Evaluation device, evaluation system, evaluation method, and evaluation program for evaluating intestinal condition

Abstract

To accurately evaluate intestinal conditions.SOLUTION: An evaluation device 3 for evaluating intestinal conditions of a subject, comprises: an extraction unit 32 that extracts bowel sound from acoustic data obtained from the subject; a first feature amount calculation unit 331 that calculates a first feature amount being a feature amount of the bowel sound before stimulating the intestines in the extracted bowel sound; a second feature amount calculation unit 332 that calculates a second feature amount being a feature amount of the bowel sound after stimulating the intestines in the extracted bowel sound; and an evaluation unit 34 that evaluates the intestinal conditions of the subject on the basis of the first feature amount and the second feature amount.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to an evaluation device, an evaluation system, an evaluation method, and an evaluation program for evaluating the state of the intestines. [Background technology]

[0002] Techniques for evaluating the state of the intestines based on bowel sounds have been proposed (for example, Patent Document 1). Specifically, Patent Document 1 discloses a method for calculating the amount of gas in the intestines based on time domain features such as the duration and frequency of bowel sounds. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-74238 Summary of the Invention [Problem to be solved by the invention]

[0004] However, even if the condition of the intestines is similar, the duration and frequency of bowel sounds vary from person to person. For example, among subjects with healthy bowel conditions, there are subjects who have a relatively high frequency of bowel sounds and subjects who have a relatively low frequency of bowel sounds. Therefore, the conventional technology described in Patent Document 1 has the problem of being unable to accurately evaluate the condition of the intestines.

[0005] An object of the present disclosure is to accurately evaluate the state of the intestine. [Means for solving the problem]

[0006] In order to solve the above problems, the present disclosure includes the following aspects. Section 1. An evaluation device for evaluating the intestinal condition of a subject, an extraction unit that extracts bowel sounds from acoustic data obtained from the subject; a first feature amount calculation unit that calculates a first feature amount, which is a feature amount of the bowel sound before the bowel is stimulated, from among the extracted bowel sounds; a second feature amount calculation unit that calculates a second feature amount, which is a feature amount of the bowel sound after giving a stimulus to the intestine, from among the extracted bowel sounds; an evaluation unit that evaluates an intestinal condition of the subject based on the first feature amount and the second feature amount; An evaluation device comprising: Section 2. Item 2. The evaluation device according to Item 1, wherein the feature amount is a feature amount related to the time domain of the bowel sound. Section 3. 3. The evaluation device according to item 2, wherein the feature amount is the frequency of occurrence of the bowel sounds. Section 4. Item 3. The evaluation device according to Item 2, wherein the feature amount is the duration of the bowel sound. Section 5. Item 3. The evaluation device according to Item 2, wherein the feature amount is a signal level of the bowel sound. Section 6. Item 2. The evaluation device according to Item 1, wherein the feature amount is a feature amount related to a frequency domain of the bowel sound. Section 7. Item 7. The evaluation device according to item 6, wherein the feature amount is a spectral bandwidth of the bowel sounds. Section 8. Item 2. The evaluation device according to item 1, wherein the evaluation unit evaluates the state of the intestine based on the logarithm of the first feature amount and the logarithm of the second feature amount. Section 9. Item 2. The evaluation device according to item 1, wherein the evaluation unit evaluates the state of the intestine based on the logarithm of the first feature amount and the second feature amount. Section 10. Item 2. The evaluation device according to Item 1, wherein the evaluation unit evaluates the state of the intestine based on the logarithms of the first feature amount and the second feature amount. Section 11. 8. The evaluation device according to any one of items 1 to 7, wherein the evaluation unit evaluates the state of the intestines based on a ratio between the first feature amount and the second feature amount. Section 12. Item 12. The evaluation device according to Item 11, wherein the evaluation unit evaluates the state of the intestine based on the first feature amount and the ratio. Section 13. Item 13. The evaluation device according to Item 12, wherein the evaluation unit evaluates the state of the intestines based on a distance between a coordinate corresponding to the first feature amount and the ratio and an approximation curve obtained from a distribution of coordinates corresponding to the first feature amount and the ratio of the first feature amount and the second feature amount of bowel sounds extracted from acoustic data obtained from a plurality of healthy subjects. Section 14. Item 12. The evaluation device according to Item 11, wherein the evaluation unit evaluates the state of the intestine based on the logarithm of the first feature amount and the logarithm of the ratio. Section 15. Item 15. The evaluation device according to Item 14, wherein the evaluation unit evaluates the state of the intestine based on a distance between coordinates corresponding to the logarithm of the first feature amount and the logarithm of the ratio and a regression line obtained from a distribution of coordinates corresponding to the logarithm of the first feature amount and the logarithm of the ratio between the first feature amount and the second feature amount of bowel sounds extracted from acoustic data obtained from a plurality of healthy subjects. Section 16. Item 12. The evaluation device according to Item 11, wherein the evaluation unit evaluates the state of the intestine based on the logarithm of the first feature amount and the ratio. Section 17. Item 12. The evaluation device according to Item 11, wherein the evaluation unit evaluates the state of the intestine based on the first feature amount and the logarithm of the ratio. Section 18. 8. The evaluation device according to any one of items 1 to 7, wherein the evaluation unit evaluates the state of the intestine based on a difference between the first feature amount and the second feature amount. Section 19. Item 19. The evaluation device according to Item 18, wherein the evaluation unit evaluates the state of the intestine based on the first feature amount and the difference. Section 20. 8. The evaluation device according to any one of items 1 to 7, wherein the evaluation unit evaluates the state of the intestines based on the sum of the first feature amount and the second feature amount. Section 21. Item 21. The evaluation device according to Item 20, wherein the evaluation unit evaluates the state of the intestines based on the first feature amount and the sum. Section 22. 8. The evaluation device according to any one of items 1 to 7, wherein the evaluation unit evaluates the state of the intestine based on a product of the first feature amount and the second feature amount. Section 23. Item 23. The evaluation device according to Item 22, wherein the evaluation unit evaluates the state of the intestine based on the first feature amount and the product. Section 24. 24. The evaluation device according to any one of items 1 to 23, wherein the evaluation unit evaluates the state of the intestines based on a feature of a bowel sound having a signal level equal to or higher than a threshold, out of the first feature and the second feature. Section 25. 25. The evaluation device according to any one of items 1 to 24, wherein the duration of each of the bowel sounds corresponding to the first feature amount and the second feature amount is 5 to 10 minutes. Section 26. An evaluation device according to any one of items 1 to 25, a sound collection device for obtaining the acoustic data from the subject; An evaluation system comprising: Section 27. 1. A method for evaluating the intestinal condition of a subject, comprising: an extraction step of extracting bowel sounds from acoustic data obtained from the subject; a first feature calculation step of calculating a first feature, which is a feature of the bowel sound before the bowel is stimulated, from among the extracted bowel sounds; a second feature calculation step of calculating a second feature, which is a feature of the bowel sound after giving a stimulus to the intestine, from among the extracted bowel sounds; an evaluation step of evaluating an intestinal condition of the subject based on the first feature amount and the second feature amount; An evaluation method having the following characteristics. Section 28. 1. An assessment program for assessing a subject's intestinal status, comprising: an extraction step of extracting bowel sounds from acoustic data obtained from the subject; a first feature calculation step of calculating a first feature, which is a feature of the bowel sound before the bowel is stimulated, from among the extracted bowel sounds; a second feature calculation step of calculating a second feature, which is a feature of the bowel sound after giving a stimulus to the intestine, from among the extracted bowel sounds; an evaluation step of evaluating an intestinal condition of the subject based on the first feature amount and the second feature amount; An evaluation program that causes a computer to execute the above. [Effects of the Invention]

[0007] According to the present disclosure, the state of the intestines can be evaluated with high accuracy. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a block diagram illustrating a configuration of an evaluation system according to an embodiment of the present disclosure. [Figure 2] 1 is a flowchart illustrating a processing procedure of an evaluation method according to an embodiment of the present disclosure. [Figure 3] 10 is a flowchart showing more specific processing steps of the step of collecting acoustic data. [Figure 4] 1 is an example of a waveform of acoustic data obtained from a subject. [Figure 5] 10(a) is a scatter plot of the first feature amount and the ratio obtained from the database of the carbonated water intake test, and FIG. 10(b) is a scatter plot of the logarithm of the first feature amount and the logarithm of the ratio. [Figure 6] FIG. 5(b) is a scatter plot of the logarithm of the first feature amount calculated from the feature amount of bowel sounds whose signal levels are equal to or greater than a threshold, and the logarithm of the ratio. [Figure 7] FIG. 10 is a scatter plot of first feature values ​​and differences obtained from the database of carbonated water intake tests. [Figure 8] 10(a) is a scatter plot of the first feature value and the relative change amount obtained from the database of the carbonated water intake test, and FIG. 10(b) is a scatter plot of the logarithm of the first feature value and the logarithm of the relative change amount. [Figure 9] This is a scatter plot of the first feature value and the sum obtained from the database of the carbonated water intake test. [Figure 10]FIG. 10 is a scatter plot of the first feature value and its product obtained from the database of the carbonated water intake test. [Figure 11] 10(a) is a scatter plot of the first feature amount and the ratio obtained from the database of the carbonated water intake test, and FIG. 10(b) is a scatter plot of the logarithm of the first feature amount and the logarithm of the ratio. [Figure 12] (a) is a scatter plot of the logarithm of the first feature value and the logarithm of the ratio of bowel sounds obtained from subjects with healthy bowel conditions, and (b) is a scatter plot plotting the logarithm of the first feature value and the logarithm of the ratio of bowel sounds obtained from subjects with unhealthy bowel conditions. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. Note that the present disclosure is not limited to the following embodiments, and various modifications are possible without departing from the spirit of the present disclosure.

[0010] (System Configuration) 1 is a block diagram showing the configuration of an evaluation system 1 according to an embodiment of the present disclosure. The evaluation system 1 includes a sound collection device 2 and an evaluation device 3.

[0011] The sound collection device 2 is a device for obtaining acoustic data from a subject. When collecting acoustic data at a medical institution, the sound collection device 2 is, for example, a stethoscope, and when collecting acoustic data outside a medical institution, the sound collection device 2 is, for example, a microphone. When the sound collection device 2 is a microphone, the sound collection device 2 is attached to the abdomen of the subject directly or via clothing, and obtains acoustic data including the subject's bowel sounds.

[0012] The sound collection device 2 is connected to the evaluation device 3 by wire or wirelessly, and transfers the acquired acoustic data to the evaluation device 3. Note that the acoustic data may be transferred from the sound collection device 2 to the evaluation device 3 via a recording medium or a communication device.

[0013] The evaluation device 3 is a device for evaluating the intestinal condition of a subject. The evaluation device 3 can be configured as a general-purpose computer or a portable computer such as a smartphone or tablet terminal. The hardware configuration of the evaluation device 3 includes a processor such as a CPU or GPU, a main memory device such as a DRAM or SRAM (not shown), and an auxiliary memory device 30 such as an HDD or SSD. The auxiliary memory device 30 stores an evaluation program P, an extraction model M, correlation data R, etc.

[0014] The auxiliary storage device 30 may be external to the evaluation device 3. The evaluation device 3 may also be provided on the cloud.

[0015] The evaluation device 3 includes, as functional blocks, an acoustic data acquisition unit 31, an extraction unit 32, a feature calculation unit 33, and an evaluation unit 34. Furthermore, the feature calculation unit 33 includes a first feature calculation unit 331 and a second feature calculation unit 332. These functional blocks are realized by the processor of the evaluation device 3 reading an evaluation program P into a main storage device and executing it. The evaluation program P may be downloaded to the evaluation device 3 via a communication network such as the Internet, or may be recorded on a computer-readable non-transitory recording medium such as a CD-ROM or an SD card and installed into the evaluation device 3 via the storage medium. The functions of each functional block will be described later.

[0016] (Evaluation method processing procedure) The functions of the evaluation system 1 will be described with reference to Fig. 2. Fig. 2 is a flowchart showing the processing steps of the evaluation method according to this embodiment. The evaluation method has steps S1 to S5, with step S1 being executed by the sound collection device 2 and steps S2 to S5 being executed by the above-mentioned respective functional blocks of the evaluation device 3. That is, the evaluation program P causes the evaluation device 3 to execute steps S2 to S5.

[0017] In step S1, the sound collection device 2 collects acoustic data including bowel sounds of the subject. In this embodiment, a stimulus is applied to the intestines of the subject, and the sound collection device 2 collects acoustic data before and after the stimulus is applied to the intestines.

[0018] FIG. 3 is a flowchart showing more specific processing steps of step S1.

[0019] In step S11, sound collection is started by the sound collection device 2. After a predetermined time (for example, 5 minutes) has elapsed (YES in step S12), in step S13, the subject is made to ingest a drink to stimulate the subject's intestines.

[0020] The beverage is not particularly limited as long as it can stimulate the intestines, and examples thereof include carbonated water and coffee. Furthermore, the method of stimulating the intestines is not limited to ingesting a beverage. Examples of methods of stimulating the intestines include ingesting solid foods such as laxatives, intestinal stimulating exercises such as abdominal twisting, abdominal pressure, stimulating acupressure points related to the intestines, waking up and going to bed, etc.

[0021] After a predetermined time (for example, 10 minutes) has elapsed since the intestines were stimulated (YES in step S14), sound collection by the sound collection device 2 is terminated in step S15.

[0022] As described above, the sound collection device 2 collects acoustic data before and after stimulating the intestines of the subject.

[0023] In step S2 shown in FIG. 2, the acoustic data acquisition unit 31 of the evaluation device 3 acquires acoustic data obtained from the subject by the sound collection device 2.

[0024] In step S3 (extraction step), the extraction unit 32 extracts bowel sounds from the acoustic data acquired in step S2. The acoustic data contains not only bowel sounds but also noise such as breathing sounds and body movement sounds, but bowel sounds can be extracted using known techniques. In this embodiment, the extraction unit 32 extracts bowel sounds using the technique described in International Publication No. 2019 / 216320.

[0025] Specifically, the extraction unit 32 detects multiple segments from the acoustic data, extracts PNCCs (power normalized cepstral coefficients), which are frequency-related features, from each segment, and inputs the extracted PNCCs to the extraction model M. The extraction model M is a neural network model that has learned the relationship between bowel sounds in the acoustic data and PNCCs through machine learning, and outputs a prediction score indicating the likelihood that each segment contains a bowel sound based on the input PNCC. The extraction unit 32 extracts, as bowel sounds, segments with prediction scores greater than a predetermined threshold. A bowel sound is a continuous sound, and its length (time) is indefinite.

[0026] 4 shows an example of the waveform of acoustic data obtained from a subject. The dashed lines indicate segments, and the extraction unit 32 extracts segments that are likely to contain bowel sounds.

[0027] Note that the frequency of normal bowel sounds is mainly in the range of approximately 100 to 500 Hz. Therefore, it is preferable that the extraction unit 32 extracts bowel sounds after applying a band-pass filter to the acoustic data acquired in step S2 that passes a frequency band including 100 to 500 Hz (for example, 80 to 1000 Hz). It is also preferable that the extraction unit 32 extracts bowel sounds whose signal level is equal to or higher than a threshold.

[0028] 2 (first feature calculation step, second feature calculation step), feature calculation unit 33 calculates feature amounts of the bowel sounds extracted in step S3. Specifically, first feature calculation unit 331 of feature calculation unit 33 calculates a first feature amount, which is a feature amount of the bowel sounds extracted before the intestines are stimulated, and second feature calculation unit 332 of feature calculation unit 33 calculates a second feature amount, which is a feature amount of the bowel sounds extracted after the intestines are stimulated. The duration of each bowel sound corresponding to the first feature amount and the second feature amount (the measurement time of the bowel sounds before and after the stimulation) is not particularly limited, but is preferably 5 to 10 minutes, for example.

[0029] In this embodiment, the feature amount is a feature amount related to the time domain of bowel sounds, and specifically, it is preferable to use the frequency of bowel sounds, the duration of bowel sounds, or the signal level of bowel sounds. Other feature amounts related to the time domain include, for example, the maximum amplitude of bowel sounds (BS Max. amplitude), the power of bowel sounds (BS power), the maximum amplitude spectral density of bowel sounds (Maximum Amplitude Spectral Density), the zero crossing rate of bowel sounds, the kurtosis of bowel sounds, and the Ratio of largest absolute to root mean squared value of bowel sounds.

[0030] The feature amount is not particularly limited as long as it is an acoustic feature amount of bowel sounds, and may be an acoustic feature amount related to the frequency domain, etc. Examples of feature amounts related to the frequency domain include the spectral bandwidth (width from the peak to an arbitrary attenuation point) of bowel sounds, the peak frequency of bowel sounds, the spectral centroid of bowel sounds, the spectral flatness of bowel sounds, the median frequency of bowel sounds, the mean frequency of bowel sounds, the first formant of bowel sounds, the second formant of bowel sounds, the spectral entropy of bowel sounds, the spectral spread of bowel sounds, the spectral rolloff of bowel sounds, the spectral slope of bowel sounds, the spectral kurtosis of bowel sounds, and the spectral skewness of bowel sounds. Additionally, the number of segments (BS segments) that are likely to contain bowel sounds per unit time or the number of non-BS segments (also known as Bowel-Sound Duration or Silence Duration) per unit time may be used as features.

[0031] In step S5 (evaluation step), the evaluation unit 34 evaluates the intestinal condition of the subject based on the first feature amount and the second feature amount. In this embodiment, the evaluation unit 34 evaluates the intestinal condition based on the ratio or difference between the first feature amount and the second feature amount. At this time, the logarithm may be taken for at least one of the first feature amount and the second feature amount (the ratio or difference).

[0032] As will be described in the Examples below, it has been found that when the intestinal condition is healthy, there is a correlation between the first feature amount and the second feature amount. The auxiliary storage device 30 of the evaluation device 3 stores correlation data R relating to the correlation between the first feature amount and the second feature amount obtained from subjects with healthy intestinal conditions through prior verification experiments or the like.

[0033] In contrast, when the intestinal condition is poor, the response motility to intestinal stimuli is different from when the intestinal condition is healthy, and the relationship between the first feature amount and the second feature amount therefore deviates from the correlation between the first feature amount and the second feature amount when the intestinal condition is healthy. Therefore, the evaluation unit 34 can accurately evaluate the intestinal condition of the subject by comparing the first feature amount and the second feature amount calculated in step S4 with the correlation data R.

[0034] Furthermore, in the embodiment, Correlation between the first feature and the ratio of the first feature and the second feature Correlation between the logarithm of the first feature and the logarithm of the ratio Correlation between the first feature and the difference between the first feature and the second feature Correlation between the first feature and the sum of the first and second features Correlation between the first feature and the product of the first and second features Therefore, the evaluation unit 34 determines whether The first feature and the ratio The logarithm of the first feature and the logarithm of the ratio The first feature amount and the difference The first feature and the sum of the ... second feature The first feature and the product It is preferable to evaluate the state of the intestine based on the above. This allows the state of the intestine to be evaluated with higher accuracy. [Example]

[0035] Examples of the present disclosure will be described below, but the present disclosure is not limited to the following examples.

[0036] [Examples 1 to 4] In Examples 1 to 4, a beverage intake test was conducted on subjects with healthy bowel conditions to verify whether there was a correlation between the features of bowel sounds before and after intestinal stimulation. The subjects were 20 people (12 men, 8 women, age: 32.90±7.89 years, height: 167.28±8.52 cm, weight: 59.04±8.62 kg, BMI: 21.02±1.96) who were diagnosed with healthy bowel conditions according to the Rome III diagnostic criteria. Each subject underwent the beverage intake test twice.

[0037] Specifically, in the first test, each subject, who had fasted for approximately 12 hours, drank carbonated water at 10°C or below, and the biological sounds of each subject were collected as acoustic data while they were in a resting state for 5 minutes before and 10 minutes after ingestion. The acoustic data of each subject collected in the first test is designated as database (DB1).

[0038] The second test was conducted on a different day from the first test. In the second test, each subject drank coffee at approximately 45°C, and the subject's biological sounds were collected as acoustic data for five minutes before and ten minutes after the coffee was drank. The acoustic data collected for each subject in the second test is designated database (DB)2.

[0039] The equipment used to collect acoustic data in these tests was an electronic stethoscope (E-scope2, Cardionics Inc., Houston, TX, USA) and a multi-track recorder (R16 Zoom Co., Ltd., Tokyo, Japan). During acoustic data collection, the subject was placed in a supine position, and the electronic stethoscope was fixed in a cross shape using masking tape, 9 cm to the right of the navel. The electronic stethoscope has two sound collection modes: heart and respiratory. The respiratory mode was selected, which allows recording over a wider frequency range. The sampling frequency of the acoustic data during collection was 44,100 Hz, and the digital resolution was 16 bits / sample. Taking into account the frequency characteristics of the electronic stethoscope and bowel sounds, the frequency of the collected acoustic data was downsampled to 4,000 Hz.

[0040] The evaluation device 3 used a trained neural network model as the extraction model M to extract multiple bowel sounds from each acoustic data set. Specifically, the acoustic data was segmented with a segment length of 64 ms and a shift size of 16 ms, and PNCC and LPC coefficients, which are frequency-related features, were extracted from each segment. The extracted PNCC and LPC coefficients were then input into the neural network model, and a determination was made based on the neural network output as to whether each segment contained bowel sounds. When consecutive segments containing bowel sounds (BS segments) were found, the consecutive BS segments were considered to be a single BS episode. The performance of the extraction model M was as follows: sensitivity: 90.58 ± 4.46, specificity: 91.76 ± 4.61, PPV: 66.92 ± 8.47, NPV: 98.45 ± 0.55, accuracy: 92.07 ± 3.34, and F1 score: 76.60 ± 5.89.

[0041] Next, a first feature quantity, which is a feature quantity before drinking, and a second feature quantity, which is a feature quantity after drinking, were calculated from the extracted bowel sounds. Frequency of bowel sounds Bowel sound duration Bowel sound signal level Spectral bandwidth of bowel sounds The following four feature quantities were used. Below, we verified the correlation between the first and second feature quantities for each feature quantity.

[0042] Example 1 In Example 1, the correlation between the first feature amount and the second feature amount was verified when the feature amount was the frequency of bowel sounds. The first feature amount was calculated by dividing the first feature amount by the average number of bowel sounds per minute (x b ) and the second feature is defined as the average number of bowel sounds per minute (x a ) and the first feature x b and the second feature x a was calculated for each subject.

[0043] First, the first feature x b and the second feature x a The ratio of the first feature x b The correlation between the ratio and the variance was verified. The ratio was defined as in equation (1). ratio=x a / x b ···(1)

[0044] Figure 5(a) shows the first feature x obtained from DB1 of the carbonated water intake test. b 5(a) and the ratio. b It was confirmed that the larger the value, the lower the ratio tends to be. The correlation coefficient for each point in Figure 5(a) was R = -0.642, and the significance level was p = 0.002. Student's t-test was used to evaluate the significance of the correlation coefficient, and a correlation was considered to exist when the significance level p was p < 0.05.

[0045] As described above, the first feature value x when a subject with a healthy intestinal condition ingests carbonated water is b It was found that there is a high nonlinear correlation between the first feature x and the ratio. b It was found that the coordinates of and ratio tend to concentrate in a certain region.

[0046] On the other hand, when the intestinal condition is poor, the response motility to the stimulus is different from when the intestinal condition is healthy. b The coordinates of the ratio and the approximation curve of each point shown in FIG. 5(a) are obtained, and the first feature value x calculated in the same manner as above is used to calculate the bowel sounds obtained from an unknown subject who has taken carbonated water. b The intestinal condition of the unknown subject can be evaluated by calculating the distance between the coordinates of the ratio and the approximate curve. That is, the evaluation unit 34 calculates the first feature quantity x of the bowel sound extracted from the acoustic data obtained from the subject. b and the first feature x b and the second feature x a The coordinates corresponding to the ratio of the two and the first feature x of bowel sounds extracted from acoustic data obtained from multiple healthy subjects are b and the first feature x b and the second feature x a The condition of the intestine can be evaluated based on the distance to an approximate curve obtained from the distribution of coordinates corresponding to the ratio of the above.

[0047] Furthermore, the first feature x b The logarithm of the ratio and the logarithm of the first feature x obtained from DB1 were calculated for each subject. b The correlation coefficient for each point was R = -0.943, and the significance level was p = 0.000. Therefore, the first feature value x when a subject with a healthy intestinal condition ingested carbonated water b It was found that there is a very high linear correlation between the logarithm of and the logarithm of the ratio.

[0048] Therefore, a regression line of each point shown in Figure 5(b) is calculated, and the first feature x calculated in the same manner as above is calculated from the bowel sounds obtained from the unknown subject who ingested carbonated water. bIt has been found that the intestinal condition of an unknown subject can be evaluated with higher accuracy by calculating the distance between the coordinates of the ratio and the regression line. b and the first feature x b and the second feature x a The coordinates corresponding to the logarithm of the ratio of the two and the first feature x of bowel sounds extracted from acoustic data obtained from multiple healthy subjects are b and the first feature x b and the second feature x a The condition of the intestine can be evaluated based on the distance to a regression line obtained from the distribution of coordinates corresponding to the logarithm of the ratio of the above.

[0049] In this embodiment, the logarithm is a natural logarithm, but the base of the logarithm is not particularly limited. b Although the logarithm of both the first feature quantity x and the ratio is taken, the logarithm of only one of them may be taken. b Based on the logarithm and ratio of the first feature x b The intestinal condition may be evaluated based on the logarithm of the ratio . b Instead of taking the logarithm of at least one of σ and the ratio, a Yeo-Johnson transform, a Box-Cox transform, an arcsin transform, a Hilbert transform, a square root transform, dynamic range compression, exponential scaling, or a custom transform thereof may be used.

[0050] The first feature x obtained from DB2 (coffee intake test) b The correlation coefficient between the logarithm of the ratio and the logarithm of the ratio was R=-0.768, and the significance level was p=0.000.

[0051] Furthermore, the first feature x b and the second feature x a Among these, the signal level is threshold A thThe above feature quantities of bowel sounds were extracted, and the ratios were calculated in the same manner as above. th =5×10 -4 It was.

[0052] FIG. 6 shows the signal level in FIG. 5(b) when the signal level is below threshold A. th The first feature x calculated from the above bowel sound features b The correlation coefficient for each point was R = -0.952, and the significance level was p = 0.000. Therefore, the signal level is within the threshold A. th By limiting the features of bowel sounds to the above, the first feature x b and the second feature x a The correlation was found to be even higher.

[0053] Next, the difference between the first feature and the second feature (x a -x b ) and the first feature value.

[0054] Figure 7 shows the first feature x obtained from DB1. b and the difference (x a -x b ) is a scatter plot of the absolute values ​​of the correlation coefficient for each point, R = -0.637, and the significance level was p = 0.003. Therefore, the first feature value x b and the difference (x a -x b ) was found to have a high negative correlation.

[0055] Furthermore, the relative change between the first feature amount and the second feature amount ((x a -x b ) / x b ) was calculated for each subject. Figure 8(a) shows the first feature x obtained from DB1. b The correlation coefficient for each point was R = -0.642, and the significance level was p = 0.002. Therefore, the first feature value x when a subject with a healthy intestinal condition ingested carbonated water was bIt was found that there was a high nonlinear correlation between the relative change and the mean.

[0056] Furthermore, the logarithm of the relative change was calculated for each subject. Figure 8(b) shows the first feature x obtained from DB1. b The correlation coefficient for each point was R = -0.957, and the significance level was p = 0.000. Therefore, the first feature value x when subjects with healthy intestinal conditions ingested carbonated water was b It was found that there is a very high linear correlation between the logarithm of the relative change and the logarithm of the relative change.

[0057] Next, the sum of the first feature and the second feature (x a +x b ) and the first feature value.

[0058] Figure 9 shows the first feature x obtained from DB1. b and the sum (x a +x b ) is a scatter plot. The correlation coefficient for each point was R = 0.960, and the significance level was p = 0.000. Therefore, the first feature value x when a subject with a healthy intestinal condition ingested carbonated water b and the sum (x a +x b ) was found to have a high positive correlation.

[0059] Next, the product of the first feature and the second feature (x a ×x b ) and the first feature value.

[0060] Figure 10 shows the first feature x obtained from DB1. b and the product (x a ×x b ) is a scatter plot. The correlation coefficient for each point was R = 0.984, and the significance level was p = 0.000. Therefore, the first feature value x when a subject with a healthy intestinal condition ingested carbonated water b and the product (x a ×x b ) was found to have a high positive correlation.

[0061] Example 2 In Example 2, the correlation between the first feature amount and the second feature amount was verified when the feature amount was the duration of bowel sounds. The first feature amount was calculated by dividing the duration of bowel sounds by the average duration (t b ) and the second feature is defined as the average time (t a ) and the first feature t b and the second feature t a was calculated for each subject.

[0062] As in the first embodiment, the first feature t b and the second feature t a The ratio of the first feature t b The correlation between the ratio and the variance was verified. The ratio was defined as in equation (2). ratio=t a / t b ···(2)

[0063] Furthermore, the first feature t b The logarithm of and the logarithm of the ratio were calculated for each subject. As a result, the first feature x obtained from DB1 (carbonated water intake test) b The correlation coefficient between the logarithm of the ratio and the logarithm of the ratio was R = -0.707, with a significance level of p = 0.000. In addition, the first feature x obtained from DB2 (coffee intake test) b The correlation coefficient between the logarithm of the ratio and the logarithm of the ratio was R=-0.537, with a significance level of p=0.015.

[0064] As described above, the first feature value t b It was found that there is a high correlation between the logarithm of and the logarithm of the ratio.

[0065] Example 3 In Example 3, the correlation between the first feature amount and the second feature amount was verified when the feature amount was the signal level of bowel sounds. The first feature amount was calculated as the average signal level (SNRb ) and the second feature is defined as the average signal level (SNR a ) and the first feature SNR b and the second feature SNR a was calculated for each subject.

[0066] As in the first and second embodiments, the first feature SNR b and the second feature SNR a Ratio and the first feature SNR b The correlation between the ratio and the variance was verified. The ratio was defined as in equation (3). ratio=SNR a / SNR b ···(3)

[0067] Furthermore, the first feature SNR b The logarithm of the ratio and the logarithm of the ratio were calculated for each subject. As a result, the first feature SNR obtained from DB1 (carbonated water ingestion test) b The correlation coefficient between the logarithm of the ratio and the logarithm of the ratio was R = -0.538, with a significance level of p = 0.014. In addition, the first feature SNR obtained from DB2 (coffee intake test) b The correlation coefficient between the logarithm of and the logarithm of the ratio was R=-0.477, with a significance level of p=0.034.

[0068] As described above, the first feature SNR when subjects with healthy intestinal conditions ingested carbonated water or coffee b It was found that there is a high correlation between the logarithm of and the logarithm of the ratio.

[0069] Example 4 In Example 4, the correlation between the first feature and the second feature was verified when the feature was the spectral bandwidth of bowel sounds. The spectral bandwidth was defined as the frequency range in which the amplitude spectrum is higher than half the maximum amplitude. The first feature was defined as the average spectral bandwidth (w b) and the second feature is defined as the average spectral bandwidth per minute of bowel sounds for 10 minutes immediately after drinking the drink (w a ) and the first feature w b and the second feature w a was calculated for each subject.

[0070] First, the first feature w b and the second feature w a The ratio of and the first feature w b The correlation between the ratio and the variance was verified. The ratio was defined as in equation (4). ratio=w a / w b ···(4)

[0071] Figure 11(a) shows the first feature value w obtained from DB1. b The correlation coefficient of each point was R = -0.599, and the significance level was p = 0.005. Therefore, the first feature value w b It was found that there is a high correlation between the ratio and the

[0072] Furthermore, the first feature w b The logarithm of the ratio and the logarithm of the first feature w obtained from DB1 were calculated for each subject. b The correlation coefficient of each point was R = -0.708, and the significance level was p = 0.000. Therefore, the first feature value w b It was found that there is a high correlation between the logarithm of and the logarithm of the ratio.

[0073] [Example 5] In Example 5, 30 female subjects were administered a questionnaire based on the Rome IV diagnostic criteria, and based on their responses, the subjects were classified into functional diarrhea (FD), functional constipation (FC), and healthy individuals. The classification results are shown in Table 1.

[0074] [Table 1]

[0075] Next, a beverage ingestion test was conducted on each subject. Specifically, each subject, who had fasted for approximately 12 hours, ingested 200 mL of strong carbonated water at approximately 10°C or below. The subject's body sounds were collected as acoustic data during a resting state for 5 minutes before ingestion and 10 minutes after ingestion. The equipment and method used to collect the acoustic data were the same as those used in Examples 1 to 4 above. Taking into account the frequency characteristics of the electronic stethoscope and the frequency characteristics of bowel sounds, the frequency of the collected acoustic data was downsampled to 4000 Hz.

[0076] In the evaluation device 3, multiple bowel sounds were extracted from each acoustic data set using a trained neural network model as the extraction model M. Specifically, the acoustic data was segmented with a segment length of 64 ms and a shift size of 16 ms, and the signals within the segments were normalized to a mean value of 0 and a standard deviation of 1. A total of 52 features, consisting of 10-dimensional linear prediction cepstral (LPC) coefficients, their Δ and ΔΔ, and 22-dimensional MFCCs, were extracted from the normalized segments and used as input to the neural network. Each feature was labeled with a binary value indicating whether it was a bowel sound (BS) segment or not to generate training data. The generated training data was used to train the neural network and generate the extraction model M for performing two-class classification of whether it was a bowel sound or not. The number of units in the hidden layer of the neural network was 40. The performance of the extraction model M was evaluated using five-fold cross-validation, and the results were sensitivity 80.47±2.83%, specificity 97.61±0.74%, PPV 86.17±1.88%, NPV 96.52±0.74%, accuracy 95.02±0.87%, and F1 score 83.17±0.80%.

[0077] In addition, taking into account changes in acoustic features due to environmental noise, noise subtraction was performed using the average of the spectra obtained from segments determined not to be bowel sounds.

[0078] Next, a first feature value, which is a feature value before drinking, and a second feature value, which is a feature value after drinking, were calculated from the extracted bowel sounds. More specifically, the first feature value was calculated by multiplying the average (x b ) and the second feature is defined as the average (x a ) and the first feature x b and the second feature x a was calculated for each subject. ·Bow sound maximum amplitude (BS maximum amplitude) ·Bow sound power (BS power) Frequency of bowel sounds Maximum Amplitude Spectral Density (MASD) of bowel sounds Peak frequency of bowel sounds Spectral bandwidth of bowel sounds The maximum amplitude spectral density is the maximum value of the amplitude spectrum obtained by performing a fast Fourier transform (FFT) on the time signal.

[0079] Furthermore, the first feature x of the bowel sounds obtained from healthy subjects b and the first feature x b and the second feature x a A regression line is found from the distribution of coordinates corresponding to the logarithm of the ratio of b We verified whether the intestinal condition of each subject could be evaluated based on the distance between the logarithm of the logarithm of the ratio and the regression line.

[0080] The following describes the case where the feature is the spectral bandwidth of the bowel sound. First, the bowel sounds obtained from 30 subjects were analyzed, and the average spectral bandwidth corresponding to the bowel sounds for 5 minutes immediately before drinking was calculated (first feature x b) and the average spectral bandwidth corresponding to the bowel sounds for 10 minutes immediately after drinking (second feature x a ) and then calculate the first feature x b and the first feature x b and the second feature x a The logarithm of the ratio was calculated for each subject.

[0081] Next, the first feature x of the bowel sounds obtained from 10 subjects with healthy bowel conditions was calculated. b The logarithm of the ratio is extracted, and the first feature x b The logarithm of the x-axis and the logarithm of the ratio (green circle) were plotted on a graph. Furthermore, using the plotted coordinates, a linear regression model was constructed and trained to estimate the linear regression coefficients (slope and intercept) based on the least squares method. Specifically, the coefficients (model parameters) were estimated by minimizing the sum of the squared errors between the plotted coordinates and the model predictions, and the regression line was obtained.

[0082] Furthermore, a threshold line was set, which is separated from the regression line by a threshold value. The red line in Figure 12(a) is the regression line, and the two blue lines are the threshold lines.

[0083] Next, the first feature x of the bowel sounds obtained from 20 subjects whose bowel condition was not healthy (FD or FC) was calculated. b The logarithm of the ratio and the logarithm of the ratio were extracted and plotted on a graph with a regression line and a threshold line set, as shown in Figure 12(b) (red circles). It can be seen that the healthy subject group (green circles) is distributed near the regression line, whereas the FD and FC groups (red circles) are distributed in areas away from the regression line. Therefore, the first feature x of the bowel sounds obtained from the subjects b It can be seen that if the coordinates corresponding to the logarithm of and the logarithm of the ratio are located in the area between the two blue lines, that is, if the distance between the coordinates and the regression line is within the threshold, the condition of the intestines can be evaluated as healthy.

[0084] The healthy subject group (green circle) was labeled as 0, and the FD and FC groups (red circle) as 1. The distance between each circle and the regression line was calculated as a score, and a threshold was used to classify the score into two classes (1 if greater than the threshold, 0 if less than the threshold). Five feature quantities other than the spectral bandwidth of bowel sounds were also classified into two classes in the same manner as above. The classification performance of each feature quantity is shown in Table 2. The threshold was adjusted to maximize classification performance.

[0085] [Table 2]

[0086] The results in Table 2 show that whichever feature is used, regression analysis can accurately assess whether the intestinal condition is healthy, FD, or FC. While keeping in mind that the dataset is biased, it was found that, among time-domain features, the frequency of bowel sounds in particular contributed to classification, and among frequency-domain features, the spectral bandwidth of bowel sounds in particular contributed to classification.

[0087] In the results shown in Table 2, the length of the bowel sounds corresponding to the first feature was 5 minutes, and the length of each bowel sound corresponding to the second feature was 10 minutes, but two-class classification was performed in the same manner as above by changing the length of each bowel sound corresponding to the first and second feature. Table 3 shows the classification performance of each feature when the length of the bowel sounds corresponding to the first feature was 10 minutes, and the length of the bowel sounds corresponding to the second feature was 10 minutes.

[0088] [Table 3]

[0089] Table 4 shows the classification performance of each feature when the length of the bowel sound corresponding to the first feature is 5 minutes and the length of the bowel sound corresponding to the second feature is 5 minutes.

[0090] [Table 4]

[0091] From Tables 3 and 4, it is clear that the state of the intestines can be evaluated with high accuracy by setting the length of each bowel sound corresponding to the first feature amount and the second feature amount to 5 to 10 minutes.

[0092] (Additional notes) In the above examples, the average value at a predetermined time before or after stimulation was used as the feature, but statistics such as the median and variance can also be used. Furthermore, in each graph, the horizontal axis represents the measured value, but it may represent a normalized or standardized value, and the vertical axis may also be normalized or standardized. Furthermore, values ​​may be incremented by +1 or the like before logarithmic transformation. Furthermore, in each graph, the horizontal axis represents the measured value before stimulation, but it may represent the measured value after stimulation. [Explanation of symbols]

[0093] 1. Rating System 2 Sound collection device 3 Evaluation equipment 30 Auxiliary storage 31 Acoustic data acquisition unit 32 Extraction part 33 Feature calculation unit 331 First feature calculation unit 332 Second feature calculation unit 34 Evaluation Department M Extraction Model P Evaluation Program R Correlated Data

Claims

1. An evaluation device for evaluating the intestinal condition of a subject, an extraction unit that extracts bowel sounds from acoustic data obtained from the subject; a first feature amount calculation unit that calculates a first feature amount, which is one feature amount of the bowel sounds before the bowel is stimulated, from among the extracted bowel sounds; a second feature amount calculation unit that calculates a second feature amount, which is the one feature amount of the bowel sound after giving a stimulus to the intestine, from among the extracted bowel sounds; an evaluation unit that evaluates an intestinal condition of the subject based on the first feature amount and the second feature amount; Equipped with the stimulus is a stimulus caused by ingestion of a drink or solid food; The evaluation unit evaluates the state of the intestine based on the first feature amount and a ratio between the first feature amount and the second feature amount.

2. The evaluation device described in claim 1, wherein the evaluation unit evaluates the state of the intestines based on the distance between the coordinates corresponding to the first feature and the ratio of the first feature and the second feature and an approximate curve obtained from the distribution of coordinates corresponding to the first feature and the ratio of the first feature and the second feature of bowel sounds extracted from acoustic data obtained from multiple healthy subjects.

3. An evaluation device for evaluating the intestinal condition of a subject, comprising: an extraction unit that extracts bowel sounds from acoustic data obtained from the subject; a first feature amount calculation unit that calculates a first feature amount, which is one feature amount of the bowel sounds before the bowel is stimulated, from among the extracted bowel sounds; a second feature amount calculation unit that calculates a second feature amount, which is the one feature amount of the bowel sound after giving a stimulus to the intestine, from among the extracted bowel sounds; an evaluation unit that evaluates an intestinal condition of the subject based on the first feature amount and the second feature amount; Equipped with the stimulus is a stimulus caused by ingestion of a drink or solid food; The evaluation unit evaluates the state of the intestine based on the logarithm of the first feature amount and the logarithm of the ratio between the first feature amount and the second feature amount.

4. The evaluation device described in claim 3, wherein the evaluation unit evaluates the state of the intestines based on the distance between the coordinates corresponding to the logarithm of the first feature and the logarithm of the ratio between the first feature and the second feature and a regression line obtained from the distribution of coordinates corresponding to the logarithm of the first feature of bowel sounds extracted from acoustic data obtained from multiple healthy subjects and the logarithm of the ratio between the first feature and the second feature.

5. An evaluation device for evaluating the intestinal condition of a subject, comprising: an extraction unit that extracts bowel sounds from acoustic data obtained from the subject; a first feature amount calculation unit that calculates a first feature amount, which is one feature amount of the bowel sounds before the bowel is stimulated, from among the extracted bowel sounds; a second feature amount calculation unit that calculates a second feature amount, which is the one feature amount of the bowel sound after giving a stimulus to the intestine, from among the extracted bowel sounds; an evaluation unit that evaluates an intestinal condition of the subject based on the first feature amount and the second feature amount; Equipped with the stimulus is a stimulus caused by ingestion of a drink or solid food; The evaluation unit evaluates the state of the intestine based on the first feature amount and a difference between the first feature amount and the second feature amount.

6. An evaluation device for evaluating the intestinal condition of a subject, comprising: an extraction unit that extracts bowel sounds from acoustic data obtained from the subject; a first feature amount calculation unit that calculates a first feature amount, which is one feature amount of the bowel sounds before the bowel is stimulated, from among the extracted bowel sounds; a second feature amount calculation unit that calculates a second feature amount, which is the one feature amount of the bowel sound after giving a stimulus to the intestine, from among the extracted bowel sounds; an evaluation unit that evaluates an intestinal condition of the subject based on the first feature amount and the second feature amount; Equipped with the stimulus is a stimulus caused by ingestion of a drink or solid food; The evaluation unit evaluates the state of the intestine based on the first feature amount and the sum of the first feature amount and the second feature amount.

7. An evaluation device for evaluating the intestinal condition of a subject, comprising: an extraction unit that extracts bowel sounds from acoustic data obtained from the subject; a first feature amount calculation unit that calculates a first feature amount, which is one feature amount of the bowel sounds before the bowel is stimulated, from among the extracted bowel sounds; a second feature amount calculation unit that calculates a second feature amount, which is the one feature amount of the bowel sound after giving a stimulus to the intestine, from among the extracted bowel sounds; an evaluation unit that evaluates an intestinal condition of the subject based on the first feature amount and the second feature amount; Equipped with the stimulus is a stimulus caused by ingestion of a drink or solid food; The evaluation unit evaluates the state of the intestine based on the first feature amount and a product of the first feature amount and the second feature amount.

8. 8. The evaluation device according to claim 1, wherein the feature amount is a feature amount related to a time domain of the bowel sound.

9. The evaluation device according to claim 8 , wherein the feature amount is an occurrence frequency of the bowel sounds.

10. The evaluation device according to claim 8 , wherein the feature amount is a duration of the bowel sound.

11. The evaluation device according to claim 8 , wherein the feature amount is a signal level of the bowel sound.

12. 8. The evaluation device according to claim 1, wherein the feature amount is a feature amount related to a frequency domain of the bowel sounds.

13. The evaluation device according to claim 12 , wherein the feature amount is a spectral bandwidth of the bowel sound.

14. 8. The evaluation device according to claim 1, wherein the evaluation unit evaluates the state of the intestines based on a feature of a bowel sound having a signal level equal to or higher than a threshold, out of the first feature and the second feature.

15. 8. The evaluation device according to claim 1, wherein the duration of each of the bowel sounds corresponding to the first feature amount and the second feature amount is 5 to 10 minutes.

16. An evaluation device according to any one of claims 1 to 7; a sound collection device for obtaining the acoustic data from the subject; An evaluation system comprising:

17. 1. An evaluation method in which a computer evaluates a subject's intestinal condition, comprising: an extraction step of extracting bowel sounds from acoustic data obtained from the subject; a first feature calculation step of calculating a first feature, which is one feature of the bowel sounds before the bowel is stimulated, from among the extracted bowel sounds; a second feature calculation step of calculating a second feature, which is the one feature of the bowel sound after the bowel has been stimulated, from among the extracted bowel sounds; an evaluation step of evaluating an intestinal condition of the subject based on the first feature amount and the second feature amount; and the stimulus is a stimulus caused by ingestion of a drink or solid food; In the evaluation step, the state of the intestine is evaluated based on the first feature amount and a ratio between the first feature amount and the second feature amount.

18. An evaluation method in which a computer evaluates the intestinal condition of a subject, comprising: an extraction step of extracting bowel sounds from acoustic data obtained from the subject; a first feature calculation step of calculating a first feature, which is one feature of the bowel sounds before the bowel is stimulated, from among the extracted bowel sounds; a second feature calculation step of calculating a second feature, which is the one feature of the bowel sound after the bowel has been stimulated, from among the extracted bowel sounds; an evaluation step of evaluating an intestinal condition of the subject based on the first feature amount and the second feature amount; and the stimulus is a stimulus caused by ingestion of a drink or solid food; In the evaluation step, the state of the intestine is evaluated based on the logarithm of the first feature amount and the logarithm of the ratio between the first feature amount and the second feature amount.

19. An evaluation method in which a computer evaluates the intestinal condition of a subject, comprising: an extraction step of extracting bowel sounds from acoustic data obtained from the subject; a first feature calculation step of calculating a first feature, which is one feature of the bowel sounds before the bowel is stimulated, from among the extracted bowel sounds; a second feature calculation step of calculating a second feature, which is the one feature of the bowel sound after the bowel has been stimulated, from among the extracted bowel sounds; an evaluation step of evaluating an intestinal condition of the subject based on the first feature amount and the second feature amount; and the stimulus is a stimulus caused by ingestion of a drink or solid food; In the evaluation step, the state of the intestine is evaluated based on the first feature amount and a difference between the first feature amount and the second feature amount.

20. An evaluation method in which a computer evaluates the intestinal condition of a subject, comprising: an extraction step of extracting bowel sounds from acoustic data obtained from the subject; a first feature calculation step of calculating a first feature, which is one feature of the bowel sounds before the bowel is stimulated, from among the extracted bowel sounds; a second feature calculation step of calculating a second feature, which is the one feature of the bowel sound after the bowel has been stimulated, from among the extracted bowel sounds; an evaluation step of evaluating an intestinal condition of the subject based on the first feature amount and the second feature amount; and the stimulus is a stimulus caused by ingestion of a drink or solid food; In the evaluation step, the state of the intestine is evaluated based on the first feature amount and the sum of the first feature amount and the second feature amount.

21. An evaluation method in which a computer evaluates the intestinal condition of a subject, comprising: an extraction step of extracting bowel sounds from acoustic data obtained from the subject; a first feature calculation step of calculating a first feature, which is one feature of the bowel sounds before the bowel is stimulated, from among the extracted bowel sounds; a second feature calculation step of calculating a second feature, which is the one feature of the bowel sound after the bowel has been stimulated, from among the extracted bowel sounds; an evaluation step of evaluating an intestinal condition of the subject based on the first feature amount and the second feature amount; and the stimulus is a stimulus caused by ingestion of a drink or solid food; In the evaluation step, the state of the intestine is evaluated based on the first feature amount and a product of the first feature amount and the second feature amount.

22. 1. An assessment program for assessing a subject's intestinal status, comprising: an extraction step of extracting bowel sounds from acoustic data obtained from the subject; a first feature calculation step of calculating a first feature, which is one feature of the bowel sounds before the bowel is stimulated, from among the extracted bowel sounds; a second feature calculation step of calculating a second feature, which is the one feature of the bowel sound after the bowel has been stimulated, from among the extracted bowel sounds; an evaluation step of evaluating an intestinal condition of the subject based on the first feature amount and the second feature amount; on the computer, the stimulus is a stimulus caused by ingestion of a drink or solid food; In the evaluation step, the state of the intestine is evaluated based on the first feature amount and a ratio between the first feature amount and the second feature amount.

23. An evaluation program for evaluating the intestinal condition of a subject, comprising: an extraction step of extracting bowel sounds from acoustic data obtained from the subject; a first feature calculation step of calculating a first feature, which is one feature of the bowel sounds before the bowel is stimulated, from among the extracted bowel sounds; a second feature calculation step of calculating a second feature, which is the one feature of the bowel sound after the bowel has been stimulated, from among the extracted bowel sounds; an evaluation step of evaluating an intestinal condition of the subject based on the first feature amount and the second feature amount; on the computer, the stimulus is a stimulus caused by ingestion of a drink or solid food; In the evaluation step, the state of the intestine is evaluated based on the logarithm of the first feature amount and the logarithm of the ratio between the first feature amount and the second feature amount.

24. An evaluation program for evaluating the intestinal condition of a subject, comprising: an extraction step of extracting bowel sounds from acoustic data obtained from the subject; a first feature calculation step of calculating a first feature, which is one feature of the bowel sounds before the bowel is stimulated, from among the extracted bowel sounds; a second feature calculation step of calculating a second feature, which is the one feature of the bowel sound after the bowel has been stimulated, from among the extracted bowel sounds; an evaluation step of evaluating an intestinal condition of the subject based on the first feature amount and the second feature amount; on the computer, the stimulus is a stimulus caused by ingestion of a drink or solid food; In the evaluation step, the state of the intestine is evaluated based on the first feature amount and a difference between the first feature amount and the second feature amount.

25. An evaluation program for evaluating the intestinal condition of a subject, comprising: an extraction step of extracting bowel sounds from acoustic data obtained from the subject; a first feature calculation step of calculating a first feature, which is one feature of the bowel sounds before the bowel is stimulated, from among the extracted bowel sounds; a second feature calculation step of calculating a second feature, which is the one feature of the bowel sound after the bowel has been stimulated, from among the extracted bowel sounds; an evaluation step of evaluating an intestinal condition of the subject based on the first feature amount and the second feature amount; on the computer, the stimulus is a stimulus caused by ingestion of a drink or solid food; In the evaluation step, the state of the intestine is evaluated based on the first feature amount and the sum of the first feature amount and the second feature amount.

26. An evaluation program for evaluating the intestinal condition of a subject, comprising: an extraction step of extracting bowel sounds from acoustic data obtained from the subject; a first feature calculation step of calculating a first feature, which is one feature of the bowel sounds before the bowel is stimulated, from among the extracted bowel sounds; a second feature calculation step of calculating a second feature, which is the one feature of the bowel sound after the bowel has been stimulated, from among the extracted bowel sounds; an evaluation step of evaluating an intestinal condition of the subject based on the first feature amount and the second feature amount; on the computer, the stimulus is a stimulus caused by ingestion of a drink or solid food; In the evaluation step, the state of the intestine is evaluated based on the first feature amount and the product of the first feature amount and the second feature amount.

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