JP2021053460A - Respiratory sound analysis apparatus, respiratory sound analysis method, computer program, and recording medium - Google Patents

Abstract

To output information on a bubbling sound contained in a respiratory sound.SOLUTION: A respiratory sound analysis apparatus includes detection means (140) for detecting information on a minimum value of the strength of a respiratory sound spectrum, and output means (150 and 160) for outputting information on a normal respiratory sound and a bubbling sound contained in the respiratory sound separately on the basis of the information on the minimum value. The output means calculates an envelope curve of the minimum value from the information on a plurality of the minimum values, outputs information on the bubbling sound on the basis of a part corresponding to an area above the envelope curve, and outputs information on the normal respiratory sound on the basis of a part corresponding to an area below the envelope curve.SELECTED DRAWING: Figure 9

Description

The present invention relates to a respiratory sound analyzer and a respiratory sound analysis method for analyzing a respiratory sound including a plurality of sound types, and a technical field of a computer program and a recording medium.

As a device of this type, there is known a device that discriminates between a normal sound type and an abnormal sound type with respect to the breath sounds of a living body detected by an electronic stethoscope or the like. For example, Patent Document 1 proposes a technique of extracting a peak that is a candidate for a la sound from the envelope of an analysis signal and detecting the la sound based on at least one of the instantaneous frequency or the instantaneous bandwidth corresponding to the peak. Has been done. Patent Document 2 proposes a technique of performing feature determination based on the amplitude of a biosonic envelope. Patent Document 3 proposes a technique of obtaining an envelope of a respiratory sound spectrum and extracting a feature value by approximating it to a model waveform.

Japanese Unexamined Patent Publication No. 2009-106574 Japanese Unexamined Patent Publication No. 2013-123494 Japanese Unexamined Patent Publication No. 2013-123495

As a sound type included in the breath sounds, a blister sound, which is one of the crackles, is known. Blistering sounds are useful in diagnosing, for example, bronchiectasis, chronic bronchitis, and diffuse panbronchiolitis. However, in the techniques described in Patent Documents 1 to 3 described above, there is a technical problem that it is difficult to detect the blistering sound separately from other sound types (for example, normal breath sounds).

Examples of the problems to be solved by the present invention include the above. An object of the present invention is to provide a respiratory sound analyzer and a respiratory sound analysis method capable of preferably acquiring information on water bubble sound, and a computer program and a recording medium.

Respiratory sound analyzers for solving the above problems include a calculation unit that calculates a value indicating the ratio of sound types contained in the respiratory sound based on the respiratory sound spectrum and a predetermined basis, and a minimum of the value. The output means includes a detection means for detecting information on a value and an output means for separately outputting information on a normal breath sound and a water bubble sound included in the breath sound based on the information on the minimum value. The wrapping line of the minimum value is calculated from the information on the plurality of minimum values, the information on the water bubble sound is output based on the portion corresponding to the region above the wrapping line, and the information on the water bubble sound is output, and the wrapping line below the wrapping line is output. Information about the normal breath sounds is output based on the portion corresponding to the region.

Respiratory sound analysis methods for solving the above problems include a calculation step of calculating a value indicating the proportion of sound types contained in the respiratory sound based on the respiratory sound spectrum and a predetermined basis, and a minimum of the value. The output means includes a detection step of detecting information on a value and an output step of separately outputting information on a normal breath sound and a water bubble sound included in the breath sound based on the information on the minimum value. The wrapping line of the minimum value is calculated from the information on the plurality of minimum values, the information on the water bubble sound is output based on the portion corresponding to the region above the wrapping line, and the information on the water bubble sound is output, and the wrapping line below the wrapping line is output. Information about the normal breath sounds is output based on the portion corresponding to the region.

A computer program for solving the above problems relates to a calculation step of calculating a value indicating the ratio of sound types contained in the breath sounds based on the spectrum of the breath sounds and a predetermined basis, and a minimum value of the values. The output means includes a detection step of detecting information and an output step of separately outputting information on normal breath sounds and water bubble sounds included in the breath sounds based on the information on the minimum value. The wrapping line of the minimum value is calculated from the information on the minimum value, and the information on the water bubble sound is output based on the portion corresponding to the region above the wrapping line, and in the region below the wrapping line. Have the computer execute a breath sounds analysis method that outputs information about the normal breath sounds based on the corresponding part.

The computer program described above is recorded as a recording medium for solving the above problems.

It is a block diagram which shows the whole structure of the respiratory sound analysis apparatus which concerns on this Example. It is a flowchart which shows the operation of the respiratory sound analyzer which concerns on this Example. It is a graph which shows an example of a frequency analysis method. It is a figure which shows an example of the frequency analysis result. It is a graph which shows an example of the coupling coefficient of normal breath sounds. It is a graph which shows an example of the coupling coefficient of a blister sound. It is a conceptual diagram which shows the calculation method of a coupling coefficient. It is a figure which shows the relationship between a spectrum, a basis and a coupling coefficient. It is a conceptual diagram which shows the separation method using the envelope of the minimum value. It is a graph which shows the fluctuation width of a coupling coefficient. It is a conceptual diagram which shows the method of changing the minimum value filter width. It is a conceptual diagram which shows the separation method based on the output of the minimum value filter. It is a figure which shows an example of the display method of the analysis result. It is a conceptual diagram which shows the output method of a blister sound component.

<1>
The respiratory sound analyzer according to the present embodiment is a detection means for detecting information on the minimum value of the intensity of the breath sound spectrum, and a normal breath sound and a water bubble sound included in the breath sound based on the information on the minimum value. It is provided with an output means for separately outputting information about and.

According to the respiratory sound analyzer according to the present embodiment, during its operation, the detection means first detects information on the minimum value of the intensity of the respiratory sound spectrum. Here, "information about the minimum value" is not limited to information that directly indicates the minimum value, but includes information that can indirectly derive the minimum value. The method of detecting the information regarding the minimum value is not particularly limited.

When the information on the minimum value is detected, the output means outputs the information on the normal breath sounds and the blisters sounds included in the breath sounds separately. That is, the information on the normal breath sounds and the information on the blisters sounds are output in a separated state or a separable state. The "information on normal breath sounds" and "information on water bubble sounds" here are not limited to information that directly indicates normal breath sounds or water bubble sounds, and include normal breath sounds or water bubble sounds included in breath sounds. The purpose is to include information that can be indirectly derived.

According to the research by the inventor of the present application, it has been found that there is a difference in the time-series fluctuation of the spectral intensity between the normal breath sounds and the blisters sounds included in the breath sounds. Specifically, the spectrum corresponding to normal breath sounds has a relatively small time-series fluctuation, and the spectrum corresponding to a blister sound has a relatively large time-series fluctuation. Therefore, if the breath sounds include normal blister sounds and blister sounds, it can be estimated that the portion that greatly changes the intensity of the spectrum is the blister sound component, and the other portion is the normal breath sound component.

Here, it is considered that the minimum value of the intensity of the respiratory sound spectrum is located at the lower limit of the portion where the intensity of the spectrum fluctuates greatly in time series. Therefore, based on the information regarding the minimum value of the intensity of the respiratory sound spectrum, it is possible to separate a component having a large time-series fluctuation and a component having a small time-series fluctuation with respect to the respiratory sound spectrum.

As a result of the above, according to the respiratory sound analyzer according to the present invention, it is possible to analyze the respiratory sound spectrum and output information on normal breath sounds and information on blisters separately.

<2>
In one aspect of the breath sounds analyzer according to the present embodiment, the output means outputs the ratio of the normal breath sounds and the blisters sound included in the breath sounds.

According to this aspect, since it is possible to know the ratio of the normal breath sounds and the blisters sounds in the breath sounds, it is possible to appropriately diagnose the health condition based on the breath sounds, for example. It becomes. In addition, the ratio may be determined for components other than normal breath sounds and blisters sounds.

<3>
In another aspect of the respiratory sound analyzer according to the present embodiment, the detection means detects information on the minimum value of the intensity of the respiratory sound spectrum for each first time interval as information on the minimum value.

According to this aspect, the detection means detects information about the minimum intensity of the respiratory sound spectrum at each first time interval. The information about the detected minimum value is treated as the information about the minimum value. Therefore, the output means outputs information on normal breath sounds and blistering sounds based on the information on the detected minimum value. The term "information about the minimum value" is not limited to information that directly indicates the minimum value, but includes information that can indirectly derive the minimum value. The "first time interval" is a time interval set to accurately detect the minimum value of the respiratory sound spectrum as the minimum value in a predetermined time interval, and is used as a fluctuation cycle of the blister sound spectrum or the like. Set accordingly. The first time interval is set in advance by, for example, a preliminary simulation, but may be appropriately changed according to the fluctuation of the spectrum of the respiratory sound to be analyzed.

In general, the minimum value can be detected as, for example, a place where a parameter that has been decreasing tends to increase, but when the fluctuation of the parameter fluctuates finely, an erroneous minimum value is detected. There is also a risk that it will end up. On the other hand, according to the above-described method of detecting the minimum value for each time interval, the minimum value can be detected periodically, so that the minimum value can be detected extremely easily and accurately. Therefore, it is possible to accurately separate the information on the normal breath sounds included in the breath sounds and the information on the blisters sound.

<4>
In the mode of detecting the information on the minimum value for each first time interval described above, the changing means for changing the first time interval may be further provided.

In this case, since the first time interval is variable, even if the information on the minimum value cannot be appropriately detected with the predetermined first time interval due to individual differences, for example, depending on the situation. The first time interval can be changed to appropriately detect information about the minimum value.

The first time interval is changed according to, for example, fluctuations in the spectrum of past (immediately preceding) breath sounds. Specifically, when the fluctuation cycle of the respiratory sound spectrum is known, the information on the minimum value can be detected more accurately by changing the first time interval according to the fluctuation cycle.

<5>
In another aspect of the respiratory sound analyzer according to the present embodiment, the output means calculates the envelope of the minimum value from the information on the plurality of minimum values, and the portion corresponding to the region above the envelope. Information on the normal breath sounds is output based on the above, and information on the blisters sounds is output based on the portion corresponding to the region below the envelope.

According to this aspect, the envelope of the minimum value is calculated based on the information regarding the plurality of minimum values. The envelope is calculated as, for example, a line connecting adjacent minimum values on the time axis. However, the method of calculating the envelope is not particularly limited, and it may be appropriately calculated by using various approximation methods or the like.

Here, the portion corresponding to the region above the envelope of the minimum value is considered to be a component having a relatively large time-series fluctuation. On the other hand, the portion corresponding to the region below the envelope of the minimum value is considered to be a component having relatively small time-series fluctuation. Therefore, the portion corresponding to the region above the envelope of the minimum value is output as information regarding the blister sound having a relatively large time-series fluctuation. In addition, the portion corresponding to the region below the envelope of the minimum value is output as information on normal breath sounds with relatively small time-series fluctuations.

As described above, if the envelope with the minimum value is used, it is possible to easily separate the information on the normal breath sounds contained in the breath sounds and the information on the blisters sound.

<6>
In another aspect of the respiratory sound analyzer according to the present embodiment, the output means outputs information on the blistering sound as an average value or a median value for each second time interval.

According to this aspect, the information regarding the blistering sound is not output as it is, but is output after the average value or the median value for each second time interval is calculated. The "second time interval" here is a value set to appropriately suppress and output fluctuations in information related to blister sound, as will be described later, and is used in the fluctuation cycle of information related to blister sound, etc. It is set appropriately according to it. The second time interval may be the same value as the first time interval described above.

As already mentioned, the blister sound is a component having a relatively large time-series fluctuation. Therefore, if the output is performed as it is, the value indicating the analysis result displayed on a display or the like also fluctuates greatly, and it may be difficult to visually recognize the intensity of the blistering sound.

On the other hand, in the method of outputting the average value or the median value described above, since the time-series fluctuation of the blister sound is suppressed, it is possible to output in a state where the actual intensity of the blister sound can be easily determined. Therefore, it is possible to effectively use the information regarding the blistering sound output as the analysis result.

<7>
In another aspect of the respiratory sound analyzer according to the present embodiment, a decomposition means for decomposing the respiratory sound spectrum based on a reference spectrum as a reference for classifying the breath sounds is provided, and the detection means is the decomposition means. Detects information about the minimum value of the intensity of the respiratory sound spectrum decomposed by, and the output means separately outputs information about normal breath sounds and water bubble sounds included in the respiratory sounds decomposed by the decomposition means. To do.

According to this aspect, the spectrum of breath sounds is first decomposed by the decomposition means. The decomposition means decomposes the respiratory sound spectrum based on a plurality of reference spectra that serve as criteria for classifying the respiratory sounds. The "reference spectrum" here refers to each sound type in order to classify a plurality of sound types (for example, normal breath sounds, continuous rales, crepitus, etc.) included in the breath sounds. This is a preset spectrum. The reference spectrum is set, for example, as a spectrum having a shape peculiar to each sound type.

By using the reference spectrum, it is possible to know the proportion of each sound type corresponding to each reference spectrum included in the breath sound spectrum. That is, the spectrum of breath sounds can be decomposed into each sound type corresponding to the reference spectrum. However, it is not necessary that the normal breath sounds and the blisters sound are decomposed at the time of decomposition in the decomposition means.

The detection of information about the minimum value is performed on the spectrum of breath sounds after being decomposed by the decomposition means (that is, the spectrum decomposed to include normal breath sounds and blistering sounds). Similarly, the output of information on the normal breath sounds and the blisters sounds is also performed on the spectrum of the breath sounds after being decomposed by the decomposition means.

As described above, if the information on the normal breath sounds and the blisters sounds is further separated after the decomposition by the reference spectrum is performed in advance, the components corresponding to the sound types other than the normal breath sounds and the blisters sounds are excluded. Separation can be done in the state. Therefore, due to the existence of sound types other than normal breath sounds and blisters sounds, a situation may occur in which information on normal breath sounds and information on blisters sounds cannot be output separately. Can be suppressed.

<8>
In the aspect provided with the above-mentioned decomposition means, the reference spectrum may include those corresponding to the normal breath sounds and the blisters sounds.

In this case, the spectrum including the normal breath sounds and the blisters can be suitably decomposed from the spectrum of the breath sounds by the reference spectrum corresponding to the normal breath sounds and the blisters. Therefore, it is possible to preferably output the information on the normal breath sounds and the information on the blisters sound using the information on the subsequent minimum values.

It should be noted that the reference spectrum corresponding to the normal breath sounds and the blister sounds has a predetermined difference in the amount of fluctuation over time in order to accurately separate the information on the normal breath sounds and the information on the blister sounds. It is preferable that it is set as a thing.

<9>
The respiratory sound analysis method according to the present embodiment has a detection step of detecting information on the minimum value of the intensity of the respiratory sound spectrum, and a normal breath sound and a water bubble sound included in the breath sound based on the information on the minimum value. It is provided with an output process that outputs information about and separately.

According to the respiratory sound analysis method according to the present embodiment, similarly to the respiratory sound analysis device according to the present embodiment described above, the respiratory sound spectrum is analyzed to obtain information on normal breath sounds and information on water bubble sounds. It is possible to output them separately.

In the respiratory sound analysis method according to the present embodiment, it is possible to adopt various aspects similar to the various aspects in the respiratory sound analysis device according to the above-described embodiment.

<10>
The computer program according to the present embodiment relates to a detection step of detecting information on the minimum value of the intensity of the breath sound spectrum, and a normal breath sound and a water bubble sound included in the breath sound based on the information on the minimum value. Have the computer perform an output process that outputs information separately.

According to the computer program according to the present embodiment, the computer can execute the same processing as the above-described respiratory sound analysis method according to the present embodiment. Therefore, the respiratory sound spectrum is analyzed to provide information on the normal respiratory sound. And the information about the sound of water bubbles can be output separately.

In the computer program according to the present embodiment, it is possible to adopt various aspects similar to the various aspects in the respiratory sound analyzer according to the above-described embodiment.

<11>
The computer program described above is recorded on the recording medium according to the present embodiment.

According to the recording medium according to the present embodiment, by executing the above-mentioned computer program by a computer, the spectrum of the breath sounds is analyzed, and the information about the normal breath sounds and the information about the blisters sound are output separately. Is possible.

The respiratory sound analyzer and the respiratory sound analysis method according to the present embodiment, and the actions and other gains of the computer program and the recording medium will be described in more detail in the examples shown below.

Hereinafter, examples of a respiratory sound analyzer, a respiratory sound analysis method, and a computer program and a recording medium will be described in detail with reference to the drawings.

<Overall configuration>
First, the overall configuration of the respiratory sound analyzer according to this embodiment will be described with reference to FIG. Here, FIG. 1 is a block diagram showing the overall configuration of the respiratory sound analyzer according to the present embodiment.

In FIG. 1, the respiratory sound analysis device according to the present embodiment has, as main components, a biological sound acquisition unit 110, a frequency analysis unit 120, a coupling coefficient calculation unit 130, a filter creation unit 140, and a signal intensity calculation. It is configured to include a unit 150 and an analysis result output unit 160.

The biological sound acquisition unit 110 is configured as a sensor or the like capable of acquiring the breath sounds of the living body. The biological sound acquisition unit 110 is composed of, for example, an ECM (Electret Condenser Microphone), a microphone using a piezo, a vibration sensor, or the like. The breath sounds acquired by the biological sound acquisition unit 110 are output to the frequency analysis unit 120.

The frequency analysis unit 120 is configured to be capable of performing frequency analysis such as a fast Fourier transform. The analysis result by the frequency analysis unit 120 is output to the coupling coefficient calculation unit 130.

The coupling coefficient calculation unit 130 is a specific example of the “decomposition means”, and is configured to be able to execute a decomposition process based on the basis of the frequency-analyzed spectrum. The coupling coefficient calculation unit 130 has a storage means (not shown) for storing a predetermined basis. The coupling coefficient calculated by the coupling coefficient calculating unit 130 is output to the filter creating unit 140.

The filter creating unit 140 is a specific example of the “detection means”, and is configured to create a minimum value filter for separating the normal breath sounds component and the blister sound component from the coupling coefficient. Specifically, the filter creation unit is configured to store the coupling coefficient in time series, and creates a minimum value filter that detects the minimum value from the latest stored coupling coefficients. The minimum value filter created by the filter creation unit 140 is configured to be output to the signal strength calculation unit 150.

The signal strength calculation unit 150 can calculate the signal strength of each of the normal breath sounds component and the blister sound component by separating them from the spectrum to be analyzed by using the minimum value filter created by the filter creation unit 140. It is composed of. The signal strength calculated by the signal strength calculation unit 150 is output to the analysis result output unit 160.

The analysis result output unit 160 outputs the analysis result based on the signal strength (that is, the amount of components of each sound type) calculated by the signal strength calculation unit 150, for example, a device capable of displaying an image such as a display, or a sound of a speaker or the like. Is configured to be output to a device that can output.

The signal strength calculation unit 150 and the analysis result output unit 160 are specific examples of the “output means”.

<Operation explanation>
Next, the operation of the respiratory sound analyzer according to this embodiment will be described with reference to FIG. 2 in addition to FIG. Here, FIG. 2 is a flowchart showing the operation of the respiratory sound analyzer according to the present embodiment. Here, a brief description will be given for grasping the overall flow of the processing executed by the respiratory sound analyzer according to the present embodiment. Details of each process will be described later.

In FIGS. 1 and 2, when the respiratory sound analyzer according to the present embodiment is operated, the respiratory sound signal is first acquired by the biological sound acquisition unit 110 (step S101).

When the breath sound signal is acquired, the frequency analysis unit 120 executes frequency analysis (step S102). The frequency analysis unit 120 may detect the peak frequency from the frequency analysis result.

After the frequency analysis, the coupling coefficient calculation unit 130 calculates the coupling coefficient of each sound type corresponding to a predetermined basis (that is, a value indicating the ratio of each sound type included in the breath sounds) (step S103). The coupling coefficient calculation unit 130 calculates the coupling coefficient corresponding to at least the components including normal breath sounds and blistering sounds.

Subsequently, the filter creation unit 140 creates a minimum value filter, and calculates the minimum value from the latest N time-series values for the coupling coefficient corresponding to the components including the normal breath sounds and the blisters sounds (step). S104). The calculation of the minimum value is performed a plurality of times for every N points.

When the minimum value filter is created, the signal intensity calculation unit 150 uses the calculated minimum value to determine the signal intensity of the normal breath sounds and the blister sounds (that is, the value indicating the component amounts of the normal breath sounds and the blister sounds). The calculation is performed (step S105).

When the signal strength is calculated, the analysis result output unit 160 generates image data or the like indicating the signal strength and displays it as the analysis result on an external display or the like (step S106).

After that, it is determined whether or not to continue the analysis process (step S107). If it is determined that the analysis process is to be continued (step S107: YES), the process from step S101 is executed again. When it is determined that the analysis process is not continued (step S107: NO), the series of processes ends.

Hereinafter, each of the above-mentioned processes will be individually and specifically described.

<Frequency analysis>
First, the frequency analysis process of the breath sound signal will be described in detail with reference to FIGS. 3 and 4. Here, FIG. 3 is a graph showing an example of the frequency analysis method, and FIG. 4 is a diagram showing an example of the frequency analysis result.

In FIG. 3, a frequency analysis is first performed on the acquired breath sound signal. The frequency can be performed by utilizing an existing technique such as a fast Fourier transform. In this embodiment, the amplitude value for each frequency (that is, the amplitude spectrum) is used as the frequency analysis result. The sampling frequency, window size, and window function (for example, Hanning window) at the time of data acquisition may be appropriately determined.

As shown in FIG. 4, the frequency analysis result is obtained as being composed of _{n values (y 1} , y ₂ , y ₃ , ..., Y n). Note that "n" is a value determined by the window size or the like in the frequency analysis.

<Calculation of coupling coefficient>
Next, the calculation process of the coupling coefficient will be described in detail with reference to FIGS. 5 to 8. Here, FIG. 5 is a graph showing an example of the coupling coefficient of normal breath sounds, and FIG. 6 is a graph showing an example of the coupling coefficient of blister sounds. Further, FIG. 7 is a conceptual diagram showing a method of calculating the coupling coefficient, and FIG. 8 is a diagram showing the relationship between the spectrum and the basis and the coupling coefficient.

As shown in FIGS. 5 and 6, the coupling coefficient corresponding to normal breath sounds is preferably calculated assuming that the amount of fluctuation over time is relatively small. On the other hand, the coupling coefficient corresponding to the blistering sound is preferably calculated assuming that the amount of fluctuation over time is relatively large. Therefore, the basis used for calculating the coupling coefficient is determined to cause a predetermined difference between the fluctuation amount of the coupling coefficient corresponding to the normal breath sounds and the fluctuation amount of the coupling coefficient corresponding to the blistering sound. A plurality of bases for calculating the coupling coefficient are determined to correspond to each sound type, and are treated as a base set.

As shown in FIG. 7, normal breath sounds are applied to the spectrum of breath sounds including normal breath sounds and blisters sounds by applying the same basis (that is, the basis corresponding to both normal breath sounds and blisters). The coupling coefficient of sound and blistering sound can be obtained. In FIG. 7, the coupling coefficient corresponding to the normal breath sounds and the coupling coefficient corresponding to the blister sounds are shown separately, but at this stage, the coupling coefficient corresponding to the normal breath sounds and the coupling corresponding to the blister sounds are shown separately. The coefficients may not be calculated separately. That is, one coupling coefficient corresponding to the normal breath sounds and the blisters sounds may be calculated.

Here, the relationship between the spectrum y to be analyzed, the basis h (f), and the coupling coefficient u can be expressed by the following mathematical formula (1).

図８に示すように、スペクトルｙ及び各基底ｈ（ｆ）は、ｎ個の値を有している。他方、結合係数は、ｍ個の値を有している。なお、「ｍ」は、基底集合に含まれる基底の数である。

As shown in FIG. 8, the spectrum y and each basis h (f) have n values. On the other hand, the coupling coefficient has m values. Note that "m" is the number of bases included in the base set.

In the respiratory sound analyzer according to this embodiment, the coupling coefficient of each basis included in the basis set is calculated by using the non-negative matrix factorization. Specifically, u that minimizes the optimization reference function D represented by the following mathematical formula (2) (however, each component value of u is non-negative) may be obtained.

なお、一般的な非負値行列因子分解は、基底スペクトルの集合を表す基底行列と、結合係数を表すアクティベーション行列を共に算出する手法であるが、本実施例においては、基底行列を固定して結合係数のみを算出している。

Note that general non-negative matrix factorization is a method of calculating both a basis matrix representing a set of basis spectra and an activation matrix representing a coupling coefficient, but in this embodiment, the basis matrix is fixed. Only the coupling coefficient is calculated.

Incidentally, as a means for calculating the coupling coefficient, an approximation method other than the nonnegative matrix factorization may be used. However, even in this case, the condition that it is non-negative is desired.

<Creation of minimum value filter>
Next, the process of creating the minimum value filter will be described in detail with reference to FIGS. 9 to 11. Here, FIG. 9 is a conceptual diagram showing a separation method using an envelope having a minimum value, and FIG. 10 is a graph showing a fluctuation range of the coupling coefficient. Further, FIG. 11 is a conceptual diagram showing a method of changing the minimum value filter width.

In FIG. 9, the minimum value filter is used to separate the coupling coefficient of the sound type including the normal breath sounds and the blister sounds into the normal breath sounds component and the blister sound component. The minimum value filter is a filter for calculating the envelope of the minimum value of the coupling coefficient, and detects the minimum value at a predetermined time interval as the minimum value of the coupling coefficient. When the envelope is calculated using the minimum value filter, the lower part of the envelope is the component with small time-series fluctuation (that is, the normal sound absorption component), and the upper part of the envelope is the component with large time-series fluctuation. (That is, it can be separated as a blister sound component).

In FIG. 10, a predetermined time interval (that is, a time interval for detecting the minimum value), which is an example of the “first time interval”, is set as the minimum value filter width in the minimum value filter. The minimum value filter width here is set to be the same width as the one-time fluctuation width of the coupling coefficient (specifically, the interval from when the coupling coefficient starts to rise to when it falls completely). More specifically, a filter that outputs the minimum value of the latest N time-series values of the coupling coefficient, with N points having the same width as the one-time fluctuation width of the coupling coefficient. Is created.

In FIG. 11, the minimum value filter width may be a fixed value, but may be variable depending on the situation. Specifically, the width between the minimum values in the time-series change of the coupling coefficient may be calculated each time, and the width may be used as the minimum value filter width. In this case, even if the width between the minimum values of the coupling coefficient is different due to individual differences or the like, the minimum value can be detected accurately.

<Calculation of signal strength>
Next, the signal strength calculation process will be described in detail with reference to FIG. Here, FIG. 12 is a conceptual diagram showing a separation method based on the output of the minimum value filter.

In FIG. 12, when the minimum value filter is applied to the coupling coefficient of sound types including normal breath sounds and blistering sounds, the minimum value for every N points (that is, the minimum value of the coupling coefficient) is output. Then, as described with reference to FIG. 9, the lower portion of the envelope connecting the minimum values of the coupling coefficient can be separated as a normal respiratory sound component. In addition, the upper portion of the envelope can be separated as a blister sound component. The output value of each component thus separated is output as a signal strength (that is, indicating the ratio of normal breath sounds and blistering sounds).

<Display of analysis results>
Next, the display processing of the analysis result will be described in detail with reference to FIGS. 13 and 14. Here, FIG. 13 is a diagram showing an example of a method of displaying the analysis result. Further, FIG. 14 is a conceptual diagram showing a method of outputting a blister sound component.

In FIG. 13, the analysis result is displayed as an image showing the signal intensities of the normal breath sounds and the blisters sounds. Here, for example, when the output of the normal breath sounds at time t is A and the output of the blisters sound is B (see the graph on the left side of the figure), the normal breath sounds intensity is relatively small A. A bar graph indicating that the blisters sound intensity is relatively high B is displayed in the display area 200. According to such a display, the ratio between the normal breath sounds and the blisters sounds can be intuitively recognized.

The method of displaying the analysis result is not limited to the form of the bar graph shown here, and can be displayed by using various drawing methods. For example, the ratio of the decomposed normal breath sounds to the blisters sounds may be displayed as a bar graph, a pie chart, or a radar chart, or may be displayed numerically. Further, the sound separated by a speaker or the like may be output.

In FIG. 14, the signal strength of the output of the blister sound component fluctuates greatly in a short time. Therefore, if the output value is displayed as it is, the visibility may be poor and it may be difficult to grasp the magnitude of the intensity. Therefore, regarding the output of the blister sound component, the average value or the median value of the signal strength at a predetermined time interval (a specific example of the "second time interval") may be output. By doing so, for example, the average value is calculated for each of the sections 1 to 4 in the figure, and the value of the average value in each section is output as the analysis result. As a result, it is possible to suppress the fluctuation range of the output indicating the blister sound component, and it is possible to provide data with good visibility.

As described above, according to the respiratory sound analyzer according to the present embodiment, it is possible to analyze the respiratory sound spectrum and output information on normal breath sounds and information on blisters separately. ..

The present invention is not limited to the above-described embodiment, and can be appropriately modified within the scope of claims and within the scope not contrary to the gist or idea of the invention that can be read from the entire specification, and respiratory sound analysis accompanied by such modification. Devices and breath sound analysis methods, as well as computer programs and recording media, are also within the technical scope of the present invention.

110 Biological sound acquisition unit 120 Frequency analysis unit 130 Coupling coefficient calculation unit 140 Filter creation unit 150 Signal strength calculation unit 160 Analysis result output unit 200 Display area y spectrum h (f) Basis u coupling coefficient

Claims (8)

A calculation unit that calculates a value indicating the ratio of sound types contained in the breath sounds based on the spectrum of the breath sounds and a predetermined basis, and a calculation unit.
A detection means that detects information about the minimum value of the above value, and
It is provided with an output means for separately outputting information on normal breath sounds and blisters sounds included in the breath sounds based on the information on the minimum value.
The output means calculates the envelope of the minimum value from a plurality of information on the minimum value, outputs the information on the blistering sound based on the portion corresponding to the region above the envelope, and outputs the information on the blisters sound. A respiratory sound analyzer characterized by outputting information on the normal respiratory sound based on a portion corresponding to an area below the line. The respiratory sound analysis device according to claim 1, wherein the output means outputs a ratio of the normal breath sound and the blistering sound included in the breath sound. The respiratory sound analysis device according to claim 1 or 2, wherein the detection means detects information on the minimum value of the intensity of the spectrum of the respiratory sound at each first time interval as information on the minimum value. The respiratory sound analyzer according to claim 3, further comprising a changing means for changing the first time interval. The respiratory sound analysis device according to any one of claims 1 to 4, wherein the output means outputs information on the blister sound as an average value or a median value at every second time interval. A calculation step of calculating a value indicating the ratio of sound types contained in the breath sounds based on the spectrum of the breath sounds and a predetermined basis, and a calculation step.
A detection step that detects information about the minimum value of the above value, and
It is provided with an output step of separately outputting information on normal breath sounds and blisters sounds included in the breath sounds based on the information on the minimum value.
In the output means, the envelope of the minimum value is calculated from the information on the plurality of minimum values, and the information on the blisters sound is output based on the portion corresponding to the region above the envelope, and the envelope is output. A respiratory sound analysis method characterized by outputting information on the normal respiratory sound based on a portion corresponding to an area below the line. A calculation step of calculating a value indicating the ratio of sound types contained in the breath sounds based on the spectrum of the breath sounds and a predetermined basis, and a calculation step.
A detection step that detects information about the minimum value of the above value, and
It is provided with an output step of separately outputting information on normal breath sounds and blisters sounds included in the breath sounds based on the information on the minimum value.
In the output means, the envelope of the minimum value is calculated from the information on the plurality of minimum values, and the information on the water bubble sound is output based on the portion corresponding to the region above the envelope, and the envelope is output. A computer program comprising causing a computer to execute a breath sounds analysis method that outputs information on the normal breath sounds based on a portion corresponding to an area below the line. A recording medium on which the computer program according to claim 7 is recorded.

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