Biosound sensor system

Abstract

To provide a biological sound sensor that can be mounted on the head of a subject easily capable of simultaneously measuring a blood vessel sound and a respiratory sound without giving a feeling of discomfort to a subject and enhancing noise reduction capability.SOLUTION: A biological sound sensor system includes: a biological sound sensor 1 consisting of a main input sensor 5 installed in the vicinity of a lower tip of an extension part 4 extending downward from a holding part 3 that can be mounted on the head of a subject, which is brought into contact with the vicinity of the mastoid, and a reference input sensor 6 installed in the vicinity of an upper part of the extension part 4, and disposed in the vicinity of an auricle: and biological sound signal processing means 2. The biological sound signal processing means 2 executes reduction of noise from the main input using a main input amplitude spectrogram |S(t,ω)| and a reference input amplitude spectrogram |N(t,ω)| in which the main input and the reference input acquired by the biological sound sensor 1 are subjected to STFT, and a multichannel Wiener filter H(t,ω) designed on the basis of |S(t,ω)|2-|N(t,ω)|2 and |N(t,ω)| / |S(t,ω)|.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] This invention relates to a contact-type biosound sensor that can measure vascular sounds and respiratory sounds using a microphone attached to the skin, including narrow spaces such as the inside of the ear, even in noisy environments. [Background technology]

[0002] The proportion of the elderly population in Japan's total population exceeded 10% in 1985, surpassed 20% in 2005, and reached 28.7% in 2020, showing a steady increase year after year. This proportion is expected to continue rising, reaching 35.3% by 2040. National medical expenses were 16.0159 trillion yen in 1985 and reached 43.3949 trillion yen in 2018, increasing year after year in proportion to the elderly population. To reduce medical expenses, self-medication in daily life (taking responsibility for one's own health and treating minor ailments oneself) is necessary. To realize self-medication, the development of wearable devices capable of continuously measuring biometric information is desirable. However, biometric information encompasses various types, such as pulse rate, respiration, blood pressure, and electroencephalogram (EEG), and the measurement methods and corresponding devices are also diverse. Furthermore, if simultaneous measurement of pulse rate and respiration becomes possible, early detection of cardiovascular and respiratory diseases (such as heart disease and pneumonia) can be expected. However, pulse rate and respiration measuring devices installed in medical institutions are difficult for non-medical professionals to operate and are not easily accessible.

[0003] Therefore, the present inventors have developed a contact-type biosound sensor that accurately detects abnormal lung conditions and respiratory sounds by using a microphone attached to a relatively deep position in the external auditory canal, detecting vascular sounds and respiratory sounds transmitted through bone, etc., separating vascular sounds from respiratory sounds, and removing pulse noise from the separated respiratory sound waveform. Furthermore, as described in Non-Patent Document 1 (36th Annual Meeting of the Japan Society for Life Support (LIFE2020-2021), September 16-18, 2021, pp. 168-171), the inventors determined the difference between the squares of the amplitude spectrograms of the main input and the reference input (|S(t,ω)|) based on a main input consisting of biological sounds and noise measured by two sensor units (condenser microphones) attached to the head of the subject, and a reference input consisting of noise. 2 -|N(t,ω)| 2 We found that the signal-to-noise ratio can be improved by using a multi-channel Wiener filter that employs an adaptive margin m(t,ω) determined by ). Furthermore, Patent Document 3 (Japanese Patent No. 6226301) describes a system having a first microphone (11) that outputs a main signal x(t) and a second microphone (12) that outputs a reference signal r1(t), converting the signals from the time domain to the frequency domain using the main signal x(t) and reference signal r1(t) as inputs, and outputting the main signal spectrum X(ω) and the reference signal spectrum R1(ω), and when the main signal spectrum X(ω) and the reference signal spectrum R1(ω) are inputs... The documentation describes how to output the main signal power spectrum Px(ω) and the second reference signal power spectrum Pr2(ω), how the noise suppression coefficient calculation unit (108B) outputs the noise suppression coefficient H(ω), and how to calculate the weighting coefficient α(ω) which corresponds to the time average of the spectral ratio Px(ω) / Pr1'(ω) using Px(ω) and the power spectrum Pr1'(ω) (see paragraphs 0042-0054, 0173-0179 and Figures 1, 2, and 12 in particular). [Prior art documents] [Patent Documents]

[0004] [Patent Document 1] Japanese Patent Publication No. 2020-121120 [Patent Document 2] Japanese Patent Publication No. 2021-074464 [Patent Document 3] Japanese Patent Publication No. 6226301 (International Publication No. 2014 / 097637) [Non-patent literature]

[0005] [Non-Patent Document 1] Katsuma Fujimoto, Mikiya Tanaka, Sotaro Shimada, Seiji Nishiwaki, Shota Nakajima, "Noise Reduction Method for Biological Sounds by Multi-channel Wiener Filter with Adaptive Margin", The 36th Annual Conference of Life Support Society (LIFE2020-2021), September 16-18, 2021, pp.168-171 [Summary of the Invention] [Problem to be Solved by the Invention]

[0006] All of the biological sound sensors described in Patent Documents 1 and 2 are worn in the external auditory canal to simultaneously measure vascular sounds and respiratory sounds, and separate the vascular sounds and respiratory sounds. They do not consider noise removal, and since they are worn in the external auditory canal, they tend to cause discomfort to the subject. Non-Patent Document 1 describes a noise reduction method based on a main input and a reference input measured by sensor units located at two positions. The adaptive margin m(t,ω) used in the multi-channel Wiener filter is, as described in the last line of the right column on p.169, (|S(t,ω)| 2 -|N(t,ω)| 2 ), it is only 3 or 100 determined thereby, so there are cases where the noise reduction capability is inferior. Furthermore, the noise reduction method described in Patent Document 3 calculates a weighting factor α(ω) corresponding to the time average of the spectral ratio Px(ω) / Pr1'(ω). Therefore, when there is a difference between the noise signal corrected by calculation and the noise level mixed into the input, the optimal weighting factor α(ω) for extracting the target sound cannot be determined, and a sufficient noise reduction effect may not be obtained. The present invention aims to solve these problems, and a first object of the present invention is to simultaneously measure vascular sounds and respiratory sounds without causing discomfort to a subject, and to improve noise reduction capability. A second object of the present invention is to provide a biological sound sensor that mainly holds a sensor for measuring vascular sounds and respiratory sounds and a sensor for measuring noise, and can be easily worn on the head of a subject. [Means for Solving the Problem]

[0007] The biological sound sensor system according to the invention of claim 1 is: a biological sound sensor consisting of a main input sensor that mainly measures vascular sounds and respiratory sounds to obtain a main input, and a reference input sensor that mainly measures noise to obtain a reference input; a Wiener filter designed from the main input acquired by said main input sensor and the reference input acquired by said reference input sensor, for reducing noise from the main input; said main input sensor is configured to be brought into contact with the vicinity of the mastoid process of a subject; said reference input sensor is configured to be disposed above said main input sensor and in the vicinity of the auricle of said subject; said Wiener filter is a multi-channel Wiener filter H(t,ω) designed according to the following formula (1) using a main input amplitude spectrogram |S(t,ω)| obtained by performing short-time Fourier transform on said main input, a reference input amplitude spectrogram |N(t,ω)| obtained by performing short-time Fourier transform on said reference input, and an adjustment margin α(t,ω); said adjustment margin α(t,ω) is determined by the following formula (2-1') when a value obtained by subtracting the square of said reference input amplitude spectrogram |N(t,ω)| from the square of said main input amplitude spectrogram |S(t,ω)| is greater than 0, and is determined by the following formula (2-2') when a value obtained by subtracting the square of said reference input amplitude spectrogram |N(t,ω)| from the square of said main input amplitude spectrogram |S(t,ω)| is less than or equal to 0: H(t,ω)=|S(t,ω)| 2 / (|S(t,ω)| 2 +α(t,ω)|N(t,ω)| 2 )···Formula (1) α(t,ω)=K1(|N(t,ω)| / |S(t,ω)|)···········Formula (2-1') α(t,ω)=K2(|N(t,ω)| / |S(t,ω)|)···········Formula (2-2') provided that (t,ω) represents a component at each time t and angular frequency ω, and satisfies all of the following conditions: K1<K2, 10≦K1<30 and 10<K2≦30. Further, the invention according to claim 2 is characterized in that A biosound sensor consisting of a primary input sensor that mainly measures vascular sounds and respiratory sounds to acquire the primary input, and a reference input sensor that mainly measures noise to acquire the reference input, It is designed from the main input acquired by the main input sensor and the reference input acquired by the reference input sensor, and includes a Wiener filter that reduces noise from the main input. The aforementioned main input sensor is placed in contact with the area near the mastoid process of the subject. The reference input sensor is positioned above the main input sensor and near the subject's auricle. The Wiener filter is a multi-channel Wiener filter H(t,ω) designed using the following equation (1), with respect to the main input amplitude spectrogram |S(t,ω)| obtained by applying a short-time Fourier transform to the main input, the reference input amplitude spectrogram |N(t,ω)| obtained by applying a short-time Fourier transform to the reference input, and the adjustment margin α(t,ω). the adjustment margin α(t,ω) is 1-|N(t,ω)| 2 / |S(t,ω)| 2 is determined by the following formula (4-1') when the value thereof is greater than 0.36; 1-|N(t,ω)| 2 / |S(t,ω)| 2 is determined by the following formula (4-2') when the value thereof is less than or equal to 0.36 and greater than -1.25; 1-|N(t,ω)| 2 / |S(t,ω)| 2 is determined by the following formula (4-3') when the value thereof is less than or equal to -1.25. H(t,ω)=|S(t,ω)| 2 / (|S(t,ω)| 2 +α(t,ω)|N(t,ω)| 2 )...Equation (1) α(t,ω)=L1(|N(t,ω)| / |S(t,ω)|)···········Formula (4-1') α(t,ω)=L2(|N(t,ω)| / |S(t,ω)|)···········Formula (4-2') α(t,ω)=L3(|N(t,ω)| / |S(t,ω)|)···········Formula (4-3') provided that L1<L2<L3, and all of the following conditions are satisfied: 10≦L1<20, 10<L2<30 and 20<L3≦30.

[0008] Claim 3 the invention according to is the biological sound sensor system according to claim 1 or 2 , wherein The biosound sensor includes a retaining part that can be detachably attached to the subject's head and an extension part that extends downward from the retaining part, so that the main input sensor is in contact with the vicinity of the subject's mastoid process, and the reference input sensor is positioned above the main input sensor and near the subject's auricle. The main input sensor is installed near the lower end of the extension, The reference input sensor is characterized by being installed near the upper part of the extension.

[0009] Claim 4 The invention relating to this is as described in claim 1 or 2 In the biosound sensor system described above, The biosound sensor is equipped with a left ear hook and a right ear hook that can be detachably attached to the left and right ears of the subject, and a connecting part that connects the rear ends of the left ear hook and the right ear hook, so that the main input sensor is in contact with the vicinity of the subject's mastoid process, and the reference input sensor is positioned above the main input sensor and near the subject's auricle. The main input sensor is installed near the rear end of the left or right ear hook. The reference input sensor is characterized in that it is installed near the upper part of the left ear hook or the right ear hook. [Effects of the Invention]

[0010] According to the biosound sensor system of the invention described in claim 1, the main input sensor is placed in contact with the area near the mastoid process of the subject, so that vascular sounds and respiratory sounds can be measured without causing discomfort to the subject. Furthermore, the Wiener filter that performs noise reduction from the main input is a multi-channel Wiener filter H(t,ω) designed in equation (1) using the main input amplitude spectrogram |S(t,ω)|, the reference input amplitude spectrogram |N(t,ω)|, and the adjustment margin α(t,ω). The adjustment margin α(t,ω) is determined by equation (2-1') when the difference between the square of the main input amplitude spectrogram |S(t,ω)| and the square of the reference input amplitude spectrogram |N(t,ω)| is greater than 0, and by equation (2-2') when the difference between the square of the main input amplitude spectrogram |S(t,ω)| and the square of the reference input amplitude spectrogram |N(t,ω)| is less than or equal to 0. Therefore, the adjustment margin, which is a parameter that adjusts the amount of noise reduction, can be dynamically changed, resulting in a better signal-to-noise ratio (hereinafter referred to as "SNR") compared to conventional methods and improving noise reduction capability. Furthermore, according to the biosound sensor system of the invention described in claim 2, Since the main input sensor is placed in contact with the vicinity of the subject's mastoid process, vascular sounds and respiratory sounds can be measured without causing discomfort to the subject. Furthermore, the Wiener filter used to reduce noise from the main input is a multi-channel Wiener filter H(t,ω) designed using equation (1) with the main input amplitude spectrogram |S(t,ω)|, the reference input amplitude spectrogram |N(t,ω)|, and the adjustment margin α(t,ω). The adjustment margin α(t,ω) is 1-|N(t,ω)| 2 / |S(t,ω)| 2 If the value of is greater than 0.36, it is determined by equation (4-1') and 1-|N(t,ω)| 2 / |S(t,ω)| 2 If the value is 0.36 or less and greater than -1.25, it is determined by equation (4-2') and 1-|N(t,ω)| 2 / |S(t,ω)| 2 If the value of is -1.25 or less, it is determined by equation (4-3'), so the discontinuity of α(t,ω) can be reduced.

[0011] Claim 3 According to the biological sound sensor system of the invention relating to claim 1 or 2In addition to the effects of the invention described above, the device is equipped with a retaining part that can be detachably attached to the subject's head and an extension part that extends downward from the retaining part. The main input sensor is installed near the lower end of the extension part, and the reference input sensor is installed above the extension part. Therefore, the main input sensor and the reference input sensor can be easily attached to the subject's head.

[0012] Claim 4 According to the biological sound sensor system of the invention relating to claim 1 or 2 In addition to the effects of the invention described above, the device is equipped with a left ear hook and a right ear hook that can be detachably attached to the left and right ears of the subject, as well as a connecting part that connects the rear ends of the left and right ear hooks. The main input sensor is installed near the rear end of the left or right ear hook, and the reference input sensor is installed near the upper part of the left or right ear hook. Therefore, the main input sensor and the reference input sensor can be easily attached to the subject's head. [Brief explanation of the drawing]

[0013] [Figure 1] Conceptual diagram of the biosound sensor system according to Example 1. [Figure 2] This figure shows the biosound sensor 1 of Example 1 attached to the head of a subject. [Figure 3] A cross-sectional view showing the structure of the main input sensor 5 and the reference input sensor 6. [Figure 4] Graph of the adjustment margin α(t,ω) and the adaptive margin m(t,ω) from Non-Patent Document 1. [Figure 5] A table showing the SNR after noise reduction processing for each margin. [Figure 6] This figure shows the biosound sensor 12 of Example 2 attached to the head of the subject. [Figure 7] This figure shows the biosound sensor 17 of Example 3 attached to the head of the subject. [Modes for carrying out the invention]

[0014] Embodiments of the present invention will be described below with reference to examples. [Examples]

[0015] Figure 1 is a conceptual diagram of the biosound sensor system according to Example 1, and Figure 2 shows the biosound sensor 1 of Example 1 attached to the head of a subject. As shown in Figure 1, the biosound sensor system according to Example 1 comprises a biosound sensor 1 attached to the head of the subject, and a biosound signal processing means 2 that processes the biosound acquired by the biosound sensor 1 to reduce noise and outputs the subject's vascular sound waveform and respiratory sound waveform. The biosound sensor 1 includes a U-shaped elastic holding part 3 that can be detachably attached to the head of the subject, two elastic extension parts 4 that extend downward from the holding part 3, a main input sensor 5 installed near the lower tip of the extension part 4, and a reference input sensor 6 installed facing outward near the upper part of the extension part 4 (in Figure 1, in front of the upper end of the extension part 4). As shown in Figure 2, the holding part 3 of the biosound sensor 1 can be fixed to the back and sides of the subject's head by elastic force, and the main input sensor 5, which is installed near the lower end of the extension part 4, is positioned so as to contact the area around the subject's mastoid process and be pressed slightly inward by elastic force when the holding part 3 is fixed to the subject's head. The mastoid process is a cone-shaped protrusion located in the posterior inferior part of the temporal bone. By bringing the main input sensor 5 into contact with the area around the mastoid process, vascular sounds and respiratory sounds can be measured simultaneously, and this does not cause discomfort to most subjects. Furthermore, since the size of the subject's head and the position of the mastoid process vary, several types of holding parts 3 and extension parts 4 of different sizes are prepared. Seven to ten holes are provided on the front side of the holding part 3, and the upper end of the extension part 4 can be screwed into any of these holes.

[0016] Figure 3 is a cross-sectional view showing the structure of the main input sensor 5 and the reference input sensor 6. The main input sensor 5 and the reference input sensor 6 are both acoustic sensors that utilize an electret condenser microphone (hereinafter referred to as "ECM") 7, as shown in Figure 3(B). As shown in Figure 3(A), they consist of an ECM 7, an acoustic conduction section 8 made of polyurethane elastomer, and a case 9 made of photocurable resin. The ECM 7 is housed in a housing 10 and has a wave-receiving surface 11, which is the surface of the diaphragm that detects sound. Furthermore, the housing 10 is housed inside a case 9 made of photocurable resin, and the surface of the wave receiving surface 11 and the periphery of the housing 10 are covered with an acoustic conduction section 8 made of polyurethane elastomer. The reason for covering the surface of the wave-receiving surface 11 with the acoustic conduction section 8 is that if biological sound is acquired by bringing the ECM7 directly close to the surface of the human body, the biological sound will propagate through the air, which has different acoustic properties than the human body, and will be reflected and attenuated at the interface. In order to propagate biological sound to the ECM7 without attenuation, it is best to fill the space between the surface of the human body and the surface of the wave-receiving surface 11 with a medium that has the same acoustic properties as the human body. Therefore, in Example 1, the surface of the wave receiving surface 11 is covered with a polyurethane elastomer having acoustic properties equivalent to those of human skin. By doing so, bio-sound acquired from around the mastoid process can be accurately propagated to the ECM 7, especially in the main input sensor 5. Then, by positioning the main input sensor 5 and the reference input sensor 6 on the subject's head as shown in Figure 2, the main input sensor 5 can primarily measure vascular sounds and respiratory sounds, while the reference input sensor 6 can primarily measure noise. Note that since the reference input sensor 6 is used without contact with the subject, the ECM 7 alone may also be used.

[0017] Next, the biological sound processing method in the biological sound signal processing means 2 will be described. As shown in Figure 1, first, the main input signal acquired by the main input sensor 5 and the reference input signal acquired by the reference input sensor 6 are transmitted to the biosound signal processing means 2 via wired or wireless transmission means. The biosound signal processing means 2 then processes the main input signal and the reference input signal according to the following procedure, performs noise reduction on the main input signal, and then performs signal separation using conventional techniques to acquire highly accurate vascular sound waveforms and respiratory sound waveforms. (Procedure 1) A short-time Fourier transform (hereinafter referred to as "STFT") is performed on the received main input signal and the reference input signal to obtain the main input amplitude spectrogram (|S(t,ω)|) and the reference input amplitude spectrogram (|N(t,ω)|). (Step 2) Noise reduction is performed from the main input using a multi-channel Wiener filter noise reduction method with margins. Specifically, a multi-channel Wiener filter H(t,ω) designed from |S(t,ω)| and |N(t,ω)| obtained in Step 1 above, in addition to an adjustment margin α(t,ω) for gain adjustment is used. The designed multi-channel Wiener filter H(t,ω) is shown in equation (1) below. Note that (t,ω) represents the components at each time t and angular frequency ω. H(t,ω)=|S(t,ω)| 2 / (|S(t,ω)| 2 +α(t,ω)|N(t,ω)| 2 )...Equation (1) In Example 1, the value obtained by subtracting the square of the reference input amplitude spectrogram from the square of the main input amplitude spectrogram is (|S(t,ω)|). 2 -|N(t,ω)| 2 Depending on the value of , the adjustment margin α(t,ω) is defined by the following equations (2-1) and (2-2). |S(t,ω)| 2 -|N(t,ω)| 2 >0:α(t,ω)=10(|N(t,ω)| / |S(t,ω)|)...Equation (2-1) |S(t,ω)| 2 -|N(t,ω)| 2 ≦0:α(t,ω)=30(|N(t,ω)| / |S(t,ω)|)····Formula (2-2) Then, by designing H(t,ω) using α(t,ω) defined in equations (2-1) and (2-2), and calculating H(t,ω)S(t,ω), noise reduction is performed from the main input, and a reconstructed biological sound X'(t,ω) that approximates the original biological sound X(t,ω) can be obtained. In other words, there is not much noise mixed in |S(t,ω)| 2 -|N(t,ω)| 2 When it is >0, the adjustment margin α(t,ω) changes in the range of 0≦α(t,ω)<10 in proportion to the value of |N(t,ω)| / |S(t,ω)| (see the solid line on the left side of Figure 4), but since α(t,ω) is relatively small, noise is reduced while suppressing the degradation of the main input. Also, when noise is introduced into |S(t,ω)| 2 -|N(t,ω)| 2 When ≤0, the adjustment margin α(t,ω) changes proportionally to the value of |N(t,ω)| / |S(t,ω)| so that 30 ≤ α(t,ω) (see the solid line on the right side of Figure 4), but since α(t,ω) is relatively large, the noise is significantly reduced. In contrast, the adaptive margin m(t,ω) in Non-Patent Document 1 is |S(t,ω)|, as shown by the dotted line in Figure 4. 2 -|N(t,ω)| 2 If it is >0, it is fixed at 3, so when the value of |N(t,ω)| / |S(t,ω)| is close to 1, noise reduction becomes insufficient. Conversely, |S(t,ω)| 2 -|N(t,ω)| 2 Since it is fixed at 100 when ≤ 0, if the value of |N(t,ω)| / |S(t,ω)| is close to 1, excessive noise reduction will occur, degrading the main input.

[0018] (Step 3) Since the biosound X(t,ω) contains both vascular sounds and respiratory sounds, it is necessary to separate them based on the frequency difference between the two. Specifically, the frequency of vascular sounds is around 75-200Hz, and the frequency of respiratory sounds is around 200-2000Hz. Therefore, the reconstructed biosound X'(t,ω) obtained in Step 2 above is processed using a low-frequency bandpass filter to allow only signals with frequencies of 20-100Hz to pass through, and a high-frequency bandpass filter to allow only signals with frequencies of 200-2000Hz to pass through, thereby separating the vascular sounds and respiratory sounds. (Step 4) The vascular sound waveform is obtained by performing an inverse short-time Fourier transform (hereinafter referred to as "ISTFT") on the vascular sound separated in Step 3 above. (Step 5) In the respiratory sounds separated in Step 3 above, a small amount of vascular sound remains due to the difference in sound pressure. Therefore, the residual vascular sound is reduced from the separated respiratory sounds by Harmonic Percussive Sound Separation (HPSS), and then ISTFT is performed to obtain the respiratory sound waveform. (Step 6) The acquired vascular sound waveforms and respiratory sound waveforms are transmitted to a waveform display means, depending on the purpose of their subsequent use. For example, if a doctor or other professional checks these waveforms, they are displayed as vascular sound waveforms or respiratory sound waveforms, or the fluctuations in vascular sound, fluctuations in respiratory sound, or stress index (SI) are calculated and displayed. In addition, if a state determination is made based on the calculated vascular sound and respiratory sound, and the determination result is to be displayed, the data is transmitted to a vascular sound / respiratory sound calculation and display means.

[0019] Next, we conducted an experiment to verify the noise reduction effect of a multi-channel Wiener filter using the adjustment margin α(t,ω) from Example 1. The experimental procedure and results will be described below. In this experiment, the SNR defined by equation (3) below was used to evaluate the noise reduction effect. SNR=20log 10 [max(|s(t)|) / max(|n(t)|)]...Equation (3) However, s(t) is the time signal in which only biological sounds are observed, and n(t) is the time signal in which only noise is observed. The experimental method will be explained step by step below. (Experimental Procedure 1) Ten subjects (Subjects A-J) ranging in age from their 20s to 80s were tested. In a quiet environment, while sitting in chairs, a biosound sensor 1 was attached to the head of each subject as shown in Figure 2, and biosound was measured for 10 seconds using the main input sensor 5. (Experimental Procedure 2) To verify the effect of noise reduction, two types of noise (a 600Hz sine wave and a male voice) were added to the measured biological sounds. The noise intensity was adjusted to be equal to the maximum amplitude of the breath sound. (Experimental Procedure 3) Noise reduction processing was performed on the sound signal to which two types of noise had been added, using a multi-channel Wiener filter with an adaptive margin m(t,ω) fixed to 3 and a multi-channel Wiener filter with an adjustment margin α(t,ω), as described in Non-Patent Document 1. (Experimental Procedure 4) The SNR of the respiratory sound waveform obtained after noise reduction processing with an adaptive margin m(t,ω)=3 and the respiratory sound waveform obtained after noise reduction processing with an adjustment margin α(t,ω) were calculated. Furthermore, since a high signal-to-noise ratio (SNR) was obtained for vascular sound waveforms without noise reduction processing, we did not perform any further verification. (Experimental Procedure 5) For 20 patterns of sound signals obtained by adding two types of noise to the biological sounds of subjects A to J, the SNR of the respiratory sound waveform obtained after noise reduction processing with an adaptive margin m(t,ω)=3 was compared with the SNR of the respiratory sound waveform obtained after noise reduction processing with an adjustment margin α(t,ω).

[0020] (Experimental results) Figure 5(A) is a table showing the SNR of the respiratory sound waveform obtained after noise reduction processing with an adaptive margin m(t,ω)=3 and the SNR of the respiratory sound waveform obtained after noise reduction processing with an adjustment margin α(t,ω) for the sound signals obtained by adding a sine wave to the biological sounds of subjects A to J. Figure 5(B) is a table showing the SNR with an adaptive margin m(t,ω)=3 and the SNR with an adjustment margin α(t,ω) obtained by similar processing for the sound signals obtained by adding a male voice to the biological sounds of subjects A to J. Here, a higher SNR indicates less noise interference, so a high SNR means a high noise reduction effect, and a low SNR means a low noise reduction effect. As shown in Figures 5(A) and (B), the SNR with adjustment margin α(t,ω) was higher for all subjects than the SNR with adaptive margin m(t,ω)=3. This indicates that the noise reduction effect of the multi-channel Wiener filter using adjustment margin α(t,ω) in Example 1 is higher than the noise reduction effect of the multi-channel Wiener filter with adaptive margin m(t,ω) fixed at 3 in Non-Patent Literature 1. In other words, using the adjustment margin α(t,ω) of Example 1 is effective for noise reduction by a multi-channel Wiener filter, regardless of the type of noise. [Examples]

[0021] Figure 6 shows the biosound sensor 12 of Example 2 attached to the head of the subject. In the biosound sensor 1 of Example 1, the holding part 3 was fixed to the back and sides of the subject's head by elastic force. However, in the biosound sensor 12 of Example 2, the holding part 13 was fixed to the front and sides of the subject's head by elastic force. The main input sensor 15 was installed near the lower end of an elastic extension part 14 that extends downward from the holding part 13, and the reference input sensor 16 was installed facing outward near the upper part of the extension part 14. Furthermore, the basic configuration of the holding unit 13, extension unit 14, main input sensor 15, and reference input sensor 16 is the same as that of the holding unit 3, extension unit 4, main input sensor 5, and reference input sensor 6 in Embodiment 1, so a detailed explanation is omitted. The biosound signal processing means 2 has exactly the same configuration as in Embodiment 1, so its explanation is also omitted. Furthermore, as described above, the retaining part 13 of Example 2 is fixed to the forehead and temporal region of the subject, which has the advantage of being easy to use even for subjects with long hair or those lying supine. [Examples]

[0022] Figure 7(A) shows the external appearance of the left ear hook 18, right ear hook 19, and connection part 20 of the biosound sensor 17 of Example 3, and Figure 7(B) shows the biosound sensor 17 of Example 3 attached to the head of a subject. In the biosound sensor 1 of Example 1 and the biosound sensor 12 of Example 2, the holding parts 3 and 13 were fixed to the subject's head by elastic force. However, in the biosound sensor 17 of Example 3, the left ear hook 18, the right ear hook 19, and the connecting part 20 that connects the rear ends of the left and right ear hooks 18 and 19 can be fixed by placing them on the subject's left and right ears. Therefore, compared to the biosound sensors 1 and 12 of Examples 1 and 2, it takes less time to attach the sensor and positioning is easier, so stable biosounds (vascular sounds and respiratory sounds) can be acquired. The main input sensor is installed facing outwards near the rear end of the left ear hook 18 or the right ear hook 19, and the reference input sensor is installed facing outwards near the upper part of the left ear hook 18 or the right ear hook 19. Furthermore, in the biosound sensor 17 of Example 3, the main input sensor and the reference input sensor are installed in the right ear hook portion 19, so these sensors are not shown in Figure 7(B). Furthermore, the basic configuration of the main input sensor and reference input sensor used in Example 3 is the same as that of the main input sensor 5 and reference input sensor 6 in Example 1, so a detailed explanation will be omitted. The biosound signal processing means 2 has exactly the same configuration as in Example 1, so its explanation will also be omitted.

[0023] The following are examples of modifications of the biosound sensor systems described in Examples 1 to 3. (1) In Examples 1 and 2, U-shaped elastic retaining parts 3 and 13 that can be detachably attached to the subject's head were used, but a ring-shaped elastic retaining part that can be detachably attached around the entire circumference of the subject's head or a cap-shaped retaining part whose circumference can be adjusted may also be used. (2) In Examples 1 and 2, the biosound sensor 1 had two elastic extensions 4 that extended downward from the left and right sides of the holding part 3, but only one extension 4 may be provided. (3) In Examples 1 and 2, the main input sensors 5 and 15 and the reference input sensors 6 and 16 were placed only on the left side of the subject, but they may be placed only on the right side or on both sides. Furthermore, in Example 3, the main input sensor and the reference input sensor were installed only on the right ear hook portion 19, but they may also be installed only on the left ear hook portion 18 or on both ear hook portions 18 and 19. (4) In Examples 1 and 2, the reference input sensor 6 was installed facing outward at the front of the upper end of the extension 4, but the installation position may be at the rear, top, or bottom of the upper end of the extension 4, and the orientation may be at the inward, forward, backward, upward, or downward. In Example 3, the reference input sensor was installed facing outward near the top of the right ear hook portion 19, but it can be facing inward, forward, backward, upward, or downward.

[0024] (5) In Examples 1 to 3, ECMs were used for the main input sensors 5 and 15 and the reference input sensors 6 and 16, but conventional condenser type, moving coil type, piezoelectric type microphones may be used instead of ECMs. (6) In Examples 1 to 3, the surface of the wave receiving surface 11 and the periphery of the housing 10 were covered with an acoustic conduction part 8 made of polyurethane elastomer. However, it is not limited to polyurethane elastomer; medical-grade silicone may also be used, and any suitable elastic polymer material can be used as long as it is a hydrophobic resin that has acoustic impedance characteristics equivalent to those of human skin after curing. (7) In Examples 1-3, the multi-channel Wiener filter H(t,ω) was designed using the adjustment margin α(t,ω) defined by equations (2-1) and (2-2), but |S(t,ω)| 2 -|N(t,ω)| 2 It would be better to reduce the discontinuity in α(t,ω) or to connect α(t,ω) seamlessly by dividing the value into three or more stages. For example, α(t,ω) can be defined by the following equations (4-1) to (4-3). 1-|N(t,ω)| 2 / |S(t,ω)| 2>0.36:α(t,ω)=10(|N(t,ω)| / |S(t,ω)|)...Equation (4-1) 0.36≧1-|N(t,ω)| 2 / |S(t,ω)| 2 >-1.25:α(t,ω)=20(|N(t,ω)| / |S(t,ω)|)…Equation (4-2) -1.25≧1-|N(t,ω)| 2 / |S(t,ω)| 2 :α(t,ω)=30(|N(t,ω)| / |S(t,ω)|)...Equation (4-3) (8) In Examples 1 to 3, vascular sounds and respiratory sounds were separated using a low-pass filter and a high-pass filter. However, vascular sounds and respiratory sounds may also be separated using a low-pass filter that allows all signals below a predetermined frequency to pass through and a high-pass filter that allows all signals above a predetermined frequency to pass through. [Explanation of symbols]

[0025] 1. Biosound sensor 2. Biosound signal processing means 3. Holding part 4. Extension part 5. Main input sensors 6. Reference input sensors 7. ECM 8 Acoustic conduction section 9 Case 10 Housing 11 Wave receiving surface 12 Biosound sensor 13 Holding part 14 Extension part 15. Main input sensors 16. Reference input sensors 17 Biosound sensor 18 Left ear hook 19 Right ear hook 20 Connection part ECM Electret Condenser Microphone HPSS Harmonic Percussion Sound Separation ISTFT (Inverse Short-Time Fourier Transform) SNR (Signal-to-Noise Ratio) SI Stress Index (STFT) Short-Time Fourier Transform (t) ω Angular frequency α(t,ω) Adjustment margin H(t,ω) Multi-channel Wiener filter m(t,ω) Adaptive margin |N(t,ω)| Reference input amplitude spectrogram |S(t,ω)| Main input amplitude spectrogram X(t,ω) Biological sound X'(t,ω) Reconstructed biological sound

Claims

1. A biosound sensor consisting of a primary input sensor that mainly measures vascular sounds and respiratory sounds to acquire the primary input, and a reference input sensor that mainly measures noise to acquire the reference input, It is designed from the main input acquired by the main input sensor and the reference input acquired by the reference input sensor, and includes a Wiener filter that reduces noise from the main input. The aforementioned main input sensor is placed in contact with the area near the mastoid process of the subject. The reference input sensor is positioned above the main input sensor and near the subject's auricle. The Wiener filter is a multi-channel Wiener filter H(t,ω) designed using the following equation (1), with respect to the main input amplitude spectrogram |S(t,ω)| obtained by applying a short-time Fourier transform to the main input, the reference input amplitude spectrogram |N(t,ω)| obtained by applying a short-time Fourier transform to the reference input, and the adjustment margin α(t,ω). The adjustment margin α(t,ω) is determined by the following equation (2-1') if the value obtained by subtracting the square of the reference input amplitude spectrogram |N(t,ω)| from the square of the main input amplitude spectrogram |S(t,ω)| is greater than 0, and by the following equation (2-2') if the value obtained by subtracting the square of the reference input amplitude spectrogram |N(t,ω)| from the square of the main input amplitude spectrogram |S(t,ω)| is 0 or less. A biosound sensor system characterized by the following features. H(t,ω)=|S(t,ω)| 2 / (|S(t,ω)| 2 + α(t,ω)|N(t,ω)| 2 )...Equation (1) α(t,ω)=K1(|N(t,ω)| / |S(t,ω)|)・・・・・・・・・・・・Formula (2-1') α(t,ω)=K2(|N(t,ω)| / |S(t,ω)|)・・・・・・・・・・・・Formula (2-2') However, (t,ω) represents the components at each time t and angular frequency ω, satisfying all the conditions K1 < K2, 10 ≤ K1 < 30, and 10 < K2 ≤ 30.

2. A biosound sensor comprising a primary input sensor that primarily measures vascular sounds and respiratory sounds to acquire primary inputs, and a reference input sensor that primarily measures noise to acquire reference inputs, It is designed from the main input acquired by the main input sensor and the reference input acquired by the reference input sensor, and includes a Wiener filter that reduces noise from the main input. The aforementioned main input sensor is placed in contact with the area near the mastoid process of the subject. The reference input sensor is positioned above the main input sensor and near the subject's auricle. The Wiener filter is a multi-channel Wiener filter H(t,ω) designed using the following equation (1), with respect to the main input amplitude spectrogram |S(t,ω)| obtained by applying a short-time Fourier transform to the main input, the reference input amplitude spectrogram |N(t,ω)| obtained by applying a short-time Fourier transform to the reference input, and the adjustment margin α(t,ω). The aforementioned adjustment margin α(t,ω) is 1 - |N(t,ω)| 2 / |S(t,ω)| 2 If the value of is greater than 0.36, it is determined by the following formula (4-1'), and 1-|N(t,ω)| 2 / |S(t,ω)| 2 If the value is 0.36 or less and greater than -1.25, it is determined by the following formula (4-2'), and 1-|N(t,ω)| 2 / |S(t,ω)| 2 If the value is -1.25 or less, it is determined by the following formula (4-3'). A biosound sensor system characterized by the following features. H(t,ω)=|S(t,ω)| 2 / (|S(t,ω)| 2 + α(t,ω)|N(t,ω)| 2 )...Equation (1) α(t,ω)=L1(|N(t,ω)| / |S(t,ω)|)・・・・・・・・・・・・Formula (4-1') α(t,ω)=L2(|N(t,ω)| / |S(t,ω)|)・・・・・・・・・・・・Equation (4-2') α(t,ω)=L3(|N(t,ω)| / |S(t,ω)|)・・・・・・・・・・・・Equation (4-3') However, all of the following conditions are satisfied: L1 < L2 < L3, 10 ≤ L1 < 20, 10 < L2 < 30, and 20 < L3 ≤ 30.

3. The biosound sensor includes a retaining part that can be detachably attached to the subject's head and an extension part that extends downward from the retaining part, so that the main input sensor is in contact with the vicinity of the subject's mastoid process, and the reference input sensor is positioned above the main input sensor and near the subject's auricle. The main input sensor is installed near the lower end of the extension, The reference input sensor is installed near the upper part of the extension. The biosound sensor system according to claim 1 or 2, characterized by the above.

4. The biosound sensor is equipped with a left ear hook and a right ear hook that can be detachably attached to the left and right ears of the subject, and a connecting part that connects the rear ends of the left ear hook and the right ear hook, so that the main input sensor is in contact with the vicinity of the subject's mastoid process, and the reference input sensor is positioned above the main input sensor and near the subject's auricle. The main input sensor is installed near the rear end of the left or right ear hook. The aforementioned reference input sensor is installed near the upper part of the left or right ear hook. The biosound sensor system according to claim 1 or 2, characterized by the above.

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