Apparatus, system, method and program for evaluating gastrointestinal condition

Abstract

To accurately evaluate the state of a gastrointestinal tract.SOLUTION: An evaluation device 3 for evaluating the state of a gastrointestinal tract of a subject comprises: an extraction unit 32 which extracts a plurality of bowel sounds from acoustic data obtained from the subject; a feature amount calculation unit 33 which calculates a feature amount relevant to a frequency region of each bowel sound; a distribution data generation unit 34 which generates distribution data A indicating the distribution of each feature amount; and an evaluation unit 35 which evaluates the state of the gastrointestinal tract of the subject on the basis of the distribution data A.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 a gastrointestinal condition. [Background technology]

[0002] Conventionally, 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 occurrence 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, as shown in a comparative example described later, the time domain feature quantities of bowel sounds cannot detect certain changes in the gastrointestinal state related to hunger, after eating, health condition, lifestyle, etc. Therefore, the conventional technology described in Patent Document 1 has a problem in that it cannot accurately evaluate the gastrointestinal state.

[0005] An object of the present disclosure is to accurately evaluate the gastrointestinal condition. [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 a gastrointestinal condition of a subject, comprising: an extracting unit for extracting a plurality of bowel sounds from acoustic data obtained from the subject; a feature calculation unit that calculates a feature associated with a frequency domain of each of the bowel sounds; a distribution data generating unit that generates distribution data indicating a distribution of each of the feature amounts; an evaluation unit that evaluates a gastrointestinal condition of the subject based on the distribution data; An evaluation device comprising: Section 2. The evaluation unit is a reference data acquiring unit that acquires reference distribution data indicating a distribution of the feature amounts of a plurality of bowel sounds included in acoustic data obtained from the subject when the gastrointestinal condition of the subject is in a predetermined state; a similarity calculation unit that calculates a similarity between the distribution data generated by the distribution data generation unit and the reference distribution data; Equipped with Item 2. The evaluation device according to item 1, which evaluates the state based on the predetermined state and the similarity. Section 3. 3. The evaluation device according to item 1 or 2, wherein the feature amount is a representative frequency. Section 4. The feature amount calculation unit is a division unit for dividing each of the bowel sounds into a plurality of segments at regular time intervals; A peak frequency calculation unit that calculates a peak frequency of each of the segments; a representative frequency selection unit that selects a maximum peak frequency from the peak frequencies as the representative frequency; Item 4. The evaluation device according to item 3, comprising: Section 5. An evaluation device according to any one of items 1 to 4, a sound collection device for obtaining the acoustic data from the subject; An evaluation system comprising: Section 6. 1. A method for evaluating a gastrointestinal condition of a subject, comprising: extracting a plurality of bowel sounds from acoustic data obtained from the subject; a feature calculation step of calculating a feature associated with a frequency domain of each of the bowel sounds; a distribution data generating step of generating distribution data indicating a distribution of each of the feature amounts; evaluating a gastrointestinal condition of the subject based on the distribution data; The evaluation method has the following features: Section 7. The evaluation step includes: a reference data acquiring step of acquiring reference distribution data indicating a distribution of the feature amounts of a plurality of bowel sounds included in acoustic data obtained from the subject when the gastrointestinal condition of the subject is in a predetermined state; a similarity calculation step of calculating a similarity between the distribution data generated in the distribution data generation step and the reference distribution data; having Item 7. The evaluation method according to item 6, further comprising evaluating the state based on the predetermined state and the similarity. Section 8. Item 8. The evaluation method according to item 6 or 7, wherein the feature amount is a representative frequency. Section 9. The feature amount calculation step includes: a segmentation step of dividing each of the bowel sounds into a plurality of segments at regular time intervals; a peak frequency calculation step of calculating a peak frequency of each of the segments; a representative frequency selection step of selecting a maximum peak frequency from the peak frequencies as the representative frequency; Item 9. The evaluation method according to item 8, comprising: Section 10. 1. An assessment program for assessing a gastrointestinal condition of a subject, comprising: extracting a plurality of bowel sounds from acoustic data obtained from the subject; a feature calculation step of calculating a feature associated with a frequency domain of each of the bowel sounds; a distribution data generating step of generating distribution data indicating a distribution of each of the feature amounts; evaluating a gastrointestinal condition of the subject based on the distribution data; An evaluation program that causes a computer to execute the above. Section 11. The evaluation step includes: a reference data acquiring step of acquiring reference distribution data indicating a distribution of the feature amounts of a plurality of bowel sounds included in acoustic data obtained from the subject when the gastrointestinal condition of the subject is in a predetermined state; a similarity calculation step of calculating a similarity between the distribution data generated in the distribution data generation step and the reference distribution data; having Item 11. The evaluation program according to item 10, which evaluates the state based on the predetermined state and the similarity. Section 12. Item 12. The evaluation program according to item 10 or 11, wherein the feature amount is a representative frequency. Section 13. The feature amount calculation step includes: a segmentation step of dividing each of the bowel sounds into a plurality of segments at regular time intervals; a peak frequency calculation step of calculating a peak frequency of each of the segments; a representative frequency selection step of selecting a maximum peak frequency from the peak frequencies as the representative frequency; Item 13. The evaluation program according to item 12, comprising: Effect of the Invention

[0007] According to the present disclosure, gastrointestinal conditions can be evaluated with high accuracy. [Brief description of the drawings]

[0008] [Figure 1] 1 is a block diagram showing a configuration of an evaluation system according to an embodiment of the present disclosure. [Diagram 2] 1 is a flowchart showing a processing procedure of an evaluation method according to an embodiment of the present disclosure. [Diagram 3] 2 is an example of a waveform of acoustic data obtained from a subject. [Figure 4] 13 is a flowchart showing more specific processing steps of the feature amount calculation step. [Diagram 5] 1 is a graph showing the frequency spectrum of each segment of one bowel sound. [Figure 6] 1 is an example of distribution data. [Figure 7] 13 is a flowchart showing more specific processing steps of the evaluation step. [Figure 8] 1 is an example of reference distribution data. [Figure 9] 13 is a graph showing the average value for each subject of the degree of similarity of distribution data for three combinations. [Figure 10] (a)–(d) are box plots showing the BS power, BS length, BS frequency, and BS occurrence interval extracted from acoustic data obtained before carbonated water ingestion (BSI), before water ingestion (BWI), after carbonated water ingestion (ASI), and after water ingestion (AWI), respectively. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

[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 gastrointestinal condition of a subject. In this disclosure, "gastrointestinal" means "at least one of the stomach and intestines." The evaluation device 3 can be configured as a general-purpose computer, as well as a portable computer such as a smartphone or a tablet terminal. The evaluation device 3 includes, as its hardware configuration, 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, reference distribution data B, and the like.

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

[0015] The evaluation device 3 includes, as functional blocks, an acoustic data acquisition unit 31, an extraction unit 32, a feature amount calculation unit 33, a distribution data generation unit 34, and an evaluation unit 35. Furthermore, the feature amount calculation unit 33 includes a division unit 331, a peak frequency calculation unit 332, and a representative frequency selection unit 333, and the evaluation unit 35 includes a reference data acquisition unit 351 and a similarity calculation unit 352. These functional blocks are realized by the processor of the evaluation device 3 reading the evaluation program P into the 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 the evaluation program P may be recorded in a computer-readable non-transitory recording medium such as a CD-ROM or an SD card and installed in the evaluation device 3 via the recording medium.

[0016] (Procedure for evaluation method) The function of the evaluation device 3 will be described with reference to Fig. 2. Fig. 2 is a flowchart showing the processing procedure of the evaluation method according to this embodiment. The evaluation method has steps S1 to S5, and the above-mentioned functional blocks of the evaluation device 3 execute steps S1 to S5. That is, the evaluation program P causes the evaluation device 3 to execute steps S1 to S5.

[0017] In step S1, the acoustic data acquisition unit 31 acquires acoustic data obtained from the subject by the sound collection device 2. The length (time) of the acoustic data is not particularly limited, but is, for example, 5 minutes.

[0018] In step S2 (extraction step), the extraction unit 32 extracts a plurality of bowel sounds from the acoustic data acquired in step S1. The acoustic data includes noise such as breathing sounds and body movement sounds in addition to bowel sounds, but bowel sounds can be extracted by a known method. In this embodiment, the extraction unit 32 extracts bowel sounds by the method described in International Publication No. 2019 / 216320.

[0019] Specifically, the extraction unit 32 detects a plurality of 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 machine-learned the relationship between bowel sounds in the acoustic data and PNCCs, and outputs a prediction score indicating the possibility that bowel sounds are included in each segment according to the input PNCC. The extraction unit 32 extracts, as bowel sounds, segments with a prediction score greater than a predetermined threshold. A bowel sound is a continuous sound, and its length (time) is indefinite.

[0020] 3 is an example of a waveform of acoustic data obtained from a subject. The dashed boxes indicate segments, and the extraction unit 32 extracts segments that are likely to include bowel sounds.

[0021] Note that normal bowel sounds are centered around frequencies of 100 to 500 Hz. Therefore, it is preferable that extraction unit 32 apply a band-pass filter that passes a frequency band including 100 to 500 Hz (for example, 80 to 1000 Hz) to the acoustic data acquired in step S1, and then extracts the bowel sounds.

[0022] In step S3 (feature calculation step) shown in Fig. 2, feature calculation unit 33 calculates a feature related to the frequency domain of each bowel sound extracted in step S2. In this embodiment, the feature is a representative frequency. The representative frequency is a frequency that typically represents the interval (pitch) of one continuous sound, and in this embodiment, the representative frequency is calculated as follows.

[0023] FIG. 4 is a flowchart showing more specific processing steps of step S3.

[0024] In step S31 (division step), the division unit 331 divides each bowel sound into a plurality of segments at regular time intervals.

[0025] In step S32 (peak frequency calculation step), peak frequency calculation unit 332 calculates the peak frequency of each segment divided in step S31. Specifically, peak frequency calculation unit 332 performs a Fourier transform on the waveform of each segment to decompose it into different frequency components, calculate the intensity of each component, and determine the frequency with the greatest intensity as the peak frequency.

[0026] For example, if one bowel sound is divided into five segments, the frequency spectra of segments 1 to 5 are obtained as shown in FIG. 5, and the frequencies corresponding to the peaks of each frequency spectrum indicated by triangular marks are the peak frequencies.

[0027] In step S33 (representative frequency selection step), the representative frequency selection unit 333 selects the maximum peak frequency from the peak frequencies calculated in step S32 as the representative frequency. In the example shown in FIG. 5, the peak frequency of segment 3 among the five peak frequencies indicated by the triangular marks is the representative frequency F R The representative frequency selection unit 333 may select, as the representative frequency, a basic statistic of the peak frequency from the peak frequencies calculated in step S32.

[0028] By executing the above steps S31 to S33 for all bowel sounds (YES in step S34), the feature amount calculation unit 33 calculates the representative frequency of all bowel sounds. Note that the feature amount calculation unit 33 does not divide each bowel sound into segments, but calculates the frequency corresponding to the peak of the frequency spectrum of one bowel sound as the representative frequency F R In step S32, instead of calculating the peak frequency of each segment, the spectral features (spectral flatness, spectral bandwidth, spectral centroid, etc.) of each segment may be calculated.

[0029] 2, the distribution data generating unit 34 generates distribution data A indicating the distribution of each feature amount (representative frequency). In this embodiment, the distribution data generating unit 34 generates the distribution data A as a histogram.

[0030] 6 is an example of the distribution data A. This distribution data A has 42 frequency bins in the range of 0 to 1000 Hz. Note that the format of the distribution data A is not limited to a histogram.

[0031] In step S5 (evaluation step) shown in Fig. 2, the evaluation unit 35 evaluates the gastrointestinal condition of the subject based on the distribution data A. In this embodiment, the evaluation unit 35 evaluates the gastrointestinal condition based on the similarity between the distribution data A and the reference distribution data B stored in the auxiliary storage device 30. Specifically, the evaluation of the gastrointestinal condition is performed as follows.

[0032] FIG. 7 is a flowchart showing more specific processing steps of step S5.

[0033] In step S51 (reference data acquisition step), the reference data acquisition unit 351 acquires reference distribution data B from the auxiliary storage device 30. The reference distribution data B is data indicating a distribution of representative frequencies of multiple bowel sounds contained in acoustic data obtained from a subject when the subject's gastrointestinal condition is in a predetermined state.

[0034] For example, when a subject with serious constipation visits a medical institution for the first time, acoustic data is acquired from the subject, and distribution data of the representative frequencies is generated by the processes in steps S2 to S4 described above. The distribution data is stored in the auxiliary storage device 30 as reference distribution data B.

[0035] 8 is an example of the reference distribution data B. The format of the reference distribution data B may be the same as that of the distribution data A shown in FIG.

[0036] 7 (similarity calculation step), the similarity calculation unit 352 calculates the similarity between the distribution data A and the reference distribution data B. In this embodiment, the similarity calculation unit 352 calculates the similarity between the distribution data A and the reference distribution data B represented by a histogram as a Pearson's correlation coefficient R. The correlation coefficient R is calculated by the following formula.

[0037]

number

[0038] The closer the correlation coefficient R is to 1, the higher the similarity, and the closer the correlation coefficient R is to 0, the lower the similarity. Note that the index representing the similarity is not limited to the correlation coefficient R, and for example, Euclidean distance, Bhattacharya distance, cosine similarity, intersection set, etc. may be used.

[0039] In step S53, the evaluation unit 35 evaluates the gastrointestinal condition of the subject based on the state of the subject (predetermined state) when the acoustic data for generating the reference distribution data B was acquired and the similarity calculated in step S52. As will be described in the examples below, the lower the similarity, the greater the change in the state of the intestines. In other words, the degree of change in the state of the subject relative to the predetermined state can be determined depending on the magnitude of the similarity, so that the state of the subject when the acoustic data for generating the distribution data A was acquired can be evaluated.

[0040] For example, assume that the subject's gastrointestinal condition was severely constipated at the time when acoustic data for generating reference distribution data B was acquired at a medical institution. The subject then acquires acoustic data and transmits it to the evaluation device 3, which generates distribution data A from the acoustic data and calculates the similarity between distribution data A and reference distribution data B. This makes it possible to evaluate how much the gastrointestinal condition has changed (i.e., improved) from the severe constipation state at the time when the acoustic data was acquired at home. In other words, the lower the similarity, the better the gastrointestinal condition can be evaluated to be.

[0041] Furthermore, since the subject can easily obtain acoustic data using a microphone at home, etc., the gastrointestinal condition can be monitored on a daily basis by continuously obtaining acoustic data and generating distribution data A.

[0042] As described above, the evaluation unit 35 can evaluate the intestinal condition of the subject. In the examples described later, it is shown that the present disclosure can grasp not only the change in the amount of gas in the intestine but also the change in the contents in the intestine. Therefore, the present disclosure can accurately evaluate the gastrointestinal condition. EXAMPLES

[0043] Examples of the present disclosure will be described below, but the present disclosure is not limited to the following examples.

[0044] In this example, it was verified whether the present disclosure can accurately evaluate changes in intestinal condition due to beverage intake or over time. The subjects were 19 people (11 men, 8 women, age: 33.37±7.82 years, height: 166.71±8.37 cm, weight: 59.09±8.84 kg, BMI: 21.16±1.90) who were determined not to have irritable bowel syndrome (IBS) according to the Rome III diagnostic criteria, and each subject underwent a beverage intake test twice.

[0045] Specifically, in the first test, each subject, who had fasted for about 12 hours, was given water at about 10°C, and the body sounds of each subject were collected as acoustic data for five minutes immediately before the ingestion and for five minutes 10 to 15 minutes after the ingestion. The second test was conducted three days after the first test. In the second test, each subject, who had fasted for about 12 hours, was given carbonated water at about 10°C, and the body sounds of each subject were collected as acoustic data for five minutes immediately before the ingestion and for five minutes 10 to 15 minutes after the ingestion.

[0046] In these tests, the equipment used to collect acoustic data was an electronic stethoscope (E-scope2, Cardionics Inc., Houston, TX, USA) and a multi-track recorder (R16 Zoom Co., Ltd., Tokyo, Japan). When collecting acoustic data, the subject was placed in a supine position, and the electronic stethoscope was fixed in a cross shape using masking tape at a position 9 cm to the right of the navel. The electronic stethoscope has two sound collection modes: heart sound mode and respiratory sound mode, and the electronic stethoscope was set to the respiratory sound mode, which can record a wider frequency range. The sampling frequency of the acoustic data during collection was 44100 Hz, and the digital resolution was 16 bits / sample.

[0047] In the evaluation device 3, a trained neural network model was used as the extraction model M to extract multiple bowel sounds from each piece of acoustic data. Specifically, the acoustic data was segmented with a segment length of 64 ms and a shift size of 16 ms, and a PNCC, which is a feature related to frequency, was extracted from each segment. The extracted PNCC was then input to the neural network model, and it was determined whether each segment contained bowel sounds based on the output of the neural network. When there were consecutive segments containing bowel sounds (BS episodes), the consecutive segments were treated as one BS episode.

[0048] Next, the representative frequency of each bowel sound was calculated to generate distribution data. Specifically, the distribution data was generated in the following procedure. (1) As preprocessing, the sampling frequency of the bowel sounds was downsampled to 4000 Hz, and a band-pass filter was applied to pass the 80 to 1000 Hz band, which is the main frequency band of bowel sounds. (2) Each bowel sound was segmented with a segment length of 64 ms (256 samples) and a shift size of 16 ms (64 samples). (3) An FFT was performed for each segment using a window of 128 ms (512 samples) to determine the peak frequency. (4) The frequency with the maximum power among the peak frequencies obtained for each segment was selected as the representative frequency of bowel sounds. (5) Using representative values ​​of bowel sounds generated within a certain period of time, histogram distribution data was generated in the frequency range of 0 to 1000 Hz with 42 frequency bins.

[0049] From the acoustic data obtained from each subject, a total of four distribution data were generated: two distribution data generated from acoustic data acquired before and after water intake in the first test, and two distribution data generated from acoustic data acquired before and after carbonated water intake in the second test. Here, the timing at which acoustic data was acquired before water intake in the first test is referred to as BWI (Before water intake), and the timing at which acoustic data was acquired after water intake in the first test is referred to as AWI (After water intake). In addition, the timing at which acoustic data was acquired before carbonated water intake in the second test is referred to as BSI (Before soda intake), and the timing at which acoustic data was acquired after carbonated water intake in the second test is referred to as ASI (After soda intake). That is, the four distribution data are distribution data generated from acoustic data acquired in BWI, AWI, BSI, and ASI, and in this embodiment, these distribution data are simply referred to as BWI, AWI, BSI, and ASI.

[0050] Next, the similarity of the distribution data for the following three combinations from the four distribution data was calculated as Pearson's correlation coefficient R. ·BWI-AWI · BSI-ASI ·BSI-BWI

[0051] Figure 9 is a graph showing the average value of the distribution data similarity for these three combinations for each subject. The correlation coefficient R(BWI-AWI), which indicates the similarity between BWI and AWI, is close to 1, suggesting that there is little change in gastrointestinal condition before and after water intake. Previous research has shown that water intake hardly increases gastrointestinal motility, and the results of this example show a similar tendency to the results of previous research.

[0052] The correlation coefficient R(BSI-ASI), which indicates the similarity between BSI and ASI, is significantly smaller than the above correlation coefficient R(BWI-AWI). This result indicates that the change in gastrointestinal state before and after ingestion of carbonated water is greater than the change in gastrointestinal state before and after ingestion of water. Therefore, it is suggested that the present disclosure makes it possible to grasp the change in gastrointestinal state caused by ingestion of carbonated water.

[0053] Furthermore, the correlation coefficient R (BSI-BWI), which indicates the similarity between BSI and BWI, is significantly smaller than the above two correlation coefficients R. This result indicates that the change in intestinal condition between before water intake in the first test and before carbonated water intake in the second test is greater due to the influence of lifestyle and the like caused by the different measurement dates than the change in gastrointestinal condition before and after water intake and before and after carbonated water intake. Therefore, it is suggested that the present disclosure captures not only the change in gastrointestinal condition when carbonated water is taken, but also the change in gastrointestinal condition due to the influence of lifestyle and the like.

[0054] (Comparative Example) As a comparative example, bowel sounds were extracted from the acoustic data obtained from each of the 19 subjects mentioned above, and further, BS power, BS length, BS frequency (No. of BS detected), and BS occurrence interval (Sound to Sound Interval, SSI) were extracted as time domain features of the bowel sounds.

[0055] Figures 10(a)-(d) are box plots showing the BS power, BS length, BS frequency, and BS occurrence interval extracted from the acoustic data obtained before carbonated water intake (BSI), before water intake (BWI), after carbonated water intake (ASI), and after water intake (AWI), respectively. Figure 10 shows that the time domain features are significantly different before and after carbonated water intake (BSI-ASI), but no significant difference was observed before and after water intake (BWI-AWI). In addition, the time domain features were not significantly different before and after water intake (BWI) and before carbonated water intake (BSI), i.e., before and after the passage of three days.

[0056] Therefore, while time domain features of bowel sounds can sensitively detect changes in gastrointestinal motility due to the ingestion of carbonated water, they are thought to be less sensitive in detecting changes in gastrointestinal condition due to lifestyle, etc. [Explanation of symbols]

[0057] 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 Split section 332 Peak Frequency Calculation Unit 333 Representative Frequency Selection Unit 34 Distribution Data Generation Unit 35 Evaluation Section 351 Reference Data Acquisition Unit 352 Similarity calculation unit A Distribution Data B. Reference distribution data M Extraction Model P Evaluation Program

Claims

1. An evaluation device for evaluating a gastrointestinal condition of a subject, comprising: an extracting unit for extracting a plurality of bowel sounds from acoustic data obtained from the subject; a feature calculation unit that calculates a feature associated with a frequency domain of each of the bowel sounds; a distribution data generating unit that generates distribution data indicating a distribution of each of the feature amounts; an evaluation unit that evaluates a gastrointestinal condition of the subject based on the distribution data; Equipped with The evaluation unit is a reference data acquiring unit that acquires reference distribution data indicating a distribution of the feature amounts of a plurality of bowel sounds included in acoustic data obtained from the subject when the gastrointestinal condition of the subject is in a predetermined state; a similarity calculation unit that calculates a similarity between the distribution data generated by the distribution data generation unit and the reference distribution data; Equipped with An evaluation device that evaluates the state based on the predetermined state and the similarity.

2. An evaluation device for evaluating a gastrointestinal condition of a subject, comprising: an extracting unit for extracting a plurality of bowel sounds from acoustic data obtained from the subject; a feature calculation unit that calculates a feature associated with a frequency domain of each of the bowel sounds; a distribution data generating unit that generates distribution data indicating a distribution of each of the feature amounts; an evaluation unit that evaluates a gastrointestinal condition of the subject based on the distribution data; Equipped with the feature amount is a representative frequency, The feature amount calculation unit is a division unit for dividing each of the bowel sounds into a plurality of segments at regular time intervals; A peak frequency calculation unit that calculates a peak frequency of each of the segments; a representative frequency selection unit that selects a maximum peak frequency from the peak frequencies as the representative frequency; An evaluation device comprising:

3. The evaluation device according to claim 1 , wherein the feature amount is a representative frequency.

4. The feature amount calculation unit is a division unit for dividing each of the bowel sounds into a plurality of segments at regular time intervals; A peak frequency calculation unit that calculates a peak frequency of each of the segments; a representative frequency selection unit that selects a maximum peak frequency from the peak frequencies as the representative frequency; The evaluation device according to claim 3 .

5. The evaluation device according to claim 1 or 4, a sound collection device for obtaining the acoustic data from the subject; An evaluation system comprising:

6. A method for evaluating a gastrointestinal condition of a subject by a computer, comprising: extracting a plurality of bowel sounds from acoustic data obtained from the subject; a feature calculation step of calculating a feature related to a frequency domain of each of the bowel sounds; a distribution data generating step of generating distribution data indicating a distribution of each of the feature amounts; evaluating a gastrointestinal condition of the subject based on the distribution data; having The evaluation step includes: a reference data acquiring step of acquiring reference distribution data indicating a distribution of the feature amounts of a plurality of bowel sounds included in acoustic data obtained from the subject when the gastrointestinal condition of the subject is in a predetermined state; a similarity calculation step of calculating a similarity between the distribution data generated in the distribution data generation step and the reference distribution data; having The evaluation method includes evaluating the state based on the predetermined state and the similarity.

7. A method for evaluating a gastrointestinal condition of a subject by a computer, comprising: extracting a plurality of bowel sounds from acoustic data obtained from the subject; a feature calculation step of calculating a feature related to a frequency domain of each of the bowel sounds; a distribution data generating step of generating distribution data indicating a distribution of each of the feature amounts; evaluating a gastrointestinal condition of the subject based on the distribution data; having the feature amount is a representative frequency, The feature amount calculation step includes: a segmentation step of dividing each of the bowel sounds into a plurality of segments at regular time intervals; a peak frequency calculation step of calculating a peak frequency of each of the segments; a representative frequency selection step of selecting a maximum peak frequency from the peak frequencies as the representative frequency; The evaluation method has the following features:

8. The evaluation method according to claim 6 , wherein the feature amount is a representative frequency.

9. The feature amount calculation step includes: a segmentation step of dividing each of the bowel sounds into a plurality of segments at regular time intervals; a peak frequency calculation step of calculating a peak frequency of each of the segments; a representative frequency selection step of selecting a maximum peak frequency from the peak frequencies as the representative frequency; The evaluation method according to claim 8 , comprising:

10. 1. An assessment program for assessing a gastrointestinal condition of a subject, comprising: extracting a plurality of bowel sounds from acoustic data obtained from the subject; a feature calculation step of calculating a feature related to a frequency domain of each of the bowel sounds; a distribution data generating step of generating distribution data indicating a distribution of each of the feature amounts; evaluating a gastrointestinal condition of the subject based on the distribution data; on the computer, The evaluation step includes: a reference data acquiring step of acquiring reference distribution data indicating a distribution of the feature amounts of a plurality of bowel sounds included in acoustic data obtained from the subject when the gastrointestinal condition of the subject is in a predetermined state; a similarity calculation step of calculating a similarity between the distribution data generated in the distribution data generation step and the reference distribution data; having an evaluation program that evaluates the state based on the predetermined state and the similarity degree;

11. An evaluation program for evaluating a gastrointestinal condition of a subject, comprising: extracting a plurality of bowel sounds from acoustic data obtained from the subject; a feature calculation step of calculating a feature related to a frequency domain of each of the bowel sounds; a distribution data generating step of generating distribution data indicating a distribution of each of the feature amounts; evaluating a gastrointestinal condition of the subject based on the distribution data; on the computer, the feature amount is a representative frequency, The feature amount calculation step includes: a segmentation step of dividing each of the bowel sounds into a plurality of segments at regular time intervals; a peak frequency calculation step of calculating a peak frequency of each of the segments; a representative frequency selection step of selecting a maximum peak frequency from the peak frequencies as the representative frequency; An evaluation program.

12. The evaluation program according to claim 10 , wherein the feature amount is a representative frequency.

13. The feature amount calculation step includes: a segmentation step of dividing each of the bowel sounds into a plurality of segments at regular time intervals; a peak frequency calculation step of calculating a peak frequency of each of the segments; a representative frequency selection step of selecting a maximum peak frequency from the peak frequencies as the representative frequency; The evaluation program according to claim 12 , further comprising:

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