, and it became possible to determine BF in real time from the heart rate using only F-APW data without using PCG. It was also suggested that the visualization method using the STFT method could be used not only to analyze the vibration of the heart and the pressure pulsation of the aorta, but also to diagnose the degree of change.

[0155] The results of the heart rate fluctuation experiment showed that when the heart rate is 70-80 / min at rest, the fluctuation is 1 / f, suggesting that efficient exercise is being performed. The results of the heart rate fluctuation experiment showed that when the BF is around 10Hz, the fluctuation is 1 / f, suggesting that efficient exercise is being performed. By utilizing this characteristic, it may be possible to quantify the changes that drugs have on cardiovascular function.

[0156] As described above, BF is determined from the heart rate (for 10 seconds in this case). The value of the heart rate variability (for 90 seconds) obtained from the BF tended to match the value of the heart rate variability (for 90 seconds) obtained directly from the heart rate (for 90 seconds). The results obtained by substituting these relational expressions support the validity of BF.

[0157] Furthermore, by applying bandpass filtering to the APW in the range from 0.5 Hz to BF, an ACG-approximating waveform could be drawn, and the Front CAB (0.5-BF), which represents the apical beat, could be broadly classified into five waveforms using frequency analysis, time series analysis using the STFT method, and waveform analysis using Correlogram.

[0158] When heart rate fluctuations occur during respiratory inhibition at rest, BF moves on the regression curve of the BF-HR correlation diagram (see Figure 40). This suggests the possibility that cardiac motion analysis, pressure pulsation, cardiac motion, and intraventricular blood flow can be evaluated from the vibration analysis method and BF-HR correlation diagram described above.

[0159] In addition, the same BF-HR correlation diagram was obtained in the supine and left and right lateral positions as in the sitting position (see Figure 41). Therefore, it was suggested that the BF-HR correlation diagram could be estimated with a single diagram regardless of posture, the measurement method using 4SR is highly practical because data can be easily obtained, and it can be applied to objectify visual and palpation findings, and the APW has the potential to be a physical diagnostic tool that non-invasively evaluates the dynamics of the cardiovascular system and serves as an indicator of pathology and treatment.

[0160] According to the present invention, signals spanning two frequency bands are separated by identifying the BF. In other words, by separating the signals corresponding to the apex beat and heart sounds from the APW, it becomes possible to quantitatively recognize the results of different types of cardiac activity by observing one physiological signal, and useful complementary information on the cardiac state can be obtained. In addition, by increasing the waveform resolution through signal separation, for example, the frequency at the boundary between two frequency bands and the harmonic components of the Front CAB and the dominant frequency components of the Front CAS in the vicinity of the frequency band may become new parameters of the cardiocirculatory system. In addition, Front CAB has pressure information of the systolic wave of the apexcardiogram, and Front CAS has time-phase information regarding the cardiac cycle. By combining the pressure and time-phase information, it is believed that the complementary information can be used for diagnostic purposes.

[0161] For example, the first heart sound can be identified from the Front CAS, and the notch corresponding to the first sound in the ascending limb of the systolic wave can be observed in a wide or narrow range in the STFT shown in (b) and (c) of Figures 32 to 36. The observable range can be changed by changing the window width of the 0 to 20 Hz band, and the notch can be easily identified by adjusting the contrast. This can be used to determine the presence or absence of a notch in the systolic pressure wave and to identify the cause of abnormal cardiac activity, and can be used for diagnosis. Front CAB, which is vibration information of the heart, and Front CAS, which is acoustic information of the heart, mainly indicate the mechanical properties of the atrium, ventricle, valves, blood, and aorta. The result of their complementarity is expressed as fluctuations in blood pressure, heart rate, and respiratory rate. It can also be used to quantify changes that occur during drug therapy while a patient is being administered medication. For example, in diagnosing left ventricular failure, comparing the mechanical properties of the left ventricle that should be watched out for with those of healthy people of the same age group can also be useful in diagnosing the presence of left ventricular failure. Furthermore, by correlating with ECG information such as conduction pathways, velocity characteristics, and potentials, it may be possible to estimate the health of myocardial cells. Front CAB and Front CAS information may provide data relevant for cardiac status monitoring and functional evaluation. As a result, it may be possible to model the ventricular size, the thickness of the heart walls, and the mechanical properties of the heart, and to describe the motion mathematically. Furthermore, by measuring data along the aorta, it may be possible to separate aortic hemodynamics from that resulting from the apical pulsation or heart sounds, which may provide new insight into aortic hemodynamics.

[0162] As is clear from the above, by extracting acoustic vibration information (Acoustic Pulse Wave: APW) from 0.5 to 80 Hz and applying a specified filter processing, it is possible to generate an ACG equivalent waveform (Cardiac Apex Beat: CAB) and a heart sound equivalent waveform (Cardiac Acoustic Sound: CAS). In addition, CAB and CAS have different sources and transmission pathways, and the superposition of these two vibrations forms the APW. If contractility is considered to be an inherent property of the myocardium and independent of preload and afterload, the CAB waveform during systole contains information about the influence of chemical stimuli and hormones during contraction. If CAS is considered to contain information about the movement of blood ejected from the ventricle during systole, the APW contains information about preload, afterload, and contractility. Therefore, APW, CAB, and CAS contain a lot of information about sound, vibration, and pressure, and by observing them together with the polarization phenomenon of ECG, it is possible to diagnose cardiac malfunctions. Real-time measurement of APW, CAB, and CAS adds clinically significant sound, vibration, and pressure information, and it was found that the engineering approach used for machine failure diagnosis can be applied to health checkups. Furthermore, the 4SR system of the present invention that measures APW can become a physical diagnostic tool that can obtain bioinformation non-invasively and repeatedly over a long period of time, and is expected to have a wide range of applications, including analysis using AI including the above-mentioned machine learning. [Explanation of symbols]

[0163] 1. Biosignal detection sensor (4SR) 10 Three-dimensional knitting 20 Containing Film 30 Microphones 40 cases 50 gel 100 Biosignal Analysis Device 110 Frequency analysis means 120 Boundary frequency identification means 130 Measurement state estimation means 140 Heart rate calculation means 150 Correlation data storage unit 160 Apical pulse waveform extraction means 170 Waveform classification means 180 Model Creation Methods 1000 Biosignal Detection Sensor (3SR)

Claims

1. a frequency analysis means for performing frequency analysis on biosignal data obtained by a biosignal detection sensor via a body surface; a boundary frequency identifying means for determining a boundary frequency between a vibration caused by an apex beat and a vibration caused by a heart sound in the biosignal data from a frequency analysis result of the biosignal data obtained from the frequency analysis means; having The boundary frequency identification means The biosignal analysis device includes a means for determining a sudden change portion of the power spectrum that is a boundary between harmonic vibration and irregular vibration in the frequency analysis result, and for specifying the boundary frequency based on this sudden change portion.

2. The boundary frequency identification means 2. The biosignal analyzer according to claim 1, further comprising means for determining an abrupt change in said power spectrum by adding a result of frequency analysis of simultaneously measured heart sound data to the result of said frequency analysis.

3. The boundary frequency identification means a double logarithmic axis display means for displaying a waveform obtained by averaging the results of frequency analysis of the biosignal data and the heart sound data on a double logarithmic axis using a logarithmic difference method; and a sudden change portion identification means for determining a fluctuation change point from the waveform displayed on the double logarithmic axis and identifying the fluctuation change point as a sudden change portion of the power spectrum.

3. The biosignal analysis device according to claim 2, further comprising:

4. A short-time Fourier transform is applied as the frequency analysis means, 3. The biosignal analyzer according to claim 1, wherein the boundary frequency identifying means includes means for determining an abrupt change portion of the power spectrum from the analysis result of the short-time Fourier transform.

5. the frequency analysis means includes means for outputting an analysis result of the short-time Fourier transform into image data indicating the degree of fluctuation in time, frequency, and power spectrum; 5. The biosignal analyzing apparatus according to claim 4, wherein said boundary frequency identifying means includes means for determining an abrupt change portion of said power spectrum from said image data.

6. a correlation data storage unit in which correlation data relating to the boundary frequency is stored; a measurement time state estimation means for estimating the health state of the measurement subject at the time of measurement by collating the correlation data; The biosignal analyzer according to any one of claims 1 to 5, further comprising:

7. the correlation data is correlation data between the boundary frequency and a heart rate, The biosignal analysis device according to claim 6, wherein the measurement state estimation means includes means for estimating the boundary frequency of the subject during measurement by comparing the subject's heart rate during measurement with correlation data between the boundary frequency and the heart rate.

8. the correlation data is correlation data between the boundary frequency and a fluctuation characteristic of heart rate variability, The biosignal analysis device of claim 6, wherein the measurement state estimation means includes means for estimating the fluctuation characteristics of the heart rate fluctuation of the subject during measurement by comparing the boundary frequency of the subject during measurement with correlation data between the boundary frequency and the fluctuation characteristics of the heart rate fluctuation.

9. an apex pulsation waveform extraction means for filtering the biological signal data by setting the boundary frequency identified by the boundary frequency identification means as an upper limit value, and obtaining a waveform of vibration caused by the apex pulsation; A waveform classification means for classifying the waveform of the vibration generated by the apex beat of the heart; With 7. The biosignal analysis device of claim 6, wherein the measurement condition estimation means includes means for classifying, by the waveform classification means, the waveform of vibration generated by the apex pulsation of the subject at the time of measurement, which is obtained by the apex pulsation waveform extraction means, and estimating the health condition of the subject at the time of measurement based on data of the waveform classification result at the time of measurement.

10. The correlation data storage unit stores correlation data between the waveform classification result and a health condition in advance, 10. The biosignal analysis device of claim 9, wherein the measurement condition estimation means includes means for classifying, by the waveform classification means, the waveform of vibration generated by the apex beat of the subject at the time of measurement, which is obtained by the apex beat waveform extraction means, and collating the waveform classification result at the time of measurement with pre-stored correlation data between the waveform classification result and health condition, to estimate the health condition of the subject at the time of measurement.

11. a model creation means for generating an estimation model by machine learning to estimate the health condition from the waveform information by using the waveform information output by the apex beat waveform extraction means and the health condition as teacher data, The biosignal analysis device of claim 9, wherein the measurement condition estimation means includes means for receiving as input waveform information of vibrations caused by the apex beat of the heart of the subject at the time of measurement, using an estimation model created by the model creation means, and outputting a value of the health condition at the time of measurement from the acquired waveform information at the time of measurement.

12. The biological signal detection sensor includes: Three-dimensional knitting and A containing film that hermetically covers the periphery of the three-dimensional knitted fabric; A microphone disposed on the outer side of the containing film; a case for covering the microphone; and a disturbance intrusion suppression member for suppressing disturbances from entering the microphone within the case.

12. The biosignal analyzer according to claim 1, comprising:

13. A computer program for processing biosignal data obtained by a biosignal detection sensor via a body surface and causing a computer to function as a biosignal analysis device, A step of subjecting the biological signal data to frequency analysis; a step of identifying a boundary frequency between vibrations caused by apex pulsation and vibrations caused by heart sounds in the biosignal data from the frequency analysis results; causing the computer to execute In the step of identifying the boundary frequency, A computer program for determining a sudden change in the power spectrum that is the boundary between harmonic vibration and irregular vibration from the frequency analysis results, and for specifying the boundary frequency based on this sudden change.

14. In the step of identifying the boundary frequency, 14. The computer program according to claim 13, further comprising: a step of calculating a sudden change in the power spectrum by adding a result of frequency analysis of simultaneously measured heart sound data to the result of the frequency analysis.

15. In the step of identifying the boundary frequency, 15. The computer program according to claim 14, further comprising: displaying waveforms obtained by averaging the results of frequency analysis of the biosignal data and the heart sound data on a double logarithmic axis using a logarithmic difference method; determining a fluctuation change point from the waveform displayed on the double logarithmic axis; and identifying the fluctuation change point as a sudden change portion of the power spectrum.

16. In the step of performing the frequency analysis, a short-time Fourier transform is applied, 15. The computer program according to claim 13, wherein in the step of identifying the boundary frequency, abrupt changes in the power spectrum are obtained from the analysis results of the short-time Fourier transform.

17. In the step of performing the frequency analysis, the analysis result of the short-time Fourier transform is outputted as image data indicating the degree of fluctuation in time, frequency and power spectrum; 17. The computer program according to claim 16, wherein in the step of identifying the boundary frequency, a sudden change portion of the power spectrum is obtained from the image data.

18. A computer program according to any one of claims 13 to 17, which executes a procedure of estimating the health condition of the subject at the time of measurement by comparing the correlation data related to the boundary frequency stored in a correlation data storage unit.

19. the correlation data is correlation data between the boundary frequency and a heart rate, The computer program according to claim 18, wherein the step of estimating the health condition at the time of measurement includes estimating the boundary frequency of the subject at the time of measurement by comparing the heart rate of the subject at the time of measurement with correlation data between the boundary frequency and the heart rate.

20. the correlation data is correlation data between the boundary frequency and a fluctuation characteristic of heart rate variability, The computer program of claim 18, wherein the step of estimating the health condition at the time of measurement includes collating the boundary frequency at the time of measurement of the subject with correlation data between the boundary frequency and the fluctuation characteristics of heart rate fluctuation to estimate the fluctuation characteristics of heart rate fluctuation of the subject at the time of measurement.

21. Further, a step of filtering the biological signal data by setting the boundary frequency as an upper limit value, and extracting an apex pulsation waveform for obtaining a waveform of vibration caused by the apex pulsation; classifying the waveform of the vibration caused by the apex beat; Run the following on your computer:

20. The computer program of claim 18, wherein the step of estimating the health condition at the time of measurement includes classifying a waveform of vibration generated by the apex beat of the subject at the time of measurement by executing a step of classifying the waveform, and estimating the health condition of the subject at the time of measurement based on the waveform classification result.

22. the correlation data storage unit stores correlation data between the waveform classification result and a health condition, 22. The computer program of claim 21, wherein the step of estimating the health condition at the time of measurement includes classifying a waveform of vibration caused by the apex beat of the subject at the time of measurement by executing a step of classifying the waveform of vibration caused by the apex beat of the subject, and estimating the health condition of the subject at the time of measurement by comparing the waveform classification result at the time of measurement with correlation data between the waveform classification result and the health condition.

23. generating an estimation model by machine learning that estimates the health condition from the waveform information, using waveform information that is output by executing the procedure of extracting the apex beat waveform and is to be classified in the procedure of classifying the waveform and the health condition as teacher data; The computer program of claim 21, wherein the step of estimating the health condition at the time of measurement uses waveform information of vibrations generated by the apex beat of the subject at the time of measurement as input, uses the estimation model, and outputs a value of the health condition at the time of measurement from the acquired waveform information at the time of measurement.

24. A recording medium on which the computer program according to any one of claims 13 to 23 is recorded.

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