JP3468554B2 - Vibration data analysis method and device - Google Patents

Description

Detailed Description of the Invention

[0001]

The present invention relates to a signal analysis method,
In particular, it relates to a method of analyzing brain potential oscillation data obtained in brain electrophysiology.

[0002]

2. Description of the Related Art Generally, when an organism processes information with respect to a stimulus, the vibration pattern of brain potential vibration changes.
Therefore, it is necessary to infer the information processing process of the brain in order to elucidate the information processing process of the organism, and the analysis of the time series data of the brain potential oscillation obtained in the brain electrophysiology becomes extremely important.

The parameters that characterize the vibration pattern of brain potential vibration are the cycle of vibration and each vibration waveform.
Analysis of changes in vibration patterns was performed using vibration data obtained directly from the living body. However, the change in the vibration pattern is often subtle, and the judgment differs depending on the person. Therefore, it is necessary to quantitatively analyze the relationship between the stimulus given to the living thing and the change in the vibration pattern.

Generally, a data analysis method centered on so-called Fourier transform is often used for analysis of various signal data. However, it is not possible in quantitative discussion to apply the conventional data analysis method for steady data such as Fourier transform to non-periodic and non-stationary data such as brain potential oscillation data. Yes, and tended to be biased towards qualitative discussions.

Therefore, as a method capable of handling such non-periodic and non-stationary data, a method based on so-called wavelet transform has been receiving attention in recent years. According to this method, in principle, it is possible to perform conversion that can quantitatively handle the period of vibration and temporal changes in the vibration waveform.

[0006]

However, in the conventional method using the wavelet transform, it is not possible to perform a sufficient conversion to the extent that the change in the brain potential oscillation data can be directly quantitatively analyzed from the conversion result, and the stimulus given to the organism is stimulated. It was difficult to quantitatively analyze the relationship between changes in the vibration pattern and brain vibration data.

The present invention has been made to solve the above problems, and an object of the present invention is to provide a method and an apparatus capable of quantitatively analyzing a vibration pattern of potential vibration data.

[0008]

According to a first aspect of the present invention, there is provided a method for analyzing vibration data , comprising: an A / D conversion step for digitally converting the obtained time series data of potential vibrations ; and a digital conversion. The potential oscillation data, the calculation step of performing data conversion by a predetermined numerical calculation, and the output step of outputting the converted data, the calculation step, at least, (i) the A / D conversion step The step of reading the time series data of the potential oscillation digitally converted in step (ii), the step of reading the basic wavelet data string, (iii) the step of rearranging the read basic wavelet data string according to a predetermined rule, and (iv) A step of calculating the inner product of the read time series data and the rearranged basic wavelet data sequence,
It is characterized by including.

According to a second aspect of the present invention, there is provided an apparatus for analyzing vibration data, which comprises a data acquisition means for obtaining time series data of potential vibration from an object to be measured, and an A / D conversion means for digitally converting the obtained potential vibration data. A digitally converted potential oscillation data, arithmetic means for performing data conversion by predetermined numerical calculation, and output means for outputting the converted data, the arithmetic means is at least (i)
Means for reading the time series data of the potential oscillation digitally converted by the analog / digital converting means, and (i
(i) a means for reading the basic wavelet data sequence, and (ii)
i) A means for rearranging the read basic wavelet data sequence according to a predetermined rule, and (iv) an arithmetic means for calculating an inner product of the read time-series data and the rearranged basic wavelet data sequence. It is what

[0010]

According to the present invention, the basic wavelet data sequence is rearranged according to a predetermined rule, and the inner product of this sequence and the digitally converted time series data of the potential oscillation is calculated and output.

[0011]

The present invention will be described in detail below with reference to the drawings.
In this embodiment, an electroencephalographic vibration analysis apparatus configured to apply the vibration data analysis method of the present invention to slug brain potential oscillation data will be described as an example.

FIG. 1 shows the overall configuration of an electroencephalographic vibration data analysis apparatus according to an embodiment of the present invention.
This device is a potential oscillation measuring device 11 for measuring the oscillation of slug brain potential, and an analog digital (A / A / A) for converting analog brain potential oscillation data obtained from this potential oscillation measuring device 11 into a digital value. D) Converter 12
And an arithmetic unit 13 for performing data conversion by performing a predetermined numerical calculation on the time series data f (ti) of the brain potential output from the A / D converter 12, and the arithmetic unit 13.
For inputting a CRT display 14 that outputs the result output from the computer on a screen, a printer 15 that prints out the result output from the operation unit 13, and operation parameters necessary for numerical calculation in the operation unit 13. Keyboard 16 and a memory 17 in which numerical data of a basic wavelet data string (basic wavelet basis function), which will be described later, necessary for numerical calculation is stored.

FIG. 2 is a layout diagram for measuring brain potential oscillations of slugs, which is the object of measurement in this embodiment, and corresponds to the potential oscillation measuring device 11 and its peripheral components in FIG. . As shown in this figure, a slug cranial ganglion 23 is placed in a container 21 containing a grounded physiological saline solution 22, and a nerve bundle 24 is extended to another room through a hole in a partition wall 28. The olfactory epithelium 25 is placed.
The suction-type measurement electrode 26 inserted into the cranial ganglion 23 is
It is connected to the preamplifier 31 together with the reference electrode 27. In the cerebral ganglion 23, a weak constant brain potential oscillation occurs,
When an odor stimulus is given to the olfactory epithelium 25, this potential oscillation changes. These are input to the preamplifier 31 and amplified. The output of the preamplifier 31 is digitally converted by the A / D converter 12 via the PCM recorder 32 and input to the arithmetic unit 13 of FIG.

The operation of the brain potential vibration data analysis apparatus having the above configuration will be described. First, the flow of the entire data conversion process will be described with reference to FIG.

In FIG. 3, when slug brain potential oscillation time series data is input from the potential oscillation measuring device 11 (step S101), the A / D converter 12 converts this analog data into a digital value (step S102). ), And output to the calculation unit 13.

The computing unit 13 rearranges the basic wavelet data sequence stored in the memory 17 according to a predetermined rule, and then calculates the inner product of this sequence and the digitally converted brain potential oscillation time series data. The potential oscillation time series data is converted (here, this conversion is referred to as pseudo wavelet conversion) (step S10).
3), and outputs the result (step S104). The output data is output and displayed by the CRT display 14 or the printer 15 (step S105).
Then, from this conversion result, the parameter of the change in the vibration pattern of the brain potential vibration can be quantitatively extracted (step S106).

Next, the contents of the numerical calculation in the arithmetic unit 13 will be described in more detail with reference to FIG.

In FIG. 4, first, the A / D converter 1
2, the brain potential oscillation time series data f (j) is read (step S201), and the basic wavelet data string g (i) is read from the memory 17 (step S20).
2). Here, f (j) is, for example, digitally converted time-versus-potential data as shown in FIG. 5, and j is an integer indicating a sampling point. Where DTT =
If 1 / (sampling frequency), then j = [t / DT
T]. Therefore, for example, the sampling frequency is 1K
If Hz, the time 10 seconds of the data corresponds to j = 10000.

Next, the basic wavelet data string g
(I) is rearranged according to the following rule (step S
203).

Before rearrangement After rearrangement gI (k)-> t (2k-1) 5.0-t (251 + k)-> t (2k) gI (250 + k)-> gR (2k-1) t (251 + k)-> gR (2k) 251 + k-> gI (2k-1) gR (2k)-> gI (2k) where gR (k) and gI (k) are the wavelet transforms, respectively. The real part and the imaginary part of the basic wavelet data sequence g (i), which is a basis function, are shown, and for example, a localized function having a shape as shown in FIG. 6 can be used. Where k =
It is an integer. In the inner product calculation described later, it is necessary to match the data point i of the basic wavelet data string g (i) to the scale of time t, and this is performed using the following equation (1).

T (i) = (i / 500) −0.5 (1) Next, the wavelet data sequence g (i) rearranged according to the above rule and the brain potential oscillation time series data f
The inner product with (j) is calculated for i according to the following equations (2) and (3) (step S204).

TR (m, k) = ΣfJ (m, k, i) · gR (i) · [t (m, k, i) −t (m, k, i−1)] (2) TI (m, k) = ΣfJ (m, k, i) .gI (i). [T (m, k, i) -t (m, k, i-1)] ... (3) where: TR (m, k) and TI (m, k) are
It is a conversion value for the real part gR (i) and the imaginary part gI (i), and t (m, k, i) = t (i) / s (m) + tS (k), fJ (m, k, i). ) = F (j), j = [t (m, k, i) / DTT]. However, s (m) is a scale variable and tS (k)
Is a shift variable and m is an integer.

Then, the phase component θ (m, k) is calculated from the equations (2) and (3) by the following equation (4), and the result is output (step S205).

Θ (m, k) = (1 / π) tan-1 [TI (m, k) / TR (m, k)] (4) Next, data conversion was performed by the above procedure. Figure 7
~ It demonstrates with FIG.

7 to 9 show the results of conversion of brain potential oscillation data obtained in a state where no scent stimulation was given to slugs. Here, FIG. 7 (A) shows the brain potential oscillation data before conversion, and FIG. 7 (B) is the data obtained by quasi-wavelet conversion thereof. In (B),
The horizontal axis represents the shift variable tS (k), and the vertical axis represents the scale variable s.
(M) is shown. Further, the lightness and darkness of the color indicates the height of the phase component θ, that is, the darker the area is, the lighter the area is. Further, FIGS. 8A and 8B, 8C, and 8D are respectively (1-a), (1-b), and (1) in FIG. 7B.
-C) shows a cross section. Here, (B) is an enlarged view of the bottom of (A) in the direction of the height axis θ. And FIG. 9 is a bird's-eye view seen from the arrow direction of FIG.

FIGS. 10 to 12 show the results of conversion of the brain potential oscillation data obtained before and after the slug was given an odor stimulus. Here, FIG. 10 (A) shows the brain potential oscillation data before conversion, and FIG. 10 (B) is the data obtained by quasi-wavelet conversion thereof. Horizontal axis,
The vertical axis and the shading of the color have the same contents as in FIG. 11 (A) and (B), (C),
10D shows (2-a) and (2-) in FIG. 10B, respectively.
The cross section in (b) and (2-c) is shown. And FIG.
Is a bird's-eye view as seen from the direction of the arrow in FIG.

FIGS. 13 to 15 show the results of conversion of brain potential oscillation data obtained after scent stimulation was applied to slugs. Here, FIG. 13 (A) shows the brain potential oscillation data before conversion, and FIG. 13 (B) is the data obtained by pseudo wavelet conversion of this. The horizontal axis, the vertical axis, and the shading of the color have the same contents as in FIG. 14 (A) and (B), (C),
13D shows (3-a) and (3-) in FIG. 13B, respectively.
The cross section in (b) and (3-c) is shown. And in FIG.
Is a bird's-eye view as seen from the direction of the arrow in FIG.

FIG. 16 to FIG. 18 show the results of the conventional wavelet transform method, that is, the analysis of the brain potential oscillations of slugs without rearrangement of the basic wavelet data sequence. In FIG. 16, (A) shows brain potential oscillation data of slugs, the horizontal axis represents time (seconds),
The vertical axis represents the potential. 7B shows the result of converting the brain potential oscillation data by the conventional wavelet conversion, and FIG. 7B shows the results of the horizontal axis, the vertical axis, and the shading of color.
It has the same contents as. 17 (A), (B),
(C) and (D) are respectively (a) and (a) of FIG. 16 (B).
The section in (b), (c), (d) is shown. And
FIG. 17 is a bird's-eye view as seen from the direction of the arrow in FIG.

As is clear from the above data, in the analysis by the conventional wavelet transform, the transform result shows an extremely complicated shape as shown in FIG. 16 (b).
Therefore, even if there is a sudden change in the original brain potential oscillation data, it is extremely difficult to extract this.

On the other hand, in the pseudo wavelet transform according to this embodiment, in particular, FIGS. 8 (B), (C) and FIG.
As can be seen from the comparison with (B) and (C), the change in brain potential oscillation caused by the odor stimulus can be clearly recognized, and the rise and fall times of each oscillation waveform are, for example, as shown in FIG. It can be uniquely understood from the data.

As described in the section of the prior art, the Fourier transform can handle only stationary data, but the exponential function exp (-jωT) is a "delocalization function".
That is, it is an infinitely continuous periodic function. vice versa,
In the pseudo wavelet transform or wavelet transform according to the present invention, the basis function is a "localized function", that is, g (ti) has a certain value for a certain finite time and is "0" at all other times. Since it is such a function, it is possible to handle non-stationary data. Furthermore, in the pseudo wavelet transform according to the present invention, unlike the wavelet transform, since the data sequence of the basis function is rearranged,
By this procedure, we were able to obtain the transformation result which could not be obtained by the conventional wavelet transformation.

In this embodiment, the localization function shown in FIG. 6 is used as the basic wavelet data string, but the invention is not limited to this.

Further, in this embodiment, the pseudo wavelet transform was used for the analysis of the brain potential oscillation data of slugs.
Not limited to this, it is also possible to apply it to vibration data in other fields by changing the way of rearranging the basic wavelet data sequence.

[0034]

As described above, according to the present invention,
Since the inner product with time-series data is calculated after rearranging the basic wavelet data sequence, it is possible to uniquely grasp the rising and falling times of each vibration waveform and also for each vibration waveform. The change in the waveform becomes clear. Therefore, there is an effect that there is no individual difference when the vibration parameter is extracted from the vibration waveform data, and the vibration data analysis can be quantitatively and objectively performed.

[Brief description of drawings]

FIG. 1 is a block diagram showing an outline of a vibration data analyzing apparatus according to an embodiment of the present invention.

FIG. 2 is an explanatory diagram showing the potential vibration measuring device of FIG. 1 and its peripheral configuration.

FIG. 3 is a flowchart showing an outline of data conversion by the vibration data analysis device of FIG.

FIG. 4 is a flowchart showing the contents of numerical calculation in FIG.

FIG. 5 is an example of time series data showing brain potential oscillations of slugs.

FIG. 6 is an explanatory diagram showing an example of a basic wavelet data string.

FIG. 7 is an explanatory diagram showing a result of data conversion of brain potential oscillation data obtained in a state in which slugs are not given an odor stimulus by pseudo wavelet transform of the present invention.

8 is a cross-sectional view of FIG. 7 (B).

9 is a bird's-eye view from the arrow direction of FIG. 7 (B).

FIG. 10 is an explanatory diagram showing a result of data conversion of brain potential oscillation data obtained before and after applying an odor stimulus to slugs by a pseudo wavelet transform of the present invention.

11 is a cross-sectional view of FIG. 10 (B).

12 is a bird's-eye view from the arrow direction of FIG.

FIG. 13 is an explanatory diagram showing a result of transforming brain potential oscillation data obtained in a state in which slugs are given an odor stimulus by a pseudo wavelet transform of the present invention.

14 is a cross-sectional view of FIG. 13 (B).

FIG. 15 is a bird's-eye view from the direction of the arrow in FIG.

FIG. 16 is an explanatory diagram showing a result of wavelet transform of brain potential oscillation data by conventional wavelet transform.

17 is a cross-sectional view of FIG. 16 (B).

FIG. 18 is a bird's-eye view from the direction of the arrow in FIG.

[Explanation of symbols]

11 Potential vibration measuring instrument 12 A / D converter 13 Operation part 14 CRT display 15 Printer 17 memory 26 Measuring electrodes 31 Preamplifier

─────────────────────────────────────────────────── ─── Continuation of the front page (72) Masahiro Iseki Inventor Masahiro Iseki 2-18-18 Keihan Hondori, Moriguchi City, Osaka Sanyo Electric Co., Ltd. (56) Reference JP-A-2-168933 (JP, A) JP-A 3-128041 (JP, A) (58) Fields investigated (Int.Cl. ⁷ , DB name) A61B 5/0476 G06F 17/14 JISC file (JOIS) Practical file (PATOLIS) Patent file (PATOLIS)

Claims (2)

(57) [Claims] 1. An A / D conversion step for digitally converting the obtained time series data of the potential vibration, and a calculation step for performing data conversion on the digitally converted potential vibration data by a predetermined numerical calculation. An output step of outputting data, wherein the calculation step includes at least a step of reading time series data of potential oscillation digitally converted in the A / D conversion step, a step of reading a basic wavelet data string, and a reading step. A method of analyzing vibration data, comprising: a step of rearranging the basic wavelet data sequence, and a step of calculating an inner product of the read time series data and the rearranged basic wavelet data sequence. 2. Data acquisition means for obtaining time-series data of potential vibration from an object to be measured, A / D conversion means for digitally converting the obtained potential vibration data, and predetermined values for the digitally converted potential vibration data. An arithmetic means for performing data conversion by numerical calculation, and an output means for outputting the converted data are provided, wherein the arithmetic means at least outputs the time series data of the potential oscillation digitally converted by the analog / digital conversion means. A means for reading, a means for reading the basic wavelet data string, a means for rearranging the read basic wavelet data string, and an operation means for calculating the inner product of the read time series data and the rearranged basic wavelet data string, An apparatus for analyzing vibration data, comprising: