JP2023154347A - Time series data processing device, time series data processing method, and computer program - Google Patents

Abstract

To provide a time series data processing device capable of data processing excluding arbitrariness.SOLUTION: Provided is a time series data processing device 10 including: a transformation unit 101 that transforms time series data into data of a frequency domain; a selection unit 102 that selects a predetermined frequency component from the data of the frequency domain; and an inverse transformation unit 103 that inversely transforms the frequency component selected by the selection unit 102 into data of a time domain. The selection unit 102 selects a frequency component with which a difference between the time series data and the data inversely transformed by the inverse transformation unit 103 is smaller than a predetermined threshold.SELECTED DRAWING: Figure 1

Description

The present invention relates to a time-series data processing device, a time-series data processing method, and a computer program.

In order to accurately obtain biological information of a subject based on the biological data of the subject, it is required to remove noise superimposed on the biological data. Therefore, techniques related to time-series data processing for removing noise from time-series data such as biological data of a subject have been disclosed. Patent Document 1 discloses a technology that reduces the burden on the subject compared to using sleep polysomnography and detects heartbeat intervals with higher accuracy than conventional methods. .

Specifically, in Patent Document 1, a first respiratory waveform is calculated based on a first step size and a vibration waveform, and a second step size and a vibration waveform having a period different from the first step size are calculated. A second respiratory waveform is calculated based on the above, a first pulse waveform is calculated by subtracting the first respiratory waveform from the vibration waveform, and a second pulse waveform is calculated by subtracting the second respiratory waveform from the vibration waveform. Disclosed is a biological information acquisition device that calculates a pulse waveform and calculates a heart rate variability index based on a pulse waveform that has a small number of abnormal peak intervals among the pulse waveforms. .

In Patent Document 1, as a preprocessing, a bandpass filter removes components of a frequency higher than the period of normal body movement, breathing, and pulse waves and a DC component from the output of the microwave radar. Further, in Patent Document 1, a simple moving average method or a weighted moving average method is used when calculating the first respiratory waveform as preprocessing.

JP2017-104360A

However, with the technology disclosed in Patent Document 1, it is possible that necessary data may be included in the frequency band components removed by the band-pass filter, or noise may be included in the components that have passed through the band-pass filter. Unable to determine gender. Furthermore, with the technique disclosed in Patent Document 1, it is not possible to determine which frequency component has been manipulated using only the moving average. Furthermore, in the technique disclosed in Patent Document 1, the preprocessing depends on the discretion of the analyst and the preprocessing is likely to be arbitrary, so there is a possibility of drawing an incorrect conclusion.

The present invention has been made in view of the above points, and an object of the present invention is to provide a time-series data processing device, a time-series data processing method, and a computer program that are capable of data processing that eliminates arbitrariness.

In order to achieve the above object, a time series data processing device according to an aspect of the present invention includes a conversion unit that converts time series data into frequency domain data, and a conversion unit that selects a predetermined frequency component from the frequency domain data. and an inverse transformation unit that inversely transforms the frequency component selected by the selection unit into time domain data, and the selection unit is configured to convert the time series data and the frequency component after the inverse transformation by the inverse transformation unit. Selecting a frequency component whose difference from the data is smaller than a predetermined threshold value.

The selection unit may select a frequency component for which data after inverse transformation by the inverse transformation unit is self-consistent.

The selection unit is configured to select, when the difference between the data after the inverse transformation of the first frequency component and the data after the inverse transformation of the second frequency component including the first frequency component becomes smaller than a predetermined threshold value. , it may be determined that the data after inverse transformation by the inverse transformation unit has become self-consistent.

The time series data may be a biological signal or a signal based on a biological signal.

In order to achieve the above object, in a time series data processing method according to another aspect of the present invention, a processor converts time series data into frequency domain data, and extracts a predetermined frequency component from the frequency domain data. The selected frequency component is inversely transformed into time domain data, and a process of selecting a frequency component for which a difference between the time series data and the data after the inverse transformation is smaller than a predetermined threshold value is executed.

In order to achieve the above object, a computer program according to another aspect of the present invention causes a computer to convert time series data to frequency domain data, select a predetermined frequency component from the frequency domain data, A process of inversely transforming the selected frequency component into time domain data and selecting a frequency component for which a difference between the time series data and the data after the inverse transformation is smaller than a predetermined threshold value is executed.

According to the present invention, it is possible to provide a time-series data processing device, a time-series data processing method, and a computer program that are capable of data processing without arbitrariness.

1 is a diagram showing a schematic configuration of a time-series data processing system according to an embodiment. FIG. 2 is a block diagram showing an example of a hardware configuration of a time-series data processing device. FIG. 2 is a block diagram showing an example of a functional configuration of a time-series data processing device. It is a flowchart which shows the flow of time series data processing by a time series data processing device. It is a graph showing an example of a convergence process of time series data, for example, brain wave data. FIG. 3 is a diagram showing an example of pulse wave data, which is an example of time-series data. FIG. 2 is a diagram showing an example of brain wave data, which is an example of time-series data. This is a graph in which original pulse wave data and time series data reconstructed based on frequency components whose error is below a threshold are superimposed. This is a graph in which original brain wave data and time series data reconstructed based on frequency components whose error is below a threshold are superimposed.

An example of an embodiment of the present invention will be described below with reference to the drawings. In addition, the same reference numerals are given to the same or equivalent components and parts in each drawing. Furthermore, the dimensional ratios in the drawings are exaggerated for convenience of explanation and may differ from the actual ratios.

FIG. 1 is a diagram showing a schematic configuration of a time-series data processing system according to this embodiment.

The time-series data processing system shown in FIG. 1 includes a biological signal measuring device 1 that measures biological signals, which are an example of time-series data, and a time-series data processing device that processes the biological signals measured by the biological signal measuring device 1. 10.

The biological signals measured by the biological signal measuring device 1 are, for example, heartbeat, electrocardiogram, magnetocardiogram, electroencephalogram, magnetoencephalogram, pulse wave, body temperature, blood pressure, electromyogram, skin potential, or signals processed from these. The invention is not limited to such examples. Examples of the signal based on the biosignal include a signal obtained by combining a plurality of the above-mentioned biosignals, or a numerical value calculated based on the above-mentioned biosignal. The signal based on the biosignal may be a combination of the biosignal and other signals.

The biological signals measured by the biological signal measuring device 1 may have noise superimposed thereon. For example, if the biological signal is based on a pulse wave, it may include not only the pulse wave of the subject but also vibrations or noise associated with body movements or breathing of the subject. When noise is superimposed, accurate processing cannot be performed on biological signals. Therefore, the noise superimposed on the biological signal needs to be removed before the biological signal is processed.

Therefore, the time-series data processing device 10 performs processing on the biosignal sent from the biosignal measurement device 1 to remove noise superimposed on the biosignal, and then performs processing related to the biosignal. The time-series data processing device 10 according to the present embodiment converts the biological signal into frequency domain data and selects a predetermined frequency component from the frequency domain data when removing noise superimposed on the biological signal. The time-series data processing device 10 then inversely transforms the selected frequency components into time domain data, and selects a frequency component for which the difference between the biological signal and the data after the inverse transformation is smaller than a predetermined threshold. Noise superimposed on the biological signal sent from the signal measuring device 1 is removed.

The time-series data processing device 10 removes noise superimposed on the biosignal sent from the biosignal measurement device 1, and then executes processing on the biosignal. The processing performed on biological signals by the time-series data processing device 10 includes, for example, displaying waveforms based on biological signals, displaying heart rate, body temperature, blood pressure, and other biological information.

The time series data processing device 10 performs the conversion to frequency domain data and inverse conversion in this way, and selects the frequency component for which the difference between the biological signal and the data after the inverse conversion is smaller than a predetermined threshold. , it becomes possible to process data without arbitrariness.

Note that the time-series data processing device 10 is applicable to all devices that handle time-series data, such as servers on cloud networks, personal computers, smartphones, smart watches, smart bands, wearable devices, medical devices, and diagnostic devices. Further, the time series data handled by the time series data processing device 10 is not limited to biological signals, and any type of data may be used as long as it is temporally continuous data.

FIG. 2 is a block diagram showing an example of the hardware configuration of the time-series data processing device 10. As shown in FIG.

As shown in FIG. 2, the time series data processing device 10 includes a CPU (Central Processing Unit) 11, a ROM (Read Only Memory) 12, a RAM (Random Access Memory) 13, a storage 14, an input section 15, a display section 16, and It has a communication interface (I/F) 17. Each configuration is communicably connected to each other via a bus 19.

The CPU 11 is a central processing unit that executes various programs and controls various parts. That is, the CPU 11 reads a program from the ROM 12 or the storage 14 and executes the program using the RAM 13 as a work area. The CPU 11 controls each of the above components and performs various arithmetic operations according to programs recorded in the ROM 12 or the storage 14. In this embodiment, the ROM 12 or the storage 14 stores a time-series data processing program for removing noise superimposed on the biological signal sent from the biological signal measurement device 1 .

The ROM 12 stores various programs and data. The RAM 13 temporarily stores programs or data as a work area. The storage 14 is constituted by a storage device such as an HDD (Hard Disk Drive), an SSD (Solid State Drive), or a flash memory, and stores various programs including an operating system and various data.

The input unit 15 includes a pointing device such as a mouse and a keyboard, and is used to perform various inputs.

The display unit 16 is, for example, a liquid crystal display, and displays various information. The display section 16 may adopt a touch panel method and function as the input section 15.

The communication interface 17 is an interface for communicating with other devices, and uses standards such as Ethernet (registered trademark), FDDI, and Wi-Fi (registered trademark), for example.

When executing the above-described time-series data processing program, the time-series data processing device 10 uses the above-mentioned hardware resources to realize various functions. The functional configuration realized by the time series data processing device 10 will be explained.

Next, the functional configuration of the time series data processing device 10 will be explained.

FIG. 3 is a block diagram showing an example of the functional configuration of the time-series data processing device 10.

As shown in FIG. 3, the time-series data processing device 10 includes a conversion section 101, a selection section 102, an inverse conversion section 103, and a data processing section 104 as functional configurations. Each functional configuration is realized by the CPU 11 reading and executing a time series data processing program stored in the ROM 12 or the storage 14.

The conversion unit 101 converts the biological signal sent from the biological signal measuring device 1 into the frequency domain. Specifically, if the biosignal sent from the biosignal measuring device 1 is x(t) (t=[1:T]), the conversion unit 101 converts the biosignal into the frequency domain by the Fourier transform F[x(t)]. Convert to

The selection unit 102 selects a predetermined frequency component from the frequency domain data converted by the conversion unit 101. Specifically, the selection unit 102 selects the frequency component [m:Tm] of the Fourier transform F[x(t)] of the biological signal. Let the frequency component [m:Tm] selected by the selection unit 102 be F _m [x(t)]. Note that m is a value from 1 to T/2. Then, the selection unit 102 selects a frequency component F _m [x(t )]. Details of the method for selecting the frequency component F _m [x(t)] will be described later.

The inverse transformation unit 103 performs inverse transformation of the frequency component data selected by the selection unit 102 into time domain data. Specifically, the inverse transform unit 103 performs an inverse Fourier transform F ⁻¹ [F _m [x(t)]] on the frequency component F _m [x(t)] selected by the selection unit 102. When selecting the frequency component F _m [x(t)], the selection unit 102 evaluates the error E(m) defined by the root mean square error shown in Formula (1).

The selection unit 102 increases m from 1 to T/2 and determines that the time series data has become self-consistent when E(m)<C, and selects the noise-removed time series data x _m ( t) is adopted. C is a threshold value for determination. Being self-consistent refers to a state in which the value related to the difference between the original time series data and the data after inverse transformation has converged to less than a predetermined threshold value C. That is, the selection unit 102 may select frequency components so that the time series data reconstructed by the inverse transformation unit 103 is self-consistent.

The data processing unit 104 performs data processing on the time series data x _m (t) selected by the selection unit 102 so that E(m)<C. The data processing unit 104 performs processing such as displaying waveforms based on biological signals, heart rate, body temperature, blood pressure, and other biological information.

By having such a configuration, the time series data processing device 10 performs conversion to frequency domain data and inverse conversion, and extracts frequency components for which the difference between the biological signal and the data after inverse conversion is smaller than a predetermined threshold. By making this selection, data processing that eliminates arbitrariness becomes possible. The time series data processing device 10 can guarantee the accuracy required for time series analysis by evaluating errors in the time domain while limiting the target frequencies of time series data.

Note that in this embodiment, Fourier transform is used to transform data from time domain data to frequency domain data, but the present invention is not limited to such an example. The time series data processing device 10 may use any transformation method such as Laplace transform or wavelet transform to transform data from time domain data to frequency domain data.

Next, the operation of the time series data processing device 10 will be explained.

FIG. 4 is a flowchart showing the flow of time-series data processing by the time-series data processing device 10. Time series data processing is performed by the CPU 11 reading a time series data processing program from the ROM 12 or the storage 14, expanding it to the RAM 13, and executing it. The time-series data processing shown in FIG. 4 is preprocessing performed prior to data processing of the biological signal x(t) sent from the biological signal measurement device 1, and is a preprocessing that is performed to eliminate noise superimposed on the biological signal x(t). This is a process to remove.

First, in step S101, the CPU 11 converts the biological signal x(t) sent from the biological signal measuring device 1 into frequency domain data by Fourier transform F[x(t)].

Subsequently, in step S102, the CPU 11 selects a certain frequency component [m:Tm] from the frequency domain data. The CPU 11 first selects the frequency component [1:T-1] of m=1. The frequency component selected by the CPU 11 in step S102 is assumed to be F ₁ [x(t)].

Subsequently, in step S103, the CPU 11 performs inverse Fourier transform on the frequency component F _m [x(t)] selected in step S102 to obtain F ⁻¹ [F _m [x(t)]]. When m=1, the CPU 11 performs inverse Fourier transform on the frequency component F ₁ [x(t)] to obtain F ⁻¹ [F ₁ [x(t)]].

Subsequently, in step S104, the CPU 11 calculates E(m) using the above formula (1), and evaluates E(m). Specifically, the CPU 11 evaluates E(m) depending on whether E(m) is less than a predetermined evaluation error C.

Subsequently, in step S105, the CPU 11 determines whether E(m)<C. In other words, the CPU 11 determines whether the time series data reconstructed by inverse Fourier transform is self-consistent. As a result of the determination in step S105, if E(m)<C (step S105; Yes), the CPU 11 ends the series of processing. On the other hand, as a result of the determination in step S105, if E(m)<C is not satisfied (step S105; No), then in step S106, the CPU 11 increases m by one and selects the frequency component in step S102. Return to processing.

By having such processing, the time series data processing device 10 performs conversion to frequency domain data and inverse conversion, and extracts frequency components for which the difference between the biological signal and the data after inverse conversion is smaller than a predetermined threshold. By making this selection, data processing that eliminates arbitrariness becomes possible.

Next, the effects of the time series data processing device 10 will be explained.

FIG. 5 is a graph showing an example of a convergence process of time series data, for example, brain wave data. In the graph of FIG. 5, the vertical axis represents the root mean square (RMS), that is, E(m) obtained by formula (1), and the horizontal axis represents the step of calculating the self-consistent field. number, that is, the value of m. As shown in FIG. 5, it can be seen that as the value of m increases, the value of E(m) decreases.

FIG. 6 is a diagram showing an example of pulse wave data, which is an example of time series data. The time series data processing device 10 can change the selection range of pulse wave data by changing the value of m. In the example of FIG. 6, it is assumed that m=250 and E(m)<C. In this case, data of approximately 166 Hz to 250 Hz and 750 to 834 Hz can be extracted as actual pulse wave data.

FIG. 7 is a diagram showing an example of electroencephalogram data, which is an example of time-series data. The time series data processing device 10 can change the selection range of brain wave data by changing the value of m. In the example of FIG. 7, it is assumed that m=250 and E(m)<C. In this case, data of approximately 166 Hz to 250 Hz and 750 to 834 Hz can be extracted as actual brain wave data.

FIG. 8 is a graph in which the original pulse wave data and the time series data reconstructed based on the frequency component adopted at the time of m=250, where E(m)<C, are superimposed. "Original" in FIG. 8 is the original pulse wave data, and "smoothed" is the reconstructed time series data. In this way, the time series data reconstructed based on the frequency components where E(m)<C is similar to the original pulse wave data, and is superimposed on the original pulse wave data. It can be seen that the noise components present in the image have been removed.

FIG. 9 is a graph in which the original brain wave data and the time series data reconstructed based on the frequency component adopted at the time of m=250, where E(m)<C, are superimposed. "Original" in FIG. 9 is the original brain wave data, and "smoothed" is the reconstructed time series data. In this way, the time series data reconstructed based on the frequency components where E(m)<C is similar to the original brain wave data, and is free from noise superimposed on the original brain wave data. It can be seen that the components have been removed.

From the above, the time series data processing device 10 according to the present embodiment clearly handles the target frequency band in the frequency domain and directly evaluates errors in the time domain, thereby preparatory to time series causal analysis, etc. In processing, stable noise removal is possible without depending on the discretion of the analyst.

Note that the time series data processing that the CPU reads and executes the software (program) in each of the above embodiments may be executed by various processors other than the CPU. In this case, the processor includes a PLD (Programmable Logic Device) whose circuit configuration can be changed after manufacturing, such as an FPGA (Field-Programmable Gate Array), and an ASIC (Application Specific Integrated Cipher). rcuit) to execute specific processing such as An example is a dedicated electric circuit that is a processor having a specially designed circuit configuration. Furthermore, time-series data processing may be executed by one of these various processors, or by a combination of two or more processors of the same or different types (for example, multiple FPGAs, or a combination of a CPU and an FPGA). combinations etc.). Further, the hardware structure of these various processors is, more specifically, an electric circuit that is a combination of circuit elements such as semiconductor elements.

Further, in each of the above embodiments, a mode has been described in which a time-series data processing program is stored (installed) in the ROM or storage in advance, but the present invention is not limited to this. The program can be stored in non-temporary memory such as CD-ROM (Compact Disk Read Only Memory), DVD-ROM (Digital Versatile Disk Read Only Memory), and USB (Universal Serial Bus) memory. (non-transitory) recorded on a recording medium It may be provided in the form of Further, the program may be downloaded from an external device via a network.

Although the embodiments of the present invention have been described above in detail with reference to the accompanying drawings, the technical scope of the present invention is not limited to such examples. It is clear that a person with ordinary knowledge in the technical field of the present invention can come up with various changes or modifications within the scope of the technical idea stated in the claims. It is understood that various changes or modifications naturally fall within the technical scope of the present invention.

1 Biosignal measurement device 10 Time series data processing device

Claims (6)

a conversion unit that converts time series data to frequency domain data;
a selection unit that selects a predetermined frequency component from the frequency domain data;
an inverse transformation unit that inversely transforms the frequency component selected by the selection unit into time domain data;
Equipped with
The selection unit selects a frequency component for which a difference between the time series data and data after inverse transformation by the inverse transformation unit is smaller than a predetermined threshold.
Time series data processing device. The time-series data processing device according to claim 1, wherein the selection unit selects a frequency component for which data after inverse transformation by the inverse transformation unit is self-consistent. The selection unit is configured to select, when the difference between the data after the inverse transformation of the first frequency component and the data after the inverse transformation of the second frequency component including the first frequency component becomes smaller than a predetermined threshold value. 3. The time-series data processing device according to claim 2, wherein the time-series data processing device determines that the data after inverse transformation by the inverse transformation unit has become self-consistent. The time-series data processing device according to claim 1, wherein the time-series data is a biological signal or a signal based on a biological signal. The processor
Convert time series data to frequency domain data,
selecting a predetermined frequency component from the frequency domain data;
inversely transforming the selected frequency components into time domain data;
selecting a frequency component for which a difference between the time series data and the data after inverse transformation is smaller than a predetermined threshold;
A time series data processing method that performs processing. to the computer,
Convert time series data to frequency domain data,
selecting a predetermined frequency component from the frequency domain data;
inversely transforming the selected frequency components into time domain data;
selecting a frequency component for which a difference between the time series data and the data after inverse transformation is smaller than a predetermined threshold;
A computer program that executes a process.