Heart valve abnormality detection device, computer program, and method for operating the heart valve abnormality detection device.

Abstract

[Problem] To detect, in a simple, highly precise manner, a cardiac-valve abnormality, in particular, serious aortic valve stenosis, in the form of a clear result. [Solution] Provided is a cardiac-valve abnormality detection device having: a cardiac-sound-data acquisition unit 21 that acquires cardiac sound data corresponding to cardiac sounds; a first processing unit that acquires, on the basis of the cardiac sound data, a peak frequency which is a peak value of a frequency in a frequency component of the cardiac sounds in a target range, the target range being between a first sound and a second sound of the cardiac sounds; and a second processing unit that outputs a detection signal indicating a high likelihood of serious aortic valve stenosis when the peak frequency is equal to or greater than a peak reference value.

Description

Technical Field

[0005] , ,

[0006]

[0001] The present invention relates to a cardiac valve abnormality detection device, a detection method, and a computer program for detecting abnormalities of cardiac valves such as aortic valve stenosis.

Background Art

[0002] With the recent aging of the population, the number of patients with valvular heart disease, particularly severe aortic stenosis (AS), has been increasing. Aortic stenosis is a heart disease in which calcification occurs in the aortic valve with aging, the valve hardens and becomes difficult to open, and the amount of blood pumped into the aorta is significantly reduced. Since it is a serious disease that can lead to sudden death or heart failure, overlooking it is a major medical problem. It is said that 3.4% of people aged 75 or older suffer from severe AS, and it is also said that there are 560,000 people in Japan.

[0003] Now, auscultation widely performed in clinical settings is useful for screening valvular heart disease, but it is said that the diagnostic accuracy of valvular heart disease is as low as about 40 to 50% for non-cardiologists with little experience in auscultation. The reasons for the low diagnostic accuracy of auscultation include that the timing, location, and frequency range of heart murmurs vary depending on the type and condition of valvular heart disease, and it is difficult to comprehensively judge such information.

[0004] Conventionally, techniques for detecting abnormalities of cardiac valves by analyzing heart sound information obtained from a stethoscope or the like have been proposed.

[0005] Patent Document 1 discloses extracting an S1 sound signal and an S2 sound signal from heart sound data based on the heart sound of a living body, and diagnosing heart disease based on comparison of the amplitude and power in a specific frequency band of the S1 sound signal and the S2 sound signal with a reference value.

[0006] Patent Document 2 discloses a method for detecting heart murmurs from collected heart sounds, generating an analysis signal using the heart murmur interval, and classifying cases of heart murmurs based on a comparison between a first frequency component, which is a predetermined frequency component of the analysis signal, and a second frequency component, which is a part of the first frequency component. Paragraph 0026 of Patent Document 2 describes determining mitral valve regurgitation or aortic valve stenosis based on the frequency band power ratio.

[0007] Patent Document 3 discloses a method for diagnosing the presence or absence of heart disease based on the shape of the power spectrum in a certain frequency band of systolic heart sound data.

[0008] Furthermore, it has been proposed to use techniques such as deep learning to analyze sound sources acquired with a digital stethoscope in order to detect various heart diseases in order to detect heart murmurs (Non-Patent Literature 1). [Prior art documents] [Patent Documents]

[0009] [Patent Document 1] Japanese Patent Publication No. 2017-12366 [Patent Document 2] Japanese Patent Publication No. 2014-87543 [Patent Document 3] Japanese Patent Publication No. 2015-188512 [Non-patent literature]

[0010] [Non-Patent Document 1] W.Reid Thompson, et al. Artificial Intelligence-Assisted Aus cultation of Heart Murmurs: Validation by Virtual Clinical Trial. Pediatric Cardiology (2019) 40:623-629) [Overview of the Initiative] [Problems that the invention aims to solve]

[0011] However, conventionally, it has not been possible to diagnose heart valve abnormalities, especially severe AS (aortic stenosis), with high accuracy using simple procedures.

[0012] For example, in the case of Patent Document 1 mentioned above, heart disease is diagnosed by comparing the amplitude and power of specific frequency bands of the first and second sound signals with reference values. This results in a large number of detection parameters used for comparison, increasing the computational burden and making it easy for errors to accumulate. The same applies to Patent Document 2, and moreover, it can only classify cases. Furthermore, in the case of Patent Document 3, the diagnosis is made by comparing the shape of the power spectrum, which leaves ambiguity in the judgment and makes it difficult to obtain high accuracy, and at best it can only determine the presence or absence of heart disease.

[0013] Valvular heart disease can progress without clear subjective symptoms, and if it is a severe case of spondylolisthesis (AS), the only treatment option is surgery, so early detection through accurate diagnosis is crucial. Large hospitals and other specialized facilities perform highly accurate severity assessments of valvular heart disease using echocardiography, but this requires equipment and specialists, making it difficult for small clinics to perform from a cost-effectiveness standpoint.

[0014] Given that screening for the necessity of echocardiography is not adequately performed by auscultation alone, there is a need for a simple detection device that is accurate, reliable, and provides clear results.

[0015] This invention has been made in view of these points, and aims to detect abnormalities of the heart valve, particularly severe aortic stenosis, in a simple and highly accurate manner with clear results. [Means for solving the problem]

[0016] The first aspect of the heart valve abnormality detection device of the present invention includes a heart sound data acquisition unit that acquires heart sound data corresponding to heart sounds, and based on the heart sound data, a target range is set between the first heart sound and the second heart sound, and a first processing unit that acquires a peak frequency, which is the peak value of the frequency in the frequency components of the heart sound within the target range. The system includes an ejection time acquisition unit that acquires ejection time based on the aforementioned heart sound data, and a storage unit that stores a first frequency which is a peak reference value for determining severe AS, a second frequency which is lower than the first frequency, and the ejection reference value, respectively. When the peak frequency is Severe AS is defined as the frequency being equal to or greater than the first frequency and the ejection time being equal to or greater than the ejection threshold value. a second processing unit that outputs a detection signal indicating When the peak frequency is less than the first frequency and greater than or equal to the second frequency, and the ejection time is greater than or equal to the ejection criterion value, a detection support signal indicating moderate AS is output. this.

[0017] Preferably, The first frequency is 300 Hz, or the second frequency is 180 Hz.

[0018] More preferably, The second processing unit outputs a detection support signal indicating a high probability of developing severe aortic stenosis in the future when the peak frequency is 150 Hz or higher and less than 300 Hz. Alternatively, the second processing unit outputs a detection support signal indicating that an echocardiogram should be performed when the peak frequency is 150 Hz or higher.

[0019] Alternatively, the second processing unit outputs a detection support signal indicating that an echocardiogram should be performed when the peak frequency is 150 Hz or higher.

[0020] Alternatively, the target range may be defined as the range from 100 ms after the start of the first sound (I) and not exceeding 500 ms. Alternatively, the first frequency may be a representative value determined based on multiple peak frequencies obtained from the target range for each of the multiple pulse waves.

[0021] Alternatively, the device has a detection unit for detecting an ejection click occurring near the first heart sound based on the heart sound data, and the second processing unit outputs the detection signal when the conditions are met that the peak frequency is equal to or greater than the first frequency and the ejection time is equal to or greater than the ejection reference value, in addition to the condition that an ejection click has been detected, and outputs the detection auxiliary signal when the conditions are met that the peak frequency is less than the first frequency and equal to or greater than the second frequency and the ejection time is equal to or greater than the ejection reference value, in addition to the condition that an ejection click has been detected.

Advantages of the Invention

[0022] According to the present invention, it is possible to simply and accurately detect an abnormality of the heart valve, particularly severe aortic valve stenosis, as a clear result.

[0023] According to the third aspect of the present invention, it is possible to simply and accurately detect the degree of abnormality of the heart valve as a clear result.

Brief Description of the Drawings

[0024] [Figure 1] It is a diagram showing the flow from the diagnosis to the treatment of aortic valve stenosis. [Figure 2] It is a block diagram showing an example of the configuration of the heart valve abnormality diagnosis system according to an embodiment of the present invention. [Figure 3]This figure shows an example of heart sounds obtained from a heart sound sensor. [Figure 4] This diagram schematically shows an example of the frequency components of heart sounds obtained through wavelet analysis. [Figure 5] This diagram shows an example of the processing flow of heart sound signals up to wavelet analysis. [Figure 6] This diagram conceptually illustrates an example of peak frequency diagnosis for severe AS. [Figure 7] This diagram conceptually illustrates an example of detecting the severity of AS in peak frequency diagnosis. [Figure 8] This figure shows an example of the results of wavelet analysis for normal heart sounds. [Figure 9] This figure shows an example of the results of wavelet analysis for moderate heart sounds. [Figure 10] This figure shows an example of wavelet analysis results for severe heart sounds. [Figure 11] This figure shows another example of wavelet analysis results for severe heart sounds. [Figure 12] This is a magnified view of a portion of the wavelet analysis results for severe heart sounds. [Figure 13] This figure illustrates the results of wavelet analysis on severe heart sounds. [Figure 14] This is a magnified view of a portion of the wavelet analysis results for normal heart sounds. [Figure 15] This figure shows a magnified view of the first heart sound (I) portion of the wavelet analysis results for severe heart sounds. [Figure 16] This figure shows an example of the correlation between severity and peak frequency in AS. [Figure 17] This figure shows the correlation between peak frequency and maximum aortic valve blood flow velocity (Vmax). [Modes for carrying out the invention]

[0025] [Principle of the present invention] First, the principle of this invention will be explained.

[0026] Figure 3 shows an example of heart sounds obtained from a heart sound sensor, with heart sounds S1-H from a severely affected aortic stenosis (AS) patient and heart sounds S1-N from a healthy individual shown together with an electrocardiogram. Figure 4 schematically shows an example of the frequency components of heart sounds obtained by wavelet analysis, with the results of the wavelet analysis schematically represented by the contour line WD of the scalogram.

[0027] Aortic stenosis (AS) is a condition in which calcification occurs in the aortic valve, causing it to harden and making it difficult to open. As a result, blood takes longer than usual to pass through the narrowed valve opening. The turbulence that occurs during this process generates a high-pitched murmur. Furthermore, because the blood passes through the narrowed lumen, the blood flow velocity increases, producing a high-pitched white noise. In other words, the higher the pitch of the noise, the more severe the condition. In echocardiography, the increased blood flow velocity at the stenotic site is used as an indicator of severity.

[0028] In this invention, instead of using echocardiography, heart sounds obtained through auscultation are used as the sound source. Wavelet analysis is used on the heart sound data obtained from the heart sounds to detect frequency increases (high tones) in specific intervals, thereby detecting severe aortic stenosis (AS). To determine whether or not it is severe AS, the peak frequency in a specific interval is detected and evaluated.

[0029] As shown in Figure 3, in severe AS, abnormal heart sounds occur between the first and second heart sounds, and these abnormal heart sounds are analyzed using wavelet analysis. Wavelet analysis reveals a peak frequency fp somewhere during ejection, as shown in Figure 4. This peak frequency fp is detected and evaluated.

[0030] Furthermore, focusing on the fact that ejection time prolongs with increasing severity of AS, we will obtain cardiac ejection time using wavelet analysis. We will evaluate the obtained ejection time and use it to detect severe AS. In addition, we will evaluate the frequencies of the main components of the first heart sound obtained from heart sounds to detect the presence or absence of ejection clicks. We will use the presence or absence of ejection clicks to detect severe AS.

[0031] Thus, the present invention accurately detects severe AS by utilizing specific acoustic patterns contained in heart sounds.

[0032] To verify the detection accuracy of the present invention, 85 patients with severe AS and 100 patients without AS were used as the control group, and evaluations were performed based on peak frequency and ejection time. As a result, the detection rate of severe AS was increased to 90%.

[0033] Since peak frequency and ejection time were found to have a positive correlation with the severity of AS, moderate AS can also be detected by performing wavelet analysis of heart sounds. In other words, according to the present invention, it is possible to diagnose the severity of AS.

[0034] Until now, there has been no technology that has been able to classify the severity of AS solely through auscultation. Using the technology of this invention, even physicians with limited experience in listening for heart murmurs can easily diagnose the severity of AS. Furthermore, by using a digital stethoscope at home, patients can detect AS themselves. This will enable referrals to specialized facilities for treatment, and is expected to reduce the number of patients who die suddenly from AS.

[0035] In specialized facilities, patients with moderate AS are sometimes asked to come in for echocardiograms about once every six months for follow-up. However, with the technology of the present invention, the progression of AS can be detected by auscultation alone, and it is expected that this will replace echocardiograms. This will help to reduce examination costs and lower medical expenses.

[0036] This invention can also be applied to the detection of other valvular heart diseases such as mitral valve regurgitation and aortic valve regurgitation.

[0037] The embodiments of the present invention will be described below. [Diagnosis process supported by a heart valve abnormality detection system] Figure 1 shows the process from diagnosis to treatment of aortic valve stenosis.

[0038] In Figure 1, for example, a patient with chronic heart disease who is recovering after being discharged from the hospital lives in their local area (P1), and visits their regular clinic for examination if any symptoms appear (P2). The clinic is equipped with a heart valve abnormality detection device, and together with its peripheral equipment, a heart valve abnormality diagnostic system 1 is constructed. The heart valve abnormality diagnostic system 1 performs peak frequency diagnosis using wavelet analysis to detect heart valve abnormalities, especially severe aortic stenosis, with high accuracy, and outputs and displays the detection results. The physician performs an examination, including auscultation using a stethoscope, and with the support of the heart valve abnormality diagnostic system 1, makes a final diagnosis of the heart disease (P3).

[0039] If a doctor suspects, for example, that a patient has severe AS, the patient will go to a hospital for an echocardiogram to confirm the diagnosis (P4). Once severe AS is confirmed, the patient will be treated with surgery such as artificial valve replacement, valve repair, or catheterization (P5).

[0040] In the heart valve abnormality diagnostic system 1, the heart valve abnormality detection device 5 is connected to hospitals and specialized facilities via a network to exchange various information. For example, the detection results from the heart valve abnormality detection device 5 are transmitted to the echocardiography room and operating room of the hospital for display, and the results of echocardiography and surgery are received and used for learning and setting various parameters in the heart valve abnormality detection device 5.

[0041] Thus, by constructing a heart valve abnormality detection system 1 centered on a heart valve abnormality detection device installed in clinics and other facilities, severe AS can be easily detected through auscultation, and this can be reliably followed up with a definitive diagnosis and treatment, thereby significantly improving the survival rate of AS patients. [Configuration of the heart valve abnormality detection system] Figure 2 shows an example of the configuration of a cardiac valve abnormality diagnostic system 1 according to an embodiment of the present invention.

[0042] In Figure 2, the cardiac valve abnormality diagnostic system 1 is equipped with a cardiac valve abnormality detection device 5, a heart sound sensor 6, an electrocardiograph 7, etc., and is connected to an echocardiography machine 8, etc. via a network NW.

[0043] The heart sound sensor 6 is an acoustic-electric converter such as a microphone, piezoelectric sensor, or accelerometer, and outputs a heart sound signal S1 corresponding to the patient's heart sounds. The heart sound signal S1 includes signals corresponding to the first heart sound, second heart sound, heart murmurs, etc. The heart sound sensor 6 may be one that is integrated into a stethoscope. The heart sound sensor 6 is placed, for example, at the second intercostal space on the right border of the patient's sternum, in the Erb region, or below the nipple to acquire heart sounds.

[0044] The electrocardiograph 7 acquires the patient's electrocardiogram and outputs an electrical signal (ECG signal) S2 that includes signals for each wave: P, Q, R, S, T, U, and P.

[0045] The echocardiography machine 18 uses ultrasound to acquire images and cross-sectional views of the heart and blood vessels, and is used to examine abnormalities in the structure and function of each part. It is installed in hospitals and specialized facilities.

[0046] The heart valve abnormality detection device 5 includes a heart sound data acquisition unit 21, a first processing unit 22, a second processing unit 23, an ejection time acquisition unit 24, a detection unit 25, an output / display unit 26, and a communication unit 27, among others.

[0047] Each or part of the heart valve abnormality detection device 5 can be configured by an information processing device such as a server or personal computer. Each function of the heart valve abnormality detection device 5 described below can be realized by a CPU that executes a computer program, or by cooperation between the CPU and hardware elements or devices. The computer program can be read from a recording medium or downloaded from a server and executed. The output / display unit 26 and the communication unit 2 can be realized by a display device and communication functions provided by the information processing device. [Heart sound data acquisition unit] Figure 5 shows an example of the processing flow of heart sound signals up to wavelet analysis.

[0048] The heart sound data acquisition unit 21 acquires and outputs heart sound data D1 corresponding to the heart sound, and includes a normalization unit 211, a noise reduction unit 212, and an amplification unit 213. The input heart sound signal S1 is either an analog signal or a digital signal. If it is an analog signal, it is converted to a digital signal by an appropriate AD converter, and then subsequent processing is performed.

[0049] The normalization unit 211 normalizes the heart sounds to accommodate differences in their magnitude.

[0050] The noise reduction unit 212 removes environmental noise, including ambient sounds unrelated to heart sounds and electrical noise. Furthermore, since respiratory sounds can sometimes interfere with heart sounds, removing these respiratory sounds would be even better.

[0051] The amplification unit 213 amplifies the heart sound signal S1, for example, by 16 times, so that it reaches a predetermined intensity level (amplitude level). The amplification level is adjustable. As a result, the heart sound data D1 output from the heart sound data acquisition unit 21 exhibits a waveform corresponding to the heart sound, and the amplitudes of the various frequency components included in the waveform are normalized. [First Processing Unit] Based on the heart sound data D1, the first processing unit 22 selects the area between the first and second heart sounds as the target range and obtains the peak frequency fp, which is the peak value of the heart sound frequency having an intensity (amplitude) equal to or greater than the reference intensity within that target range.

[0052] The first processing unit 22 includes a wavelet analysis unit 221, a reference intensity storage unit 222, a target range storage unit 223, a peak frequency storage unit 224, and the like.

[0053] The wavelet analysis unit 221 performs a continuous wavelet transform on the input heart sound data D1 to localize both the time and frequency components of the heart sound data D1. The continuous wavelet transform provides time-frequency features of the heart sound data D1, that is, the frequency and the intensity (amplitude) of each frequency component relative to the time axis. For example, analysis data is obtained in which the time t, frequency f, and the intensity (amplitude) dB of each frequency component in the heart sound data D1 are shown on the x, y, and z axes, respectively. This analysis data can be displayed on a two-dimensional plane using a scalogram, as shown in Figures 8 to 12, which will be explained later. In a scalogram, the horizontal axis represents time and the vertical axis represents frequency. The brightness or color of each point represents the intensity (amplitude) at a certain frequency at a certain point in time.

[0054] Furthermore, the various parameters and coefficients obtained by the wavelet analysis unit 221 can be used as features for machine learning by artificial intelligence (AI).

[0055] The reference intensity storage unit 222 stores a reference intensity ad for determining the peak frequency fp. The reference intensity ad is a condition that when acquiring the peak frequency fp, the intensity of its frequency component must be equal to or greater than the reference intensity ad. The reference intensity ad is used to remove the influence of noise components and harmonic components remaining in the heart sound data D1 and obtain the correct frequency components. The reference intensity ad can be determined experimentally so that the correct frequency components can be obtained empirically. If there are no noise components or harmonic components or they can be ignored, the reference intensity ad can be a value close to 0, for example, or the reference intensity ad can be excluded from the condition.

[0056] Furthermore, if a signal stronger than the peak frequency is found near half the peak frequency obtained from wavelet analysis, the stronger signal is the fundamental wave, and this frequency is taken as the peak frequency fp, excluding the harmonics. Other signals that are questionable as peak frequencies fp can be empirically excluded.

[0057] In short, it is important to obtain a meaningful value for the peak frequency fp for frequency components whose intensity is not zero.

[0058] The target range storage unit 223 stores a temporal target range tr for determining the peak frequency fp. The target range tr should be set to the time range in which the aortic valve is open and blood is flowing. However, since this is for determining the peak frequency fp within this time range, it does not necessarily have to coincide with the time range in which blood is actually flowing, as long as a useful peak frequency fp is extracted.

[0059] To illustrate how to set the target range tr, for example, the range tr is defined as the period from the start of the first heart sound (S1) to the end of the second heart sound (S2), which corresponds to ventricular systole. Furthermore, considering that the first heart sound is louder when the mitral valve opens and that it takes time for blood flow to actually start, the target range tr is defined as the period from the start of the first heart sound (S1) after time t1 has elapsed. Also, considering the prolongation of ejection time and the time period during which the peak frequency fp may actually be detected, the target range tr is defined as the period from the start of the first heart sound (S1) after time t1 has elapsed but not exceeding time t2. In addition, if the second heart sound is not clearly discernible, the target range tr is defined as the period from the start of the first heart sound (S1) after time t1 has elapsed but not exceeding time t2. In other words, the target range tr is limited in time. Furthermore, using the electrocardiogram signal S2, the range tr is defined as the period from the position of the S wave to the start or end of the second heart sound (S2).

[0060] Specifically, for example, time t1 could be set to 100ms and time t2 to 500ms, and the range could be defined as the period from 100ms after the start of the first heart sound (I) but not exceeding 500ms. Other time periods could also be used. With this setting method, the target range tr can be set based solely on the heart sound data D1, without using the electrocardiogram signal S2. Furthermore, the target range tr can be identified even if the second heart sound (II) is unclear.

[0061] The peak frequency storage unit 224 stores the acquired peak frequency fp. When acquiring the peak frequency fp, it is possible to acquire only one peak frequency fp, but there is a possibility that a peak frequency that is not appropriate due to large errors may be extracted. In other words, heart sounds vary from pulse wave (aortic wave), and the peak frequency fp may also change. In addition, if there is an arrhythmia or the pulse is prolonged, a shift in the position of the peak will occur. For this reason, it is preferable to acquire multiple peak frequencies and use their average value to improve the reliability of the peak frequency fp.

[0062] Therefore, in order to determine a representative value to be used as the peak frequency fp, for example, the average of multiple peak frequencies obtained from the target range tr for each of multiple pulse waves can be determined as the representative value of the peak frequency fp. Alternatively, for example, five peak frequencies can be obtained from the target range tr for each of five consecutive pulse waves, and the average of these five peak frequencies can be determined as the representative value of the peak frequency fp. Alternatively, the top n peak frequencies, for example five, can be selected from the multiple peak frequencies obtained from the target range tr, and the representative value of the peak frequency fp can be determined by the average of these five peak frequencies. Alternatively, the maximum value, minimum value, and other singular values ​​can be excluded from the maximum 10 peak frequencies obtained from the target range tr for each of 10 consecutive pulse waves, and the average of the remaining five peak frequencies can be determined as the representative value of the peak frequency fp. Furthermore, instead of using the mean, the root mean square or the median may be used, and a suitable selection algorithm may be used to determine the representative value. In addition, if respiratory sounds are mixed in with the heart sound data D1, the representative value can be determined using peak frequencies obtained only from the target range tr that do not overlap with the duration of the respiratory sounds.

[0063] The first processing unit 22 outputs the peak data D2, which includes the peak frequency fp acquired in this manner, to the second processing unit 23.

[0064] The ejection time acquisition unit 24 acquires the ejection time tk based on the heart sound data D1. The acquired ejection time tk is stored in the ejection time storage unit 241 and output as ejection data D3.

[0065] Ejection time tk is the time during which blood flows out of the aortic valve, and this corresponds to the ejection phase due to ventricular contraction. The ejection phase is usually the period from the first to the second heart sound during ventricular systole, after the intraventricular pressure becomes higher than the intraaortic pressure and the aortic valve opens. Therefore, ejection time tk can be obtained by detecting the timing of aortic valve opening and the timing of the second heart sound based on heart sound data D1.

[0066] Ejection time (tk) is approximately 200 ms in healthy individuals, but increases as the condition worsens. For example, in severe AS, it increases to approximately 300 ms to 500 ms. There is little individual variation.

[0067] Furthermore, since the ejection time tk is used for comparison with the ejection reference value ce, it does not necessarily have to match the actual ejection time of the patient; in short, any value corresponding to the patient's ejection time is acceptable. Therefore, for example, the time from the first heart sound to the second heart sound can be obtained as the ejection time tk, and an appropriate ejection reference value ce can be set accordingly for comparison.

[0068] The detection unit 25 detects the main component of the first heart sound, that is, the frequency of the loudest sound in the vicinity of the first heart sound, based on the heart sound data D1. The detected frequency, the main frequency fm, is stored in the main frequency storage unit 251.

[0069] Furthermore, the I-tone reference value cs is stored in the I-tone reference value storage unit 252. When the main frequency fm is greater than or equal to the I-tone reference value cs, click data D4 indicating that an ejection click has occurred is output to the second processing unit 23.

[0070] The first heart sound (S1) is normally the sound of the mitral valve closing, and the aortic valve opens immediately afterward. However, if the aortic valve hardens and its movement deteriorates, it does not open easily. Therefore, the aortic valve opens due to the impact of blood being pushed out by the contraction of the ventricle. At this time, an ejection click occurs, which is a higher-pitched sound than the normal S1 heart sound. In other words, an ejection click indicates that the aortic valve is hardening and its function is deteriorating.

[0071] Examination of the main frequency components of the ejection click revealed that they were in the 50-60 Hz range. Since the ejection click occurs immediately after the first heart sound (S1), it sounds like the S1 has become higher pitched on a stethoscope, and in heart sound data D1, it is detected as being integrated with the S1. In healthy individuals, the S1 consists of frequency components of 30 Hz to 40 Hz. In addition, in severe AS, the early stages of the S1 consist of frequency components of 40 Hz or higher.

[0072] Therefore, in order to detect the ejection click, the reference value for the first sound cs is set to, for example, 50Hz, 55Hz, or 60Hz. If the reference value for the first sound cs is set to the lower value of 50Hz, the detection sensitivity will increase, and the possibility of false detection of sounds other than ejection clicks will increase, but the possibility of failing to detect the ejection click will decrease, so by ANDing this with other conditions, the accuracy of detecting severe AS will improve. If the reference value for the first sound cs is set to the higher value of 60Hz, the detection sensitivity will decrease, but if click data D4 is detected, there is a high possibility that aortic valve hardening is progressing, and the possibility of severe AS is high. Therefore, the reference value for the first sound cs should be determined considering these factors. In addition, the weight of click data D4 in the detection of severe AS may be changed depending on the value of the reference value for the first sound cs.

[0073] In this way, by detecting ejection clicks by focusing on the high frequency of the main components of the sound, it is possible to detect heart valve abnormalities, including severe AS, with greater accuracy. [Second Processing Unit and Peak Frequency Diagnostics] The second processing unit 23 detects cardiac valve abnormalities, particularly severe AS, based on peak data D2, ejection data D3, and click data D4, in combinations of the following conditions 1 to 3, depending on the selection of the judgment mode. (Condition 1) When the peak frequency fp is equal to or greater than the peak reference value cp. (Condition 2) When the ejection time tk is equal to or greater than the ejection threshold value ce. (Condition 3) When the main frequency fm is equal to or greater than the I tone reference value cs, i.e., when an ejection click is detected.

[0074] In this embodiment, in judgment mode 1, when condition 1 is met, it is determined that there is a high probability of severe AS, and a detection signal D5 indicating this is output. In judgment mode 2, when both condition 1 and condition 2 are met simultaneously, it is determined that there is a high probability of severe AS, and a detection signal D5 indicating this is output. In judgment mode 3, when all three conditions 1, 2, and 3 are met simultaneously, it is determined that there is a high probability of severe AS, and a detection signal D5 indicating this is output.

[0075] When detection signal D5 is output, the output / display unit 26 displays on the screen that it has been determined that there is a high possibility of severe AS, emits a warning sound or other audio as needed, and transmits to other devices via the communication unit 27 and the network NW. For example, it displays the message "Severe AS suspected" on the screen.

[0076] Further details are provided below.

[0077] The second processing unit 23 stores the peak reference value cp and the ejection reference value ce in its respective storage units 231 and 232. Although not shown in the diagram, it also includes an operation unit and a setting unit for switching and setting the judgment mode.

[0078] In judgment mode 1, when the peak frequency fp is equal to or greater than the peak reference value cp, it is determined that there is a high probability of severe AS, and a detection signal D5 indicating this is output. The peak reference value cp is, for example, 300 Hz. In other words, the detection signal D5 is output when the peak frequency fp is 300 Hz or higher.

[0079] Based on experience with previous cases, the peak frequency fp in severe AS is 250 Hz or higher, with an average value of around 320 Hz and a variation of approximately ±50 Hz. Therefore, if we set the peak reference value cp at 300 Hz and detect severe AS when the peak frequency fp is 300 Hz or higher, most severe AS cases will be detected. However, a peak frequency fp of 300 Hz or higher does not necessarily mean severe AS, so in this regard, we will need to wait for combinations of other conditions or for screening such as ultrasound examinations.

[0080] Thus, according to judgment mode 1, the peak frequency fp is higher in cases of severe AS, and the detected peak frequency fp is compared with the peak reference value cp. When the peak frequency fp is, for example, 300 Hz or higher, a detection signal D5 is output. This allows for the detection of patients who are highly likely to have severe AS with considerable accuracy.

[0081] Furthermore, if the peak reference value cp is set to a value higher than 300Hz, such as 320Hz or 340Hz, the possibility of severe AS going undetected increases, but the possibility of detecting severe AS when it is not decreases. Therefore, it is best to set the peak reference value cp considering this point, as well as in combination with other conditions that compensate for this point.

[0082] In judgment mode 2, in addition to the peak frequency fp condition, if the ejection time tk is greater than or equal to the ejection threshold value ce, it is determined that there is a high probability of severe AS, and a detection signal D5 indicating this is output. The ejection threshold value ce is, for example, 300 ms.

[0083] As mentioned above, patients with severe AS have an extended ejection time, which empirically ranges from approximately 300 to 460 ms. Therefore, by setting the ejection threshold value ce to the lower of 300 ms and adding an ejection time tk of 300 ms or higher as a condition for detecting severe AS, the probability of a detection signal D5 being emitted when a patient actually has severe AS increases, thereby improving the reliability of detection.

[0084] Thus, according to judgment mode 2, when conditions 1 and 2 are met, that is, when the peak frequency fp is, for example, 300 Hz or higher, and the ejection time tk is, for example, 300 ms or higher, the detection signal D5 is output. This allows for the detection of patients who are likely to have severe AS with higher accuracy.

[0085] As mentioned above, there are errors and variations when obtaining the actual ejection time tk. Therefore, an appropriate ejection criterion value ce should be set according to the errors and variations in obtaining the ejection time tk, and according to the method of obtaining the ejection time tk.

[0086] In judgment mode 3, in addition to the conditions for peak frequency fp and ejection time tk, if the major frequency fm is greater than or equal to the I-sound reference value cs, that is, if an ejection click occurs, it is determined that there is a high probability of severe AS, and a detection signal D5 indicating this is output.

[0087] As mentioned above, an ejection click indicates that the aortic valve is hardening and its function is deteriorating, and the occurrence of an ejection click suggests the possibility of severe aortic stenosis (AS). Therefore, by adding the occurrence of an ejection click as condition 3 to conditions 1 and 2, a more reliable detection signal D5 can be obtained, and patients who are likely to have severe AS can be detected with even greater accuracy.

[0088] Furthermore, the detection accuracy and sensitivity of ejection clicks change depending on how the I-sound reference value c is set. By setting the I-sound reference value c while considering its relationship and compatibility with other conditions, it becomes possible to detect severe AS with extremely high accuracy.

[0089] In addition to the judgment modes 1-3 described above, it is possible to set judgment modes by combining conditions 1-3 in various ways. For example, it is possible to combine condition 1 and condition 3, or condition 2 and condition 3. It is also possible to combine conditions other than those 1-3.

[0090] Furthermore, depending on the values ​​of the peak frequency fp, ejection time tk, and main frequency fm obtained under other conditions, as well as the judgment results, the settings for the ejection reference value ce, the first tone reference value cs, and the peak reference value cp under the relevant conditions may be changed. In this case, the application order of conditions 1 to 3 may be set in various ways, and one condition may be applied multiple times.

[0091] Furthermore, the second processing unit 23 also includes a determination mode 4 for detecting the severity of AS. In other words, in determination mode 4, the severity of AS is determined by comparing the detected peak frequency fp with a set frequency range cf. Here, we will describe an example in which a detection signal D5 is output when AS is severe and a detection support signal D6 is output when AS is moderate.

[0092] The frequency range cf includes a first frequency for determining severe AS and a second frequency for determining moderate AS. The first frequency may be substituted with the peak reference value cp. If the peak frequency fp is equal to or greater than the first frequency, it is determined to be severe AS. If the peak frequency fp is less than the first frequency and equal to or greater than the second frequency, it is determined to be moderate AS (Condition 4).

[0093] For example, 300Hz is set as the first frequency and 180Hz as the second frequency. In this case, the second processing unit 23 outputs a detection signal D5 indicating a high probability of severe AS when the peak frequency fp is 300Hz or higher, and outputs a detection support signal D6 indicating a high probability of moderate AS when the peak frequency fp is 180Hz or higher but less than 300Hz.

[0094] Based on experience with previous cases, the peak frequency fp was in the range of 180Hz to 300Hz in cases of moderate AS. Conversely, no peak waveform appeared in cases of mild AS. Therefore, if the acquired peak frequency fp is in the range of 180Hz to 300Hz, there is a high probability that the patient has moderate AS.

[0095] Furthermore, in judgment mode 4, it is possible to detect the severity of AS by determining whether there is a high probability of developing severe AS in the future and whether an echocardiogram should be performed at this time. This will be explained next.

[0096] Figure 17 shows the peak frequency fp detected and plotted for 189 cases (patients) including mild, moderate, and severe AS.

[0097] In Figure 17, the horizontal axis represents the peak frequency fp (Hz), and the vertical axis represents the maximum aortic valve blood flow velocity Vmax (m / sec). Here, the maximum aortic valve blood flow velocity Vmax (hereinafter referred to as "Vmax") was measured for each case using continuous wave Doppler ultrasound. Vmax is an important factor used in echocardiography to assess the severity of AS, and this is described in detail in the "Guidelines for the Treatment of Valvular Heart Disease" 2020 revised edition, the Joint Guidelines of the Japanese Circulation Society / Japanese Association for Thoracic Surgery / Japanese Society for Vascular Surgery / Japanese Society for Cardiovascular Surgery (JCS / JATS / JSVS / JSCS 2020 Guideline on the Management of Valvular Heart Disease).

[0098] According to "Table 30 Assessment of AS Severity by Echocardiography" on page 63 of Chapter 5 of the aforementioned "Guidelines for the Treatment of Valvular Heart Disease," "aortic valve sclerosis" is defined as a Vmax of 2.5 (m / sec) or less, "mild AS" as 2.6-2.9, "moderate AS" as 3.0-3.9, and "severe AS" as 4.0 or more. Figure 17 shows cases of "mild AS" (78 people), "moderate AS" (15 people), and "severe AS" (96 people) according to these evaluation criteria. Note that "mild AS" (15 people) here includes not only the original "mild AS" but also "aortic valve sclerosis" with milder symptoms and the normal group.

[0099] Now, according to Figure 17, the correlation coefficient is 0.84, indicating a strong positive correlation between peak frequency fp and Vmax. In other words, it is shown that cases with a high peak frequency fp tend to have a larger Vmax.

[0100] Furthermore, in cases of "mild AS" where Vmax is less than 3.0 (m / sec), the peak frequency fp is almost always 150 Hz or less. Also, using a regression line, the peak frequency fp corresponding to Vmax 4.0 is approximately 300 Hz.

[0101] Based on these findings, cases with a peak frequency (fp) of 150 Hz or higher cannot be classified as "mild AS," and echocardiography should be performed to determine the severity. Furthermore, cases with a peak frequency (fp) of 150 Hz or higher but less than 300 Hz are likely to develop severe AS in the future. Since surgery is likely to be necessary if severe AS develops, these patients should be considered for future surgery.

[0102] Therefore, when the peak frequency fp is 150 Hz or higher but less than 300 Hz, the second processing unit 23 outputs a detection support signal D6a indicating a high probability of developing severe aortic stenosis in the future. Also, when the peak frequency fp is 150 Hz or higher, the second processing unit 23 outputs a detection support signal D6b indicating that an echocardiogram should be performed.

[0103] Although detection auxiliary signals D6, D6a, and D6b contain some overlapping information, understanding their respective meanings and using them accordingly will enable quick and accurate responses after they are displayed.

[0104] Furthermore, to detect these severities, the ejection time tk or the dominant frequency fm may be considered, in addition to the peak frequency fp, as can be referred to below.

[0105] Furthermore, by statistically using these detection support signals D6, D6a, and D6b, it is possible to predict the approximate number or trend of increase or decrease in the number of patients with severe AS or those who should undergo surgery in the future, which can be useful for hospital staffing and equipment planning, or for national medical policies.

[0106] In addition, for judgment mode 4, the judgment can be made by combining conditions 2 and 3 used in judgment modes 2 and 3. In this case, the parameters in conditions 2 and 3 may be adjusted so that continuous detection can be performed correctly according to the actual severity.

[0107] For example, when combining judgment mode 2, severe AS is determined when conditions 1 and 2 are met, and moderate AS is determined when conditions 4 and 2 are met. When determining moderate AS, the ejection threshold value ce in condition 2 may be lower than in the case of severe AS. This is because the ejection time tk is considered to be smaller in the case of moderate AS than in the case of severe AS.

[0108] In this way, by combining conditions 1 to 4 and adjusting each parameter, the cardiac valve abnormality detection device 5 can detect the severity of AS.

[0109] Furthermore, peak frequency (fp), ejection time (tk), and dominant frequency (fm) are associated not only with aortic stenosis but also with other valvular heart diseases such as aortic regurgitation and mitral regurgitation, as well as with abnormalities in prosthetic valves. Therefore, these parameters can be applied to the detection of abnormalities and deterioration in these various heart valves, including prosthetic valves.

[0110] Furthermore, by providing artificial intelligence with a large amount of data, including peak frequency (fp), ejection time (tk), primary frequency (fm), other test values, and actual symptoms and progression, it is possible to use the AI ​​to diagnose the possibility and severity of severe AS, and to detect or determine the degree of various heart valve abnormalities.

[0111] Figure 6 conceptually shows an example of peak frequency diagnosis for severe AS, and Figure 7 conceptually shows an example of detecting the severity of AS in peak frequency diagnosis.

[0112] In Figure 6, severe AS is determined when condition 1 (peak frequency fp), condition 2 (ejection time tk), and condition 3 (major frequency fm) are simultaneously met. This corresponds to determination mode 3 described above. In this case, for the portion V1 that satisfies conditions 1 and 2 but not condition 3, for the portion V2 that satisfies conditions 1 and 3 but not condition 2, and for the portion V3 that satisfies condition 1 but not conditions 2 and 3, a signal indicating their state should be included in the detection signal D5. These portions V1, V2, and V3 can be treated, for example, as severe AS or suspected severe AS.

[0113] In Figure 7, under condition 3, if the peak frequency fp is, for example, 300 Hz or higher, it is determined to be a severe AS. However, there are various ways to determine moderate AS. For example, if the peak frequency fp is in the range of 180 to 300 Hz, and condition 2 for ejection time tk and condition 3 for major frequency fm are simultaneously met, then part M1 is determined to be a moderate AS. For parts M2 to M7 that lack any of the conditions, a signal indicating that state can be included in the detection auxiliary signal D6. These parts M2 to M7 can be treated, for example, as moderate AS or suspected moderate AS.

[0114] Furthermore, while Figure 7 shows the case where the peak frequency fp is 180 Hz or higher, by replacing "180 Hz" with "150 Hz", it can be applied to detecting the severity of AS when the peak frequency fp is 150 Hz or higher. In this case, for example, it may be determined that there is a high probability of developing severe AS in the future for partial M1, M5-M7, and that surgery should be considered in the future. Alternatively, it may be determined to be a suspected case of moderate AS.

[0115] The example in Figure 7 corresponds to the judgment mode 4 described above, and can indicate the severity of AS according to parts M2 to M7. [Explanation of scalograms using wavelet analysis] Next, we performed wavelet analysis on the heart sound data D1 for several cases, and the results are shown as scalograms (amplitude scalograms) in Figures 8 to 15. We will now explain these results.

[0116] Figure 8 shows an example of wavelet analysis results for normal heart sounds, Figure 9 shows an example of wavelet analysis results for moderate heart sounds, Figure 10 shows an example of wavelet analysis results for severe heart sounds, Figure 11 shows another example of wavelet analysis results for severe heart sounds, Figure 12 shows a magnified view of a portion of the wavelet analysis results for severe heart sounds, Figure 13 is a diagram to explain the wavelet analysis results for severe heart sounds, Figure 14 shows a magnified view of a portion of the wavelet analysis results for normal heart sounds, Figure 15 shows a magnified view of the first heart sound portion of the wavelet analysis results for severe heart sounds, and Figure 16 shows an example of the correlation between severity and peak frequency in AS.

[0117] In these figures, the horizontal axis represents time (sec) on a linear scale, and the vertical axis represents frequency (kHz) on a logarithmic scale. Intensity (amplitude) is normally shown on a color scale that changes continuously from blue to green and then to yellow for better visibility, but here it is shown on a grayscale where the brightness changes continuously from black to white. For example, the intensity is normalized so that black, which has the minimum brightness, is 0, and white, which has the maximum brightness, is 1. The intervals between black and white are scaled as 0.1, 0.2, 0.3, etc., depending on the brightness. Therefore, it can be said that brightness represents intensity (amplitude) in the scalograms shown in the figures.

[0118] For example, in Figure 8, which shows a scalogram of a healthy person's normal heart sounds, the first and second heart sounds are recorded in approximately 20 pulse waves over a period of about 14 seconds along the horizontal axis. Areas with an intensity (amplitude) of 0 are shown in black, and as the intensity increases, the brightness increases and approaches white. The frequency of the first heart sound in a healthy person's normal heart sounds is approximately between 30Hz and 40Hz, and it appears as a white dot with an intensity of about 0.6 to 0.7 in this frequency range on the vertical axis.

[0119] In Figure 8, a cluster of points with an intensity of approximately 0.2 to 0.5 can be seen rising upwards from the vicinity of the first and second heart sounds, resembling flames. The peak frequency fp of normal heart sounds, that is, the peak value of the frequency component of normal heart sounds, is approximately in the range of 90 to 160 Hz, and the highest points in these clusters do not exceed 160 Hz.

[0120] Note that five arrows are shown at the top of Figure 8, and below these arrows, five peaks of point cloud with an intensity of approximately 0.3 can be seen. These are respiratory sounds and are unrelated to heart sounds. Therefore, when acquiring the peak frequency fp, it is necessary to exclude respiratory sounds. For this reason, as mentioned above, the noise reduction unit 212 removes respiratory sounds as much as possible, but if they cannot be completely removed and remain, the pulse waves that overlap with the respiratory sounds are excluded when acquiring the peak frequency fp. The period of respiratory sounds is almost constant, so respiratory sounds can be identified based on their waveform.

[0121] In the scalogram for moderate heart sounds shown in Figure 9, the white dot for the first heart sound is shifted slightly upward overall compared to the case of normal heart sounds, and the peak frequency fp is almost 180 Hz or higher.

[0122] In the scalograms for severe heart sounds shown in Figures 10 and 11, the white dot for the first heart sound (S1) is generally further upward than in the case of moderate heart sounds, and the interval between the first and second heart sounds (S2) is wider. Following the first heart sound, there is a peak in the frequency components of the heart murmur, with the highest point of the peak being the peak frequency fp, and all peak frequencies fp exceed approximately 300 Hz.

[0123] In Figures 9 to 11, the target range tr was defined as the interval between the first and second heart sounds. Any peaks other than the peak frequency fp shown in the figures are either outside the target range tr or are due to respiratory sounds, and are therefore excluded from the target range tr. In the example shown in Figure 10, the top five peak frequencies were selected from the recorded heartbeats, and the representative value of the peak frequency fp was determined by averaging these five peak frequencies. In this case, the representative value of the peak frequency fp is 304.4 Hz, which is above the peak reference value cp of 300 Hz, and is therefore judged to have a high probability of severe AS.

[0124] Figures 12 and 13 show the frequency components of four heart murmurs. The point with the highest intensity (the whitest point) near the first heart sound (S1) indicates an ejection click. The ejection click appears immediately after or together with the first heart sound, and the frequency of its main component is around 60 Hz. From there, the frequency components of the heart murmur spread out to a width of approximately 280 ms to 500 ms. This peak is due to the narrowing of blood flow caused by aortic valve stenosis, resulting in a faster blood flow velocity and the generation of a high-frequency heart murmur, with a peak frequency fp exceeding 300 Hz. Simultaneously, the ejection time is prolonged to approximately 280 ms to 500 ms, which delays the onset of the second heart sound (S2).

[0125] Figure 14 shows the normal first and second heart sounds. It can be seen that the frequency of the first heart sound is between 30 Hz and 40 Hz. Note that although the heart rate in Figure 14 is 60 bpm, the frequency of the first heart sound was also between 30 Hz and 40 Hz in a scalogram of an example with a heart rate of 75 bpm.

[0126] Figure 15 shows the first heart sound (I) of a severe case, and it can be seen that the frequency of the first heart sound exceeds 40 Hz and reaches almost 60 Hz.

[0127] As described above, the heart valve abnormality detection device 5 of this embodiment can easily and accurately detect heart valve abnormalities, particularly severe aortic stenosis, and produce clear results. Furthermore, it can easily and accurately detect the degree of heart valve abnormality and produce clear results.

[0128] Furthermore, since results can be obtained based solely on heart sound data D1 acquired through auscultation, it can be easily implemented without increasing the burden on doctors or patients in clinical settings. Severe AS can be determined by comparing the peak frequency fp obtained based on heart sound data D1 with the peak reference value cp (peak frequency diagnosis), resulting in clear judgment results and reasons for the judgment. Since the peak frequency fp can be easily obtained using wavelet analysis, processing is simple, processing time is fast, and it can be implemented at a relatively low cost. Further improvements in detection accuracy can be achieved by adjusting the peak reference value cp and ejection reference value ce by feeding back the results of echocardiography and other examinations.

[0129] Incidentally, as shown in Figure 16 as the hypothetical curve CV1, it was found that the severity of AS has a positive correlation with the peak frequency fp. Based on this finding, it is expected that the severity of AS can be detected with even higher accuracy.

[0130] In the embodiments described above, the configuration of the heart valve abnormality detection device 5 can be modified in various ways. For example, the second processing unit 23 may output not only detection results such as "moderate AS" and "severe AS," but also severity levels corresponding to peak frequency fp, ejection time tk, or major frequency fm, i.e., the progression of AS. Alternatively, the peak frequency fp, ejection time tk, major frequency fm, etc., may be output as they are.

[0131] In the embodiments described above, the heart valve abnormality detection device 5 can be implemented as a miniaturized home health device and as a portable device. A computer program that functions as the heart valve abnormality detection device 5 may be executed on a personal computer or smartphone, and the personal computer or smartphone may be implemented as the heart valve abnormality detection device.

[0132] To implement the present invention as a method for detecting heart valve abnormalities, it is sufficient to perform the functions of each part of the heart valve abnormality detection device 5 or heart valve abnormality detection system 1, including, for example, the first processing unit 22, the second processing unit 23, and the ejection time acquisition unit 24 described above.

[0133] In addition, the overall configuration, structure, shape, function, setting values, processing content, processing order, output format, and other aspects of the heart sound data acquisition unit 21, first processing unit 22, second processing unit 23, ejection time acquisition unit 24, detection unit 25, output / display unit 26, heart valve abnormality detection device 5, heart valve abnormality detection system 1, etc., as well as the content, signal form, and output timing of detection signals D5, detection auxiliary signals D6, etc., can be changed in various ways beyond those described above. [Explanation of Symbols]

[0134] 1. Heart valve abnormality detection system 5. Heart valve abnormality detection device 6 Heart sound sensor 7. Electrocardiograph 21 Heart sound data acquisition unit 22 First Processing Unit 23 Second Processing Unit 24 Ejection time acquisition section 25 Detection unit 26 Output / Display Unit 211 Normalization section 212 Noise Reduction Section 213 Amplifier section 213 D1 Heart sound data D5 detection signal D6 Detection Auxiliary Signal fp peak frequency cp peak reference value tr Target range ad standard strength fm main frequency CS I Sound Reference Value tk ejection time CE ejection threshold cf frequency range

Claims

1. A heart sound data acquisition unit that acquires heart sound data corresponding to heart sounds, A first processing unit, based on the aforementioned heart sound data, selects the area between the first and second heart sounds as the target range and acquires the peak frequency, which is the peak value of the frequency component of the heart sound within the target range. An ejection time acquisition unit that acquires ejection time based on the aforementioned heart sound data, A storage unit is provided to store a first frequency, which is a peak reference value for determining severe AS, a second frequency lower than the first frequency, and an ejection reference value, respectively. A second processing unit outputs a detection signal indicating severe AS when the peak frequency is equal to or greater than the first frequency and the ejection time is equal to or greater than the ejection reference value, and outputs a detection auxiliary signal indicating moderate AS when the peak frequency is less than the first frequency and equal to or greater than the second frequency and the ejection time is equal to or greater than the ejection reference value. A heart valve abnormality detection device characterized by having the following features.

2. The aforementioned first frequency is 300 Hz. The heart valve abnormality detection device according to claim 1.

3. The second frequency is 180 Hz. The heart valve abnormality detection device according to claim 2.

4. The second processing unit outputs a detection support signal indicating a high probability of developing severe aortic valve stenosis in the future when the peak frequency is 150 Hz or higher and less than 300 Hz. The heart valve abnormality detection device according to claim 2.

5. The second processing unit outputs a detection support signal indicating that an echocardiogram should be performed when the peak frequency is 150 Hz or higher. The heart valve abnormality detection device according to claim 1 or 2.

6. The target range is defined as the period from 100 ms after the start of the first tone, and not exceeding 500 ms. A heart valve abnormality detection device according to any one of claims 1 to 5.

7. As the peak frequency, a representative value determined based on multiple peak frequencies obtained from the target range for each of the multiple pulse waves is used. A heart valve abnormality detection device according to any one of claims 1 to 6.

8. The aforementioned ejection threshold value is 280 ms. A heart valve abnormality detection device according to any one of claims 1 to 7.

9. It has a detection unit for detecting an ejection click that occurs near the first heart sound based on the aforementioned heart sound data, The second processing unit outputs the detection signal when the conditions are met that the peak frequency is equal to or greater than the first frequency and the ejection time is equal to or greater than the ejection reference value, in addition to the detection of an ejection click. The second processing unit outputs the detection auxiliary signal indicating moderate AS when the conditions are met that the peak frequency is less than the first frequency and equal to or greater than the second frequency and the ejection time is equal to or greater than the ejection reference value, in addition to the detection of an ejection click. A heart valve abnormality detection device according to any one of claims 1 to 8.

10. The detection unit detects the main frequency, which is the frequency of the main component of the first tone, and detects the ejection click when the main frequency is equal to or greater than the first tone reference value. The heart valve abnormality detection device according to claim 9.

11. The aforementioned reference value for tone I is 50 Hz. The heart valve abnormality detection device according to claim 10.

12. The first processing unit, when acquiring the peak frequency, targets the frequency components of the heart sound that have an intensity equal to or greater than the reference intensity within the target range. A heart valve abnormality detection device according to any one of claims 1 to 11.

13. The first processing unit obtains the peak frequency by performing wavelet analysis on the heart sound data. A heart valve abnormality detection device according to any one of claims 1 to 12.

14. The aforementioned heart sound data acquisition unit is A normalization processing unit for normalizing the aforementioned heart sounds, A noise reduction unit that removes environmental noise, An amplification unit that amplifies the signal to a predetermined intensity level, A heart valve abnormality detection device according to any one of claims 1 to 13.

15. A computer program that causes a computer to function as the heart valve abnormality detection device described in any one of claims 1 to 14.

16. A computer-readable medium on which the computer program described in claim 15 is recorded.

17. A method for operating a heart valve abnormality detection device, The heart sound data acquisition unit of the heart valve abnormality detection device acquires heart sound data corresponding to the heart sound, The first processing unit of the heart valve abnormality detection device performs the following steps based on the heart sound data: the first processing unit selects the area between the first and second heart sounds as the target range and obtains a peak frequency, which is the peak value of the frequency component of the heart sound having an intensity equal to or greater than a reference intensity within the target range; The ejection time acquisition unit of the cardiac valve abnormality detection device includes the step of acquiring the ejection time based on the heart sound data, The second processing unit of the cardiac valve abnormality detection device uses a first frequency, which is a peak reference value for determining severe AS, a second frequency lower than the first frequency, and an ejection reference value stored in the memory unit, to output a detection signal indicating severe AS when the peak frequency is equal to or greater than the first frequency and the ejection time is equal to or greater than the ejection reference value, and outputs a detection auxiliary signal indicating moderate AS when the peak frequency is less than the first frequency and equal to or greater than the second frequency and the ejection time is equal to or greater than the ejection reference value. A method for operating a heart valve abnormality detection device, characterized by performing the following actions.

18. When the peak frequency is equal to or greater than the second frequency and the ejection time is not equal to or greater than the ejection reference value, a detection auxiliary signal indicating that state is output. A heart valve abnormality detection device according to any one of claims 1 to 4.

19. When the peak frequency is equal to or greater than the second frequency, and the ejection time is not equal to or greater than the ejection reference value, or when no ejection click is detected, a detection auxiliary signal indicating that state is output. The heart valve abnormality detection device according to claim 9.

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