JP2002153434A - Time sequential signal analyzer - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a time sequential signal analyzer capable of performing analysis only from cardiac sound signals. SOLUTION: By setting unit sections to time sequential signals such as the cardiac sound signals and obtaining the correlation of the time sequential signals and a plurality of periodic functions for each set prescribed unit section, a plurality of frequency elements are selected for each unit section. For each unit section, one integrated frequency element is calculated on the basis of the selected plurality of the frequency elements. On the basis of the array pattern of an integrated frequency element column for which the calculated integrated frequency elements are time sequentially arranged, block data for which the successive plurality of the integrated frequency elements are blocked are prepared. Attribute information is imparted to the prepared respective block data on the basis of a prescribed rule and the block data to which the attribute information is imparted are displayed.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a technique for analyzing a time series signal including a biological signal such as a heart sound and an electrocardiogram, and other acoustic signals.

[0002]

2. Description of the Related Art Time-series signals represented by acoustic signals include:
The components include a plurality of periodic signals. For this reason, a method of analyzing what periodic signal is included in a given time-series signal has been known for a long time. For example, Fourier analysis is widely used as a method for analyzing frequency components included in a given time-series signal.

[0003] If such a time-series signal analysis method is used, it is possible to encode an audio signal. With the spread of computers, it has become easier to sample analog audio signals as original sounds at a predetermined sampling frequency, quantize the signal strength at each sampling, and take in as digital data. If a method such as Fourier analysis is applied to the data and frequency components included in the original sound signal are extracted, the original sound signal can be encoded by a code indicating each frequency component.

On the other hand, MIDI (Musical Instrume) was born from the idea of encoding musical instrument sounds by electronic musical instruments.
The Digital Interface (nt Digital Interface) standard has also been actively used with the spread of personal computers. Code data according to the MIDI standard (hereinafter, MID)
I data) is basically data describing an operation of playing a musical instrument, such as which keyboard key of the musical instrument was played and at what strength, and the MIDI data itself contains the actual sound. No waveform is included. Therefore, when reproducing the actual sound, the MI which stores the waveform of the musical instrument sound is used.
Although a DI sound source is required separately, its high coding efficiency has been attracting attention, and the encoding and decoding technology according to the MIDI standard currently uses a personal computer to play musical instruments, practice musical instruments, compose music, and the like. Is widely adopted in software that performs

Therefore, by analyzing a time-series signal represented by an acoustic signal by a predetermined method, a periodic signal as a component of the signal is extracted, and the extracted periodic signal is converted to an M signal.
There have been proposals to encode using IDI data. For example, JP-A-10-247099, JP-A-11-73199, JP-A-11-73200
JP, JP-A-11-95753, JP-A-2000
-99009, JP-A-2000-99092, JP-A-2000-99093, Japanese Patent Application No. 11-1990.
Japanese Patent Application No. 58431, Japanese Patent Application No. 11-177875, and Japanese Patent Application No. 11-329297 analyze the frequency as a component of an arbitrary time-series signal, and convert MIDI data from the analysis result. Various methods have been proposed that can be created.

[0006] In particular, in Japanese Patent Application Laid-Open No. Hei 10-247099, medical rhythm sounds such as heart sounds or lung sounds are captured as time-series sound signals, and then encoded to visually display the characteristics thereof. It describes that the diagnosis is supported. However, JP-A-10-247
In Japanese Patent Publication No. 099, only heart sounds are encoded and displayed, and their symptoms are not analyzed.

In the first place, the heart sound has a wider frequency bandwidth than the electrocardiogram waveform, and its pathological condition is complicated, so that automatic analysis is difficult. Basic sound I and II sound components are always present in heart sounds regardless of healthy people and abnormal people, and when a doctor makes a diagnosis,
Other extra heart sound components and heart murmur components are determined based on this heart sound component. However, an illness is often accompanied by arrhythmia, and when a heart murmur is added, it is difficult even for a skilled doctor to detect basic I and II sound components. In recent years, attempts have been made to automatically analyze such heart sounds.
Japanese Patent Application Laid-open No. 4 (2004) discloses a method of simultaneously taking an electrocardiogram signal as a synchronization signal, correctly acquiring the I and II sound components of the heart sound, and then determining a pathological state using a neural network.

[0008]

However, in the above-mentioned conventional heart sound analysis method, 1) an electrocardiogram waveform sensor system is required separately from a sensor system for acquiring heart sounds, and signals of heart sounds alone cannot be analyzed. Since the disease state determination method is performed using a neural network based on the amplitude change of the signal, the learning state of the neural network (it is necessary for a doctor to input a lot of data in advance and train the device so that the determination result is correct) 3) Since only the amplitude information of the signal is used as a criterion for determination and the frequency distribution is not taken into account, it is difficult in principle to identify heart murmurs having different timbres. 4) The judgment result is only qualitatively showing the pathological signal in words, for example, even if it says `` III sound abnormal, '' there is no information on how strong the III sound is,
And the like.

In view of the above, it is an object of the present invention to provide a time-series signal analyzing apparatus capable of analyzing only a heart sound signal.

[0010]

In order to solve the above-mentioned problems, according to the present invention, a time-series signal and a plurality of cycles are provided for each predetermined unit section set for a given time-series signal. By calculating a correlation with the function, a plurality of frequency elements are selected for each unit section, and one unified frequency element is calculated for each unit section based on the plurality of selected frequency elements. Based on the array pattern of the unified frequency element sequence in which the unified frequency elements are arranged in time series, block data is created by blocking a plurality of continuous unified frequency elements, and each block created based on a predetermined rule. Attribute information is added to the data, and block data to which the attribute information is added is output. According to the first aspect of the invention, in particular, a plurality of frequency elements obtained by frequency analysis are combined into one unified frequency element, and attribute information such as diagnostic information is added based on the array pattern of the unified frequency element sequence. Since the block data is output together with the attribute information, it is possible to easily confirm what state the given time-series signal means.

According to the second aspect of the present invention, the correlation between the time series signal and a plurality of periodic functions is obtained for each predetermined unit section set for the given time series signal,
A plurality of frequency elements are selected for each unit section, one unified frequency element is calculated for each unit section based on the plurality of selected frequency elements, and the calculated unified frequency elements are arranged in time series. Based on the array pattern of the unified frequency element sequence, block data is created by blocking a plurality of continuous unified frequency elements, and attribute information to be assigned to each created block data based on a predetermined rule Is determined, the determined attribute information is assigned to the unified frequency element corresponding to each block data, and the unified frequency element to which the attribute information is assigned is output as text information having a hierarchical structure. According to the second aspect of the invention, in particular, a plurality of frequency elements obtained by frequency analysis are combined into one unified frequency element, and attribute information such as diagnostic information is added based on the array pattern of the unified frequency element sequence. Since the unified frequency element is structured and documented together with the attribute information, it becomes easy to distribute the result of diagnosing what state the given time-series signal means.

[0012]

Embodiments of the present invention will be described below in detail with reference to the drawings.

(Basic Principle of Analysis of Time Series Signal) First, the basic principle of analysis of a time series signal according to the present invention will be described. Since this basic principle is disclosed in the above-mentioned publications or in the specification, only an outline thereof will be briefly described here.

As shown in FIG. 1A, it is assumed that an analog audio signal is given as a time-series signal. In the example of FIG. 1, the horizontal axis represents time t, and the vertical axis represents amplitude (intensity), and this acoustic signal is shown. Here, first, a process of capturing the analog audio signal as digital audio data is performed. This uses the conventional general PCM method,
The analog audio signal may be sampled at a predetermined sampling frequency and the amplitude may be converted into digital data using a predetermined number of quantization bits. here,
For convenience of explanation, the waveform of the audio data digitized by the PCM method is also shown by the same waveform as the analog audio signal in FIG.

Subsequently, a plurality of unit sections are set on the time axis of the audio signal to be analyzed. In the example shown in FIG. 1A, six times t1 to t6 are equally spaced on the time axis t.
Are defined, and five unit sections d1 to d5 having these times as a start point and an end point are set. In the example of FIG. 1, unit sections having the same section length are all set, but the section length may be changed for each unit section. Alternatively, a section may be set such that adjacent unit sections partially overlap on the time axis.

When the unit sections are set in this way, a representative frequency is selected for each of the sound signals (hereinafter referred to as section signals) for each unit section. Each section signal usually contains various frequency components,
For example, a frequency component having a large intensity ratio of the component may be selected as the representative frequency. Here, the representative frequency is generally a so-called fundamental frequency, but a harmonic frequency such as a formant frequency of a voice and a peak frequency of a noise sound source may be treated as the representative frequency. Although only one representative frequency may be selected, depending on the acoustic signal, selecting a plurality of representative frequencies enables more accurate encoding. FIG. 1B shows an example in which three representative frequencies are selected for each unit section, and one representative frequency is encoded as one representative code (for convenience, shown as a musical note in the figure). Have been. Here, three tracks T1, T1,
T2 and T3 are provided in order to accommodate three representative codes selected for each unit section in different tracks.

For example, the representative codes n (d1,1), n (d1,2), n (d1,
3) are accommodated in the tracks T1, T2, T3, respectively. Here, each code n (d1, 1), n (d1,
2), n (d1, 3) are codes indicating note numbers in the MIDI code. The note number in the MIDI code takes 128 values from 0 to 127 and indicates one key of the piano keyboard. Specifically, for example, 440 Hz as a representative frequency
Is selected, this frequency has a note number n = 6
9 (corresponding to the "ra tone (A3 tone)" at the center of the piano keyboard), so that n = 69 is selected as the representative code. However, FIG. 1B is a conceptual diagram showing a representative code obtained by the above-described method in the form of a musical note, and in practice, data relating to the intensity is added to each musical note. For example, in the track T1, e (d1, 1) and e (d) are added together with data indicating pitches of note numbers n (d1, 1), n (d2, 1).
2, 1)... Are stored. The data indicating the strength is determined based on how much the component of each representative frequency is included in the original section signal. Specifically, data indicating the intensity is determined based on the correlation value of the periodic function having each representative frequency with respect to the section signal. FIG. 1 (b)
In the conceptual diagram shown in Fig. 7, the position of each unit section on the time axis is indicated by the position of the note in the horizontal direction, but in actuality, data that accurately indicates the position on the time axis as a numerical value Is added to each note.

It is not always necessary to adopt the MIDI format as a format for encoding an audio signal. However, since the MIDI format is the most widespread as this type of encoding format, the MIDI format code data is practically used. It is preferable to use In the MIDI format, "note on" data or "note off" data exists with "delta time" data interposed. Note-on data is
The "note-off" data is data specifying a specific note number N and velocity V to designate the start of performance of a specific sound, and "note-off" data is data specifying a specific note number N and velocity V. This is data to instruct the end of the performance. The “delta time” data is data indicating a predetermined time interval. Velocity V is a parameter indicating, for example, the speed at which a piano keyboard or the like is depressed (velocity at the time of note-on) and the speed at which the finger is released from the keyboard (velocity at the time of note-off). Or it indicates the strength of the performance end operation.

In the above method, the i-th unit section di
, J note numbers n (d
i, 1), n (di, 2),..., n (di, J) are obtained, and the intensities e (di, 1), e
(Di, 2),..., E (di, J) are obtained. Therefore, MIDI-format code data can be created by the following method. First, as the note number N described in the “note-on” data or “note-off” data, the obtained note number n (d
i, 1), n (di, 2),..., n (di, J) may be used as they are. On the other hand, as the velocity V described in the “note-on” data or the “note-off” data, the obtained intensities e (di, 1) and e (d
i, 2),..., and e (di, J) may be standardized by a predetermined method. The “delta time” data may be set according to the length of each unit section.

(Specific Method for Determining Correlation with Periodic Function) In the method based on the basic principle described above, one or a plurality of representative frequencies are selected for the section signal, and the periodic signal having this representative frequency is selected. Thus, the section signal is expressed. Here, the selected representative frequency is, literally, a frequency representative of a signal component in the unit section. Specific methods for selecting the representative frequency include a method using a short-time Fourier transform and a method using a generalized harmonic analysis method, as described later. Both methods have the same basic concept. A plurality of periodic functions having different frequencies are prepared in advance, and a periodic function having a high correlation with the section signal in the unit section is selected from the plurality of periodic functions. , And the frequency of the periodic function having a high correlation is selected as a representative frequency. That is, when selecting a representative frequency, an operation for calculating a correlation between a plurality of periodic functions prepared in advance and a section signal in a unit section is performed. Therefore, here, a specific method for obtaining the correlation with the periodic function will be described.

It is assumed that a trigonometric function as shown in FIG. 2 is prepared as a plurality of periodic functions. These trigonometric functions are composed of a pair of a sine function and a cosine function having the same frequency, and have 128 standard frequencies f (0) to
For each of f (127), a pair of a sine function and a cosine function is defined. Here, a pair of functions consisting of a sine function and a cosine function having the same frequency is defined as a periodic function for the frequency. That is, the periodic function for a specific frequency is constituted by a pair of a sine function and a cosine function. The reason why the periodic function is defined by the pair of the sine function and the cosine function is to consider that the correlation value is affected by the phase when calculating the correlation value of the periodic function for the signal. Variables F and k in each trigonometric function shown in FIG. 2 are variables corresponding to sampling frequency F and sample number k for section signal X. For example, a sine wave for a frequency f (0) is represented by sin (2πf (0) k / F), and given an arbitrary sample number k, the same time position as the k-th sample forming the section signal Is obtained.

Here, 128 standard frequencies f
An example in which (0) to f (127) are defined by equations as shown in FIG. 3 will be shown. That is, the n-th (0 ≦ n ≦ 1
The standard frequency f (n) of 27) is defined by the following equation: f (n) = 440 × ^{2γ (n)} γ (n) = (n−69) / 12 Defining the standard frequency using such an expression is convenient when finally performing encoding using MIDI data. This is because the 128 standard frequencies f (0) to f (127) set by such a definition have frequency values forming a geometric series, and correspond to note numbers used in MIDI data. This is because it becomes a frequency. Therefore, 128 standard frequencies f (0) to f shown in FIG.
(127) is a frequency set at equal intervals (in semitone units in MIDI) on a frequency axis represented by a logarithmic scale. For this reason, in the present application, each of the note number axes in the graphs shown in the figures is shown on a logarithmic scale.

Next, a specific description will be given of a method of obtaining a correlation of each periodic function with respect to an interval signal of an arbitrary interval. For example, as shown in FIG. 4, it is assumed that a section signal X is given for a certain unit section d. Here, it is assumed that sampling is performed at a sampling frequency F for a unit section d having a section length L, and that a total of w sample values have been obtained. 1, 2, 3, ..., k, ..., w-
2, w-1 (the w-th sample shown by a white circle is
The sample is included at the beginning of the next unit section adjacent to the right.) In this case, for any sample number k,
The amplitude value X (k) is given as digital data. In the short-time Fourier transform, X
It is normal to multiply (k) by a window function W (k) such that the weight at the center is close to 1 and the weight at both ends is close to 0 for each sample. That is, X (k) × W
The following correlation calculation is performed by treating (k) as X (k), and a cosine-wave shaped Hamming window is generally used as the shape of the window function. Here, w is described as a constant in the following description. In general, w is changed according to the value of n, and the maximum value of F / f (n) within the range not exceeding the section length L is obtained. It is desirable to set the value to an integral multiple.

The principle of obtaining a correlation value between such a section signal X and a sine function Rn having an n-th standard frequency f (n) will be described. The correlation value A (n) between the two can be defined by the first arithmetic expression in FIG. here,
X (k) is the amplitude value of the sample number k in the section signal X, as shown in FIG. 4, and sin (2πf (n) k
/ F) is the amplitude value of the sine function Rn at the same position on the time axis. This first arithmetic expression can be said to be an expression for calculating the inner product of the amplitude value of the section signal X and the amplitude vector of the sine function Rn for the dimensions of all sample numbers k = 0 to w−1 in the unit section d. .

Similarly, the second operation expression of FIG. 5 is an expression for obtaining a correlation value between the section signal X and a cosine function having the n-th standard frequency f (n). Is B (n)
Given by It should be noted that both the first equation for obtaining the correlation value A (n) and the second equation for obtaining the correlation value B (n) are finally multiplied by 2 / w. Is for normalizing the correlation value, and w is n
This coefficient is also a variable that depends on n, since it is generally changed depending on.

The effective value of the correlation between the section signal X and the standard periodic function having the standard frequency f (n) is, as shown in the third equation of FIG. 5, the value of the correlation A (n) with the sine function. It can be indicated by the root sum square (E (n)) of the correlation value B (n) with the cosine function. If a frequency of the standard periodic function having a large correlation effective value is selected as a representative frequency, the section signal X can be encoded using the representative frequency.

That is, one or more standard frequencies at which the correlation value E (n) is equal to or larger than a predetermined reference may be selected as a representative frequency. Here, the selection condition that “the correlation value E (n) is equal to or larger than a predetermined reference” is set, for example, by setting a certain threshold value and setting a standard value such that the correlation value E (n) exceeds this threshold value. Frequency f (n)
May be set as the representative frequency, but relative selection conditions such as selecting up to the Qth in the order of the magnitude of the correlation value E (n) may be set. May be set.

(Method of Generalized Harmonic Analysis) Here, a method of generalized harmonic analysis useful for analyzing a time-series signal according to the present invention will be described. As described above, when encoding an audio signal, some representative frequencies having high correlation values are selected for section signals in each unit section. Generalized harmonic analysis is a technique that enables selection of a representative frequency with higher accuracy, and its basic principle is as follows.

It is assumed that a signal S (j) exists in a unit section d as shown in FIG. here,
j is a parameter for the repetition processing (j = 1 to J) as described later. First, correlation values are obtained for this signal S (j) for all 128 periodic functions as shown in FIG. Then, the frequency of one periodic function at which the maximum correlation value is obtained is selected as a representative frequency,
A periodic function having the representative frequency is extracted as an element function. Subsequently, the content signal G as shown in FIG.
(J) is defined. The content signal G (j) is added to the extracted element function as the amplitude of the signal S of the element function.
This is a signal obtained by multiplying the correlation value for (j). For example, as shown in FIG. 2, when a pair of sine function and cosine function is used as a periodic function and a frequency f (n) is selected as a representative frequency, a sine function A (n) having an amplitude A (n) is used. ) Sin (2πf (n) k / F) and cosine function B (n) cos (2πf) having amplitude B (n)
(N) k / F) is the content signal G (j)
(In FIG. 6B, only one function is shown for convenience of illustration). Where A (n), B
Since (n) is a normalized correlation value obtained by the equation of FIG. 5, the contained signal G (j) has the frequency f (n) contained in the signal S (j). Signal component.

When the content signal G (j) is obtained in this way, the difference signal S (j + 1) is obtained by subtracting the content signal G (j) from the signal S (j). FIG. 6 (c)
The difference signal S (j + 1) thus obtained is shown. This difference signal S (j + 1) is equal to the original signal S
From (j), it can be said that it is a signal composed of the remaining signal components obtained by removing the signal components having the frequency f (n). Therefore, by increasing the parameter j by 1, the difference signal S (j + 1) is treated as a new signal S (j), and the same processing is performed by setting the parameter j to j = 1 to j = 1.
If J is repeated J times while increasing by 1 to J, J
Representative frequencies can be selected.

The J contained signals G (1) to G (J) output as a result of the correlation calculation are signals that are components of the original section signal X, When encoding X, information indicating the frequency and amplitude (intensity) of these J contained signals may be used as code data. Although J has been described as being the number of representative frequencies, it may be the same as the number of standard frequencies f (n), that is, J = 128, and this is usually performed for the purpose of obtaining a frequency spectrum. It is.

(Time Series Signal Analysis Method According to the Present Invention) Next, a time series signal analysis method according to the present invention utilizing the above basic principle will be described. FIG. 7 is a flowchart showing an outline of analysis of a time-series signal according to the present invention. As shown in FIG. 7, first, frequency analysis of a time-series signal is performed (step S1). Here, an example of a heart sound waveform captured as a time-series signal is shown in FIG. In step S1, first,
For the heart sound waveform (signal) as shown in FIG. 8, a unit section is set as described with reference to FIG. Subsequently, a periodic function as shown in FIG. 2 is prepared, a correlation with a section signal is calculated for each unit section, and a frequency element is selected. Here, the frequency element means a set of a selected representative frequency and a correlation strength corresponding to the representative frequency. Either the short-time Fourier transform or the generalized harmonic analysis method can be applied to the selection of the frequency element as described above, but it is preferable to use the generalized harmonic analysis method. As described above, the number of representative frequencies to be selected can be determined by setting, but is preferably set to about 16 frequencies. FIG. 9 shows the frequency elements selected as a result of the frequency analysis in step S1.

In FIG. 9, the frequency is indicated by the pitch of the sound and the correlation strength is indicated by the intensity of the sound, and the frequency elements are indicated by downward triangles. The pitch of the sound is represented by the position in the vertical direction of the upper side of the triangle, and the strength of the sound is represented by the height of the triangle. The length of the unit section is represented by the length of the upper side of the triangle. The number of frequency elements selected in the frequency analysis in step S1 is preferably about 16 for each unit section. However, in order to avoid complicating the drawing, three frequency elements are shown for each unit section in the example of FIG. I have.

When a frequency element is selected for each unit section, a unified frequency element is calculated for each unit section (step S2). The unified frequency Nr (t) and the unified strength Vr (t) constituting the unified frequency element are calculated by the following (Equation 1).

(Equation 1) Nr (t) = [{n × Vn (t)}] / {Vn (t) Vr (t) = [Σ ｛Vn (t)｝ ² ] ^1/2

In the above (Equation 1), n is a note number, and Vn (t) is a velocity corresponding to the note number n. The sum calculated by Σ is calculated for the number of frequency elements. That is, when 16 frequency elements are selected, the sum of 16 frequency elements is calculated. According to (Equation 1), as the unified frequency Nr (t), the frequency at the position of the center of gravity when each frequency element is plotted on a graph composed of the frequency axis and the intensity axis is calculated, and the unified intensity Vr (t) , The mean square value of the intensity distribution is calculated. FIG. 10 shows a unified frequency element calculated by the processing of step S2 for the frequency element shown in FIG.

Next, a unified frequency element which is unlikely to contain the I and II sound components of the heart sound is deleted (step S).
3). The I and II components of the heart sound exist in a relatively low frequency band. Therefore, components higher than a predetermined threshold are deleted. This threshold value needs to be adjusted because it changes depending on the frequency characteristics of a heart sound sensor system for taking in a heart sound as an acoustic signal, but usually a note number around 50 is set. Further, in order to remove noise, a component having an intensity smaller than a predetermined threshold is deleted. This threshold value is set to about 64 near the center of the velocity. As a result, those having high frequencies are deleted from the unified frequency elements shown in FIG. 10, and only the unified frequency elements as shown in FIG. 11 remain.

Next, block data in which the remaining unified frequency elements are integrated is created (step S4). Specifically, the unified frequency of the unified frequency elements adjacent in the time series direction and the average value of the correlation strength are set as the block frequency and the block strength of the block obtained by the integration. FIG. 12 shows a state in which the unified frequency elements shown in FIG. 11 are blocked. FIG.
The frequency elements blocked in are shown by rectangles. The position in the vertical direction of the upper side of the rectangle indicates the pitch of the sound, and the height of the rectangle indicates the intensity of the sound. Further, the width of the rectangle differs for each block because the number of unit sections to be integrated is different.

Subsequently, attribute information is assigned to each block (step S5). Specifically, I sound, II sound,
The other three types of attribute information are given. This determination is made based on the following four rules.

Pitch: II sound has a higher pitch than I sound. (II sound> I sound) Sound length: The sound length of both I sound and II sound is fixed short, and II sound is shorter than I sound. (I sound and II sound <Length threshold and I sound> II sound) Sound intensity: Both I sound and II sound are stronger than other components.
The balance between the I sound and the II sound changes depending on the position of the sensor to be heard. Exceptions are I sound and II
May be stronger than sound. (I sound and II sound> intensity threshold) Sound interval: The interval between I sound and II sound is shorter than the interval between II sound and I sound. When the heart rate increases, the interval between the II sound and the I sound decreases, but the interval between the I sound and the II sound does not change, that is, approaches the interval between the I sound and the II sound. (I-II interval <II-I interval)

As a result, each block shown in FIG. 12 is given attribute information as shown in FIG. Subsequently, corresponding attribute information is assigned to the unified frequency element (step S6). Here, FIG. 14 shows a state where the block data and the corresponding unified frequency element are overlapped. Based on the correspondence shown in FIG. 14, in the process of step S6, first, the same attribute information is assigned to the unified frequency element corresponding to the block data to which the attribute information of the I sound and the II sound has been added. Subsequently, for the unified frequency element corresponding to the block data to which other attribute information is added, referring to the database in which the detailed attribute information is registered from the array pattern of the unified frequency element sequence, II
I-sound, IV-sound (or superposition of III and IV), heart murmur (sub-categories such as systolic or diastolic or continuous, regurgitant or driven), clicks, fricatives, etc. Is determined, and the corresponding detailed attribute information is added. In the example of FIG. 14, detailed attribute information indicating that the sound is a composite sound of the sound III and the sound IV is added from the arrangement pattern of the unified frequency element sequence. Further, in the example of FIG. 14, the detailed attribute information of the click sound is added to the unified frequency element deleted because the pitch is higher than the predetermined threshold in the process of step S3. At this stage, it is also possible to display the block data shown in FIG. 13 or the unified frequency element as shown in FIG. 14 on an output means such as a display device. By displaying the heart sound together with the attribute information of the heart sound in this manner, it is possible to confirm at a glance the state of the heart sound of the patient.

In the present embodiment, the unified frequency element to which the attribute information is added is further documented in the structure (step S
7). XML (eXtensible Markup La) as a structured document
FIG. 15 shows an example of a source code when the nguage) standard is adopted. In the example shown in FIG. 15, one cycle of a heart sound from the beginning of the unified frequency element shown in FIG. 14 is structured and described in 21 lines. The unified code data described in FIG. 15 is described in conformity with MIDI. XML has a format in which actual data is basically surrounded by a pair of tags. In FIG. 15, the start and end of the document are defined by a pair of tags on the first line and the last line on the 21st line. In the pair of tags described in the second line, the sound generation start time (<StartTime) defined in the MIDI standard-compliant event data (defined by the <Event> tag) described from the beginning to the end of this document
The time unit (resolution per second) of the sound generation end time (defined by the <EndTime> tag) is defined. The <HeartCycle> tag on the third line indicates that data for one cycle of heart sound is described below, and the closing tag </ HeartCycle> is omitted in FIG. I have. The fourth to seventh lines describe the heart sound I. The fourth and seventh lines are a pair of tags indicating the heart sound I. The fifth and sixth lines are unified. Code data is described in accordance with the MIDI standard. For example, <StartTime>, <En
The dTime> describes a time represented by a relative time called a delta time in the MIDI standard as an absolute time, and indicates that sound is generated from a time “10” to a time “20”. <Pitch> on the fifth line corresponds to the MIDI standard note number, and indicates that the note is to be generated at a pitch corresponding to the note number “30”. 5th line <L
evel> corresponds to the velocity of the MIDI standard, and indicates that the sound is to be generated at a sound intensity corresponding to the velocity “60”.

When the created structured document is in the XML format, it can be viewed with an Internet browser, and the heart sound can be reproduced as an acoustic signal using dedicated player software and a MIDI sound source. Specifically, the created structured document is registered in a WWW server, and a user starts a browser on his / her personal computer, accesses the WWW server on the Internet, and obtains an XML document. Next, the MID plugged into the browser
If the I sequencer software is running on the M
The sound signal is reproduced while controlling the MIDI sound source according to the IDI data. By encoding the heart sound in this way, X
Recording in the ML format is not only convenient for distribution via the Internet or suitable for reproduction using MIDI sound sources, but also recently, digitization as an electronic medical record using the XML format has been performed. It is also effective from the viewpoint of consistency with existing medical record information.

When the MIDI data to which the attribute of the heart sound is added is stored as an electronic medical record, in addition to the above-mentioned effect that the heart sound can be reproduced by the MIDI sound source, a quantitative pathological condition necessary for diagnosis is read from a document as described below. There is an effect that can be. For example, the I and II sounds have pathological states of enhancement, attenuation, and division, and these pathological states can be determined from the numerical values of MIDI event data surrounded by attributes. That is, if the velocity (strength) enclosed by the <Level> tag or the note number (height) enclosed by the <Pitch> tag is higher than a predetermined value, the level is increased, and if the note number is lower than the predetermined value, the level is decreased. Then, in the example of the sound I and the sound II in FIG. 15, the event is composed of two events (notes represented by triangles) that are close on the time axis. That is, <E of the previous event
If the difference between the value of the <ndTime> tag and the value of the <StartTime> tag of the subsequent event is equal to or greater than a predetermined value, it can be determined that the event is split. In clinical diagnosis of heart murmur, Levine is often classified (classes 1 to 6; 1 is a weak noise that is difficult to hear by auscultation and cannot be identified unless it is a heart sound chart, and 6 is a case where a stethoscope is not applied. The strong noise that can be heard just by being on the side is used, but this can also be determined from the velocity (strength) enclosed by the <Level> tag. (Although it is possible to automatically classify into 6 classes based on the velocity value, it is difficult to fully automate because the velocity varies depending on the audio input conditions.)

Next, an apparatus for realizing the above-described method for analyzing a time-series signal according to the present invention will be described. FIG.
1 is a functional block diagram illustrating an embodiment of a time-series signal analysis device. 16, reference numeral 1 denotes a time-series signal acquisition unit, 2 denotes a frequency analysis unit, 3 denotes a unified frequency element calculation unit, 4 denotes a block data creation unit, 5 denotes an attribute information adding unit, 6 denotes a detailed attribute database, and 7 denotes an output unit. , 8 are structured document creation means. The time-series signal analysis apparatus shown in FIG. 16 is actually realized by connecting audio-related peripheral devices to a computer and installing a dedicated program.

The time-series signal acquiring means 1 is for acquiring a time-series signal. When acquiring a heart sound signal, a microphone is attached to a stethoscope and the acquired heart sound is converted to a PCM signal.
This is realized by a sampling device that converts the signal into a digital signal. The frequency analysis means 2 has a function of performing frequency analysis of the acquired time-series signal and selecting a frequency element. Specifically, the frequency analysis means 2 executes the processing of step S1 in FIG. 7 according to a computer program. The unified frequency element calculating means 3 has a function of calculating a unified frequency element in which a plurality of frequency elements for each unit section obtained by the frequency analyzing means 2 are unified.
Step 2 is executed according to a computer program. The block data creation means 4 has a function of creating block data in which a plurality of unified frequency elements are blocked, and specifically executes the processes of steps S3 and S4 in FIG. 7 according to a computer program. The attribute information assigning means 5 assigns attribute information to the block data in accordance with a preset rule, and assigns attribute information to the unified frequency element from which the block data is created, and provides a detailed attribute database. 6 has a function of further adding the detailed attribute information extracted from FIG. That is, the attribute information providing unit 5 performs step S5 in FIG.
The processing of S6 is executed according to a computer program. The detailed attribute database 6 is a database that records correspondence between detailed attribute information to be assigned to the unified frequency element and an array pattern of the unified frequency element,
In the present embodiment, the third and fourth heart sounds (or III and
Overlapping sounds of IV), cardiac murmurs (subclasses such as systolic or diastolic or continuity, regurgitant or driven) are also defined, and clicks and fricatives are recorded. The output means 7 displays the created block data together with the corresponding attribute information, prints it, or synthesizes and reproduces the sound included in the block data.
By changing the style sheet described in XSL from the features of the XML standard, it can be expressed in various formats such as a graph format and a table format. Specifically, the output unit 7 is realized by a display device such as a CRT or a liquid crystal, a printer device of various printing methods of monochrome or color, a sound source device of the MIDI standard, an audio output amplifier, and a speaker device.
The structured document creating means 8 has a function of converting the unified frequency element to which the detailed attribute information has been added into text information such as the XML standard. Specifically, the structured document creating means 8 performs the processing of step S7 in FIG. Execute according to.

Although the preferred embodiment of the present invention has been described above, the present invention is not limited to the above embodiment, and various modifications are possible. In the above embodiment, the case where heart sound is analyzed as a time-series signal has been described. It is also possible to analyze an acoustic signal such as sound as a time-series signal and apply it to failure diagnosis of an automobile engine.

[0048]

As described above, according to the present invention,
By calculating the correlation between the time series signal and a plurality of periodic functions for each predetermined unit section set for the given time series signal, a plurality of frequency elements are selected for each unit section, and each unit is selected. For each section, one unified frequency element is calculated based on a plurality of selected frequency elements, and a plurality of continuous unified frequency elements are arranged based on an array pattern of a unified frequency element sequence in which the calculated unified frequency elements are arranged in time series. Since block data in which a unified frequency element is blocked is created, attribute information is added to each created block data based on a predetermined rule, and the block data with the attribute information is output. , You can easily see what state the given time-series signal means,
At the same time, it is possible to obtain quantitative diagnostic basic information by referring to the unified frequency element sequence included in the block data, and capturing heart sounds as a time-series signal will help to accurately determine the patient's condition. It works.

[Brief description of the drawings]

FIG. 1 is a diagram showing a basic principle of signal analysis in a time-series signal analyzer of the present invention.

FIG. 2 is a diagram showing an example of a periodic function used in the present invention.

FIG. 3 is a diagram showing a relational expression between a frequency of each periodic function shown in FIG. 2 and a MIDI note number n.

FIG. 4 is a diagram showing a method of calculating a correlation between a signal to be analyzed and a periodic signal.

FIG. 5 is a view showing a calculation formula for performing the correlation calculation shown in FIG. 4;

FIG. 6 is a diagram showing a basic method of generalized harmonic analysis.

FIG. 7 is a flowchart showing an outline of analysis of a time-series signal according to the present invention.

FIG. 8 is a diagram illustrating an example of a heart sound waveform captured as a time-series signal.

9 is a diagram showing frequency elements selected by frequency analysis from the heart sound waveform shown in FIG. 8;

FIG. 10 is a diagram showing a unified frequency element calculated based on the frequency elements shown in FIG. 9;

FIG. 11 is a diagram illustrating a state where unnecessary components are deleted from the unified frequency elements illustrated in FIG. 10;

FIG. 12 is a diagram showing block data created based on the unified frequency elements shown in FIG. 11;

FIG. 13 is a diagram illustrating a state in which attribute information is assigned to each block illustrated in FIG. 12;

FIG. 14 is a diagram showing a state in which block data and corresponding unified frequency elements are superimposed.

FIG. 15 is a diagram showing a source code when the XML standard is adopted as a structured document.

FIG. 16 is a functional block diagram showing an embodiment of a time-series signal analyzer according to the present invention.

[Explanation of symbols]

 DESCRIPTION OF SYMBOLS 1 ... Time-series signal acquisition means 2 ... Frequency analysis means 3 ... Unified frequency element calculation means 4 ... Block data creation means 5 ... Attribute information provision means 6 ... Detailed attribute database 7. ..Output means 8 ... Structured document creation means

──────────────────────────────────────────────────続 き Continued on the front page (51) Int.Cl. ⁷ Identification symbol FI theme coat ゛ (reference) G10L 11/00 G10L 9/04 A

Claims (6)

[Claims] 1. A method of calculating a correlation between a time series signal and a plurality of periodic functions for each predetermined unit section set for a given time series signal, thereby obtaining a plurality of frequency elements for each unit section. Frequency analysis means to be selected; unified frequency element calculation means for calculating one unified frequency element based on the selected plurality of frequency elements for each unit section; unification in which the unified frequency elements are arranged in time series A block data generating means for generating block data obtained by blocking a plurality of continuous unified frequency elements based on an array pattern of a frequency element string; and an attribute for each of the generated block data based on a predetermined rule. A time-series signal comprising: attribute information adding means for adding information; and output means for outputting block data to which the attribute information has been added. Analysis apparatus. 2. A method for calculating a correlation between a time-series signal and a plurality of periodic functions for each predetermined unit section set for a given time-series signal, thereby obtaining a plurality of frequency elements for each unit section. Frequency analysis means to be selected; unified frequency element calculation means for calculating one unified frequency element based on the selected plurality of frequency elements for each unit section; unification in which the unified frequency elements are arranged in time series A block data generating means for generating block data obtained by blocking a plurality of continuous unified frequency elements based on an array pattern of a frequency element sequence; and applying the block data to each of the generated block data based on a predetermined rule. Attribute information assigning means for determining attribute information to be provided, and assigning the determined attribute information to a unified frequency element corresponding to each of the block data; And a structured document creating means for outputting the obtained unified frequency element as text information having a hierarchical structure. 3. The unified frequency element calculating means calculates a center of gravity value of a frequency element distribution as a unified frequency based on frequencies and signal intensities of a plurality of frequency elements selected in each unit section, and 3. The time-series signal analysis device according to claim 1, wherein a root mean square value of the intensity distribution is given as a unified intensity. 4. A detailed attribute database which records a correspondence relationship between an array pattern of a unified frequency element sequence corresponding to one block data and detailed attribute information, wherein said attribute information providing means includes: Attribute information to be added is determined, and detailed attribute information to be added is determined with reference to the detailed attribute database for block data for which basic basic attribute information could not be added. The time-series signal analysis device according to claim 1, wherein 5. The time-series signal is a heart sound signal, the basic attribute information is an I sound component and a II sound component of the heart sound, and the detailed attribute database includes an abnormal heart sound component such as a III sound component and an IV sound component. 5. The time-series signal analysis device according to claim 4, wherein a correspondence with the arrangement pattern with respect to the heart murmur component is made. 6. The text information output by the structured document creation means is described in conformity with the XML standard format.
3. The time according to claim 2, wherein the contained unified frequency element is described in a MIDI format having delta time information corresponding to the section, note number information corresponding to the frequency, and velocity information corresponding to the intensity. Sequence signal analyzer.