Auscultation system, stethoscope, and method

Abstract

To provide means that can easily collect a stable auscultation sound.SOLUTION: An auscultation system comprises: a sensor for collecting an auscultation sound; and a control unit. The control unit compares a collected auscultation sound waveform collected by the sensor with a predetermined auscultation sound waveform. The control unit determines a physical status on the basis of a comparison result. The predetermined auscultation sound waveform is a waveform based on an auscultation sound of a user in which the auscultation sound is collected. The predetermined auscultation sound waveform is also a waveform of an auscultation sound for an abnormal physical status which has common characteristics among a plurality of persons.SELECTED DRAWING: Figure 7

Description

[Technical field]

[0001] The present invention relates to an auscultation system, a stethoscope, and a method. [Background technology]

[0002] Among stethoscopes, there are so-called electronic stethoscopes that electronically pick up sounds such as heart sounds using sensors such as microphones, amplify the picked up sounds, and allow doctors and others to listen to the amplified sounds (see, for example, Patent Document 1). The sounds picked up by the electronic stethoscope may be subjected to, for example, frequency analysis. Patent Document 2 has the following description: "When making a diagnosis using a stethoscope in remote medical treatment using a videophone or the like, the patient touches a stethoscope with a chest piece containing a microphone to his or her own body, and the microphone converts heart sounds and the like into electrical signals, which are then transmitted to a remote doctor via communication. However, patients are not accustomed to using stethoscopes, and the chest piece may not be in proper contact with the body surface. In such cases, with conventional stethoscopes, it is very difficult for a doctor to judge whether the chest piece is in contact with the body by looking at the visual information from the remote image, and there is a risk of misdiagnosis due to the chest piece not being in proper contact with the body surface." [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2004-242849 A [Patent Document 2] International Publication No. 2017 / 159752 Summary of the Invention [Problem to be solved by the invention]

[0004] In addition to the issues described in Patent Document 2 above, with conventional stethoscopes, the determination of changes in the physical condition depends on the skill of the user to operate the stethoscope and auscultate. Therefore, in remote medical care (treatment) and home medical care, when an individual measures heart sounds, etc. with a stethoscope, there is a problem that it is very difficult to determine changes in the physical condition.

[0005] An object of the present invention is to provide a means for easily determining changes in a physical condition. [Means for solving the problem]

[0006] The auscultation system of the first invention comprises a sensor for collecting auscultatory sounds and a control unit, and the control unit compares the collected auscultatory sound waveform collected by the sensor with a predetermined auscultatory sound waveform, and judges the physical condition based on the comparison result.

[0007] In the present invention, the control unit compares the collected auscultatory sound waveform picked up by the sensor with a predetermined auscultatory sound waveform, and judges the physical condition based on the comparison result. For example, if the past auscultatory sound waveform of the user from which the auscultatory sound is collected is used as the "predetermined auscultatory waveform", a comparison can be made with the auscultatory sound waveform related to the user's body in the past, and a change in the current physical condition relative to the past physical condition can be judged. As a result, a change in the current physical condition relative to the past physical condition is judged, and the user can easily judge the change in the physical condition based on the judgment result.

[0008] The auscultation system of the second invention is the auscultation system of the first invention, characterized in that the control unit creates energy trend data showing the progression of amplitude versus time based on the auscultation sound waveform, places marks on peaks in the energy trend data, and obtains the period of the auscultation sound waveform from the spacing between the marks.

[0009] The auscultation system of the third invention is the auscultation system of the second invention, wherein the control unit: Auscultatory sound waveform data in a section that is 1 / 2 of one cycle before and after the peak of the energy trend data, or data based on the auscultatory sound waveform, is used as a collected auscultatory sound waveform or to obtain a specified auscultatory sound waveform.

[0010] The auscultation system of the fourth invention is characterized in that, in the auscultation system of the second invention, the control unit uses auscultation sound waveform data in a section that is 1 / 2 of one cycle before and after the peak of the energy trend data, or data based on the auscultation sound waveform, overwritten a predetermined number of times, as a collected auscultation sound waveform or for obtaining a predetermined auscultation sound waveform.

[0011] When the auscultatory sound waveform is, for example, a cardiac waveform, it is a periodic signal and therefore there is measurement variation. For this reason, in the present invention, the control unit uses auscultatory sound waveform data in a section of 1 / 2 of one period around the peak of the energy trend data, or data based on the auscultatory sound waveform overwritten a specified number of times, as the collected auscultatory sound waveform or for acquiring a specified auscultatory sound waveform. This makes it possible to round off the measurement variation.

[0012] The auscultation system of the fifth invention is an auscultation system of any of the second to fourth inventions, characterized in that the control unit creates energy trend data by adding up the effective value of the auscultation sound waveform, or the sum or root mean square of all frequency energy after STFT (Short-time Fourier transform).

[0013] The auscultation system of the sixth invention is the auscultation system of the third or fourth invention, characterized in that the control unit uses data obtained by subjecting the auscultation sound waveform to FFT (Fast Fourier transform) or MFCC (Mel-Frequency Cepstrum Coefficients) as data based on the auscultation sound waveform.

[0014] The auscultation system of the seventh invention is characterized in that, in the auscultation system of any of the first to sixth inventions, the predetermined auscultation sound waveform is a waveform based on the auscultation sound of a user whose auscultation sound is collected.

[0015] The auscultation system of the eighth invention is the auscultation system of the seventh invention, characterized in that the control unit notifies the patient to visit a medical institution when the collected auscultation sound waveform changes from a predetermined auscultation sound waveform.

[0016] The auscultation system of the 9th invention is characterized in that, in the auscultation system of any of the 1st to 6th inventions, the specified auscultation sound waveform is a waveform of an auscultation sound that has common characteristics among multiple people and indicates an abnormal physical condition.

[0017] The auscultation system of a tenth aspect of the present invention is the auscultation system of the first aspect of the present invention, characterized in that the control unit notifies the determined physical condition.

[0018] An auscultation system of an eleventh aspect of the present invention is the auscultation system of the first aspect of the present invention, characterized in that the control unit determines whether or not it is necessary to visit a medical institution based on the determined physical condition.

[0019] The auscultation system of a twelfth aspect of the present invention is the auscultation system of the eleventh aspect of the present invention, characterized in that the control unit notifies the user of the judged need to visit a medical institution or not.

[0020] The auscultation system of the 13th invention is characterized in that, in the auscultation system of any of the first to 12th inventions, the specified auscultation sound waveform is acquired by creating a learning model through machine learning.

[0021] The auscultation system of the 14th invention is characterized in that, in the auscultation system of the first invention, the specified auscultation sound waveform is a waveform based on the auscultation sound of the user from whom the auscultation sound is collected and a waveform of auscultation sound indicating an abnormal physical condition having common characteristics among multiple people, and the control unit compares the collected auscultation sound waveform with the waveform based on the auscultation sound of the user from whom the auscultation sound is collected, and if the collected auscultation sound waveform has changed compared to the waveform based on the auscultation sound of the user from whom the auscultation sound is collected, compares the collected auscultation sound waveform with the waveform of auscultation sound indicating an abnormal physical condition having common characteristics among multiple people.

[0022] The auscultation system of the 15th invention is the auscultation system of the 14th invention, characterized in that the control unit alerts a physical abnormality when the collected auscultation sound waveform is similar to an auscultation sound waveform that has common characteristics among multiple people and indicates an abnormal physical condition.

[0023] The auscultation system of the 16th invention is characterized in that, in the auscultation system of the 14th or 15th invention, the control unit notifies the patient to visit a medical institution when the collected auscultation sound waveform is similar to auscultation sound waveforms that have common characteristics among multiple people and indicate an abnormal physical condition.

[0024] The stethoscope of the 17th invention is characterized in that it comprises a sensor for collecting auscultatory sounds and a control unit, and the control unit compares the collected auscultatory sound waveform collected by the sensor with a predetermined auscultatory sound waveform, and judges the physical condition based on the comparison result.

[0025] The method of the 18th invention is characterized in that it compares an auscultatory sound waveform collected by a sensor with a predetermined auscultatory sound waveform, and judges the physical condition based on the comparison result.

[0026] According to the present invention, a user can easily determine changes in his / her physical condition. [Brief description of the drawings]

[0027] [Figure 1]FIG. 2 is a diagram showing an example of a heart sound waveform. [Diagram 2] 1A to 1C are diagrams illustrating an example of an actual waveform, STFT data, and energy trend data. [Diagram 3] FIG. 11 is a diagram illustrating an example of energy trend data. [Figure 4] 1A is a diagram for explaining how to cut out a waveform, and FIG. 1B is a diagram after signal processing. [Diagram 5] FIG. 1C is a diagram for explaining classification and learning model creation using machine learning. [Figure 6] FIG. 13 is a diagram for explaining classification determination using a learning model. [Figure 7] 4 is a flowchart showing the processing operation of the auscultation system. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0028] Hereinafter, an embodiment of the present invention will be described. An auscultation system according to an embodiment of the present invention is composed of, for example, a stethoscope and a smartphone. In the auscultation system, auscultation sounds (e.g., heart sounds) collected by the stethoscope are transmitted to the smartphone, and the smartphone performs analysis of the auscultation sounds. Note that the auscultation system may be composed of only a stethoscope. Furthermore, the device that performs analysis of the auscultation sounds does not have to be a smartphone, and may be a dedicated device or the like.

[0029] The stethoscope is provided with a sensor for collecting auscultatory sounds on the underside, which is the contact surface that comes into contact with the human body from which the auscultatory sounds are collected. The sensor is, for example, composed of a diaphragm and a piezoelectric element attached to the diaphragm. The sensor is, for example, composed of a diaphragm and a microphone. The sensor is, for example, a microphone. The auscultatory sounds collected by the sensor are transmitted to a smartphone.

[0030] The smartphone includes a control unit such as a CPU (Central Processing Unit). The processes described below are basically executed by the control unit, but the wording "the control unit" may be omitted in the process of "the control unit performs." The control unit of the smartphone compares auscultatory sound waveforms (hereinafter also referred to as "test data") collected by a sensor and transmitted from a stethoscope with a predetermined auscultatory sound waveform, and judges the physical condition based on the comparison result. The predetermined auscultatory sound waveform is, for example, data acquired by machine learning (hereinafter also referred to as "learning data"). The learning data is, for example, a waveform based on auscultatory sounds collected in the past by the user whose auscultatory sounds are collected. If the test data has changed with respect to the learning data, the control unit judges that the physical condition has changed, and, for example, notifies the user of a recommendation to visit a medical institution via a display screen of the smartphone. At this time, the control unit may notify that the physical condition has changed.

[0031] The learning data is a waveform of auscultatory sounds that have common characteristics among multiple people and indicate an abnormal physical condition. For example, if the auscultatory sounds are heart sounds, the learning data is a waveform of a typical heart disease. If the test data is similar to the learning data, the control unit determines that there is a suspicion of heart disease, and, for example, issues a notification via a display screen of a smartphone encouraging the user to visit a medical institution. At this time, the control unit may notify the user of the name of the suspected heart disease. In this way, the control unit determines the physical condition, and determines whether or not the user needs to visit a medical institution based on the determined physical condition. The control unit may be configured to notify the user of this even if it determines that a visit to a medical institution is not necessary.

[0032] The various notifications may be made on the display screen of the smartphone or by other means. Also, the notifications may be made by "sound."

[0033] Next, we will explain the method for incorporating auscultatory sounds into machine learning. The following steps are used to synchronize heart sounds (auscultatory sounds) and grasp their intervals, extract the target heart sounds, and compare them using machine learning.

[0034] (1) The heart sound waveform is collected by a sensor. Figure 1 shows an example of the heart sound waveform. (2) The effective value of the waveform or the energy (sum or root mean square) of all frequencies after STFT (Short-time Fourier transform) is added to generate energy trend data showing the transition of amplitude versus time. Figure 2 shows an example of the real waveform, STFT data, and energy trend data. Figure 2 shows the real waveform, STFT data, and energy trend data in the areas of the dashed line and the dashed dotted line in Figure 1. (3) Mark the peaks of the energy trend data, and obtain the time position of heartbeat synchronization and the heartbeat period T from the interval between the marks. Figure 3 shows an example of energy trend data. The figure also shows heart sounds collected from the sensor, with a large peak at the first sound. (4) For the section in which the period was measured, cut out the phonocardiogram waveform with a time of 1 / 2 the period T before and after the peak. Figure 4(a) is a diagram for explaining how the waveform is cut out. (5) The heart sounds in the cut out section are acquired as learning data or test data in the form of FFT (Fast Fourier transform), MFCC (Mel-Frequency Cepstrum Coefficients), or in the form of the waveform itself. Alternatively, since heart sounds are periodic signals, a predetermined number of (e.g., 10) waveforms are overwritten to reduce measurement variability, and the data is acquired as learning data or test data. Figure 4(b) shows the result after signal processing. Figure 5(c) shows a diagram for explaining classification by machine learning and creation of a learning model. (6) For example, the past healthy data collected in (5) is used as learning data, and heart sounds collected periodically (for example, daily) are used as test data. (7) For example, if a significant change occurs between the heart sounds (test data) collected daily and the learning data, the similarity with the learning data is lost and it is judged as an abnormality. Figure 6 shows a diagram for explaining classification judgment using a learning model. (8) If there is no similarity, an abnormality is suspected and the similarity between the heart sounds (test data) collected daily and the learning model of abnormal heart sound waveforms (learning data) is checked. If there is a similarity, the system displays (notifies) the patient of the nearby heart disease and prompts the patient to see a doctor. (9) If there is no similarity between the desired waveform and the abnormal waveform, an abnormality flag is recorded and a doctor is prompted to make a diagnosis.

[0035] The processing operation of the auscultation system will be described below with reference to the flowchart shown in FIG. 7. First, the control unit judges whether or not the mode is the initial learning mode (S1). The initial learning mode is a mode for learning auscultation sounds (e.g., heart sounds) when the user is healthy. If the control unit judges that the mode is the initial learning mode (S1: Yes), it acquires auscultation sounds from a sensor (S2). Next, the control unit preprocesses the acquired auscultation sound waveform, and acquires auscultation sound waveform data that has been preprocessed, or auscultation sound waveform data that has not been preprocessed without preprocessing (S3). Next, the control unit learns the auscultation sound waveform data acquired in S3 (S4). Next, the control unit judges whether or not a prescribed number of learnings have been completed (S5). If the control unit judges that the prescribed number of learnings has not been completed (S5: No), it performs the process of S2. If the control unit judges that the prescribed number of learnings has been completed (S5: Yes), it ends the initial learning mode.

[0036] When the control unit determines that the mode is not the initial learning mode (S1: No), it acquires auscultatory sounds from the sensor (S6). Next, the control unit performs preprocessing on the acquired auscultatory sound waveform and acquires preprocessed auscultatory sound waveform data (test data), or does not perform preprocessing and acquires non-preprocessed auscultatory sound waveform data (test data) (S7). Next, the control unit reads out the learning data (S8). The learning data here is a waveform (when healthy) based on auscultatory sounds previously collected by the user whose auscultatory sounds are to be collected. In other words, it is learning data learned in the initial learning mode.

[0037] Next, the control unit determines whether there is a significant difference between the test data and the learning data (S9). If the control unit determines that there is no significant difference between the test data and the learning data (S9: No), it ends the process. If the control unit determines that there is a significant difference between the test data and the learning data (S9: Yes), it sets an abnormality flag (S10). Next, the control unit reads out the learning data (S11). The learning data here is auscultatory sound waveforms that have common characteristics among multiple people and indicate an abnormal physical condition (e.g., waveforms of typical heart disease).

[0038] Next, the control unit judges whether the test data and the learning data are similar, for example, whether the test data is similar to a waveform of a typical heart disease (S12). If the control unit judges that the test data and the learning data are similar, for example, that the test data is similar to a waveform of a typical heart disease (S12: Yes), it records the similar disease name (S13), displays a recommendation to visit a medical institution (S14), and ends the process. If the control unit judges that the test data and the learning data are not similar, for example, that the test data is not similar to a waveform of a typical heart disease (S12: No), it displays a recommendation to visit a medical institution (S14), and ends the process.

[0039] When the auscultation system is composed of only a stethoscope, the stethoscope is provided with a control unit such as a CPU, and the control unit performs the above-mentioned processes including the processing operations shown in FIG.

[0040] As described above, in this embodiment, the control unit compares the collected auscultatory sound waveform picked up by the sensor with a predetermined auscultatory sound waveform, and judges the physical condition based on the comparison result. For example, if the past auscultatory sound waveform of the user from which the auscultatory sound is collected is used as the "predetermined auscultatory waveform," a comparison can be made with the auscultatory sound waveform related to the user's body in the past, and a change in the current physical condition relative to the past physical condition can be judged. In this way, the change in the current physical condition relative to the past physical condition is judged, and the user can easily judge the change in the physical condition based on the judgment result.

[0041] Furthermore, when the auscultatory sound waveform is, for example, a cardiac waveform, it is a periodic signal, and therefore there is measurement variation. For this reason, in this embodiment, the control unit uses auscultatory sound waveform data in a section of 1 / 2 of one cycle around the peak of the energy trend data, or data based on the auscultatory sound waveform, overwritten a specified number of times, as the collected auscultatory sound waveform, or for acquiring a specified auscultatory sound waveform. This makes it possible to round off the measurement variation. Please note that WO2018 / 117171 does not state or suggest the use of "data overwritten a specified number of times."

[0042] Although an embodiment of the present invention has been described above, the forms to which the present invention can be applied are not limited to the above-described embodiment, and appropriate modifications can be made without departing from the spirit of the present invention. [Industrial Applicability]

[0043] The present invention can be suitably adopted in an auscultation system, a stethoscope, and a method.

Claims

1. A sensor for collecting auscultatory sounds; A control unit, The control unit is Determine whether the device is in initial learning mode; If it is determined that the initial learning mode is selected, One period is cut out from the auscultatory sound waveform for creating a learning model, creating the learning model by machine learning from learning data based on the one cycle of the extracted auscultatory sound waveform for the learning model; If it is determined that the initial learning mode is not selected, One period is cut out from the collected auscultatory sound waveform collected by the sensor, comparing test data based on the collected auscultatory sound waveform of one cut cycle with the learning model; An auscultation system that determines a physical condition based on a comparison result.

2. The control unit is performing a fast Fourier transform on the one cycle of the auscultatory sound waveform for the learning model, or calculating Mel-frequency cepstrum coefficients from the one cycle of the auscultatory sound waveform for the learning model, and using these as the learning data; The auscultation system according to claim 1, characterized in that the test data is obtained by subjecting the extracted one cycle of the collected auscultatory sound to a fast Fourier transform or by calculating Mel-frequency cepstrum coefficients from the extracted one cycle of the collected auscultatory sound.

3. The control unit is The extracted one cycle of the auscultatory sound waveform for the learning model is used as the learning data.

2. The auscultation system according to claim 1, wherein the test data is a cut cycle of the collected auscultatory sound waveform.

4. The control unit is The extracted one cycle of the auscultatory sound waveform for the learning model is overwritten a predetermined number of times to obtain the learning data.

2. The auscultation system according to claim 1, wherein the test data is obtained by overwriting the collected auscultatory sound waveform of one cycle that has been cut out a predetermined number of times.

5. The control unit is Calculation of the effective value of the waveform, Calculating the sum of all frequencies of the waveform obtained by short-time Fourier transform; Calculating the root mean square of all frequencies of the short-time Fourier transformed waveform, By carrying out any one of the above, energy trend data showing the transition of amplitude versus time is created based on the auscultatory sound waveform and the collected auscultatory sound waveform, Mark the peaks of the energy trend data based on the created auscultatory sound waveform, and obtain the period of the auscultatory sound waveform from the interval between the marks. The auscultation system according to any one of claims 1 to 4, characterized in that it marks the peaks of energy trend data based on the created collected auscultatory sound waveform, and obtains the period of the collected auscultatory sound waveform from the interval between the marks.

6. The control unit is a period is extracted from the auscultatory sound waveform for the learning model by extracting the auscultatory sound waveform for the learning model in a section of 1 / 2 of one period before and after a peak of energy trend data showing a transition of amplitude versus time based on the auscultatory sound waveform for the learning model; The auscultation system according to any one of claims 1 to 4, characterized in that one cycle is extracted from the collected auscultation sound waveform by extracting the collected auscultation sound waveform in a section that is 1 / 2 of one cycle before and after the peak of energy trend data based on the collected auscultation sound waveform.

7. The auscultation system according to any one of claims 1 to 6, characterized in that the auscultation sound waveform for the learning model is a waveform based on the auscultation sound of a user whose auscultation sound is collected.

8. The auscultation system according to claim 1 , wherein the control unit notifies the user to visit a medical institution when the test data has changed relative to the learning model.

9. The auscultation system according to any one of claims 1 to 6, characterized in that the auscultation sound waveform for the learning model is a waveform of an auscultation sound that has common characteristics among multiple people and indicates an abnormal physical condition.

10. The auscultation system according to claim 1 , wherein the control unit notifies the determined physical condition.

11. 2. The auscultation system according to claim 1, wherein the control unit determines whether or not the patient needs to visit a medical institution based on the determined physical condition.

12. The auscultation system according to claim 11, wherein the control unit notifies the user of the judged necessity of visiting a medical institution.

13. A sensor for collecting auscultatory sounds; A control unit, The control unit is Determine whether the device is in initial learning mode; When it is determined that the initial learning mode is selected One period is cut out from the auscultatory sound waveform for creating a learning model, creating the learning model by machine learning from learning data based on the one cycle of the extracted auscultatory sound waveform for the learning model; If it is determined that the initial learning mode is not selected, One period is cut out from the collected auscultatory sound waveform collected by the sensor, comparing test data based on the collected auscultatory sound waveform of one cut cycle with the learning model; A stethoscope that determines a physical condition based on the comparison results.

14. By computer, Determine whether the device is in initial learning mode; If it is determined that the initial learning mode is selected, One period is cut out from the auscultatory sound waveform for creating a learning model, creating the learning model by machine learning from learning data based on the one cycle of the extracted auscultatory sound waveform for the learning model; If it is determined that the initial learning mode is not selected, One period is cut from the auscultatory sound waveform collected by the sensor, comparing test data based on the collected auscultatory sound waveform of one cut cycle with the learning model; The method comprises determining a physical condition based on the comparison result.

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