JP7278102B2 - Biological signal analysis device and its control method - Google Patents

Description

The present invention relates to a biological signal analyzer and its control method, and more particularly to a pulse wave signal analysis technique.

A pulse wave signal is a biological signal containing various information about the heart and blood vessels, and is widely used because it is easy to measure. Pulse wave signals can be measured in a variety of ways, but one method that places less burden on the subject and can be measured with a small device is to irradiate the body surface with light of a specific wavelength and transmit or reflect the light. A method of measuring changes in light intensity (light amount) as a pulse wave signal is known.

There is also known a biological information measuring device based on a pulse wave signal measured as such a change in light intensity. For example, a pulse oximeter is a device that measures arterial blood oxygen saturation (SpO ₂ ) based on the difference or ratio between the amounts of red light and infrared light transmitted or reflected by body tissue (Patent Document 1).

A pulse wave signal contains a variety of information, but in order to extract accurate biological information from the pulse wave, it is necessary to suppress the effects of components other than the pulse wave, particularly noise components. However, for pulse wave measurement based on the amount of transmitted light, for example, it is necessary to wear the measurement sensor (light-emitting part and light-receiving part) so that it is held between fingertips and earlobes where light easily penetrates, so environmental noise and body movement It is easily affected by noise due to noise, and a pulse wave signal with noise superimposed is likely to be measured.

Whether the pulse wave signal itself is to be analyzed or biological information is to be extracted from the measured pulse wave, it is desirable to use a pulse wave signal with little noise. In Patent Document 1, based on the pulse rate detected from the pulse wave signal, as a band-pass filter for extracting the component of the fundamental frequency of the pulse wave (1 Hz when the pulse rate is 60 beats per minute (bmp)), By selecting one of a plurality of bandpass filters with different center frequencies and using it as a noise removal filter applied to the pulse wave signal, the influence of respiration and body movement on _SpO2 accuracy is suppressed.

JP-A-2-172443

However, in the method of Patent Document 1, since the band-pass filter is selected based on the pulse rate detected from the pulse wave signal before noise removal, an appropriate band-pass filter when the pulse rate is affected by noise cannot be selected. In Patent Document 1, in order to suppress the influence of noise, the reliability of the pulse rate is determined based on variations in the pulse rate, and pulse rates determined to have low reliability are excluded from the calculation of the pulse rate. ing. Further, when the reliability of the pulse rate is low, the bandwidth of the band-pass filter is widened, and when the reliability of the pulse rate is high, the bandwidth of the band-pass filter is narrowed. However, as long as the band-pass filter is selected depending on the pulse rate detected from the pulse wave signal before noise removal, the influence of noise is inevitable to some extent. Also, when the bandwidth of the bandpass filter is widened, the noise removal capability is lowered.

SUMMARY OF THE INVENTION It is an object of the present invention to provide a biosignal analysis apparatus capable of extracting the fundamental frequency component of a pulse wave signal without determining the pulse rate, and a control method thereof. .

The above object is provided by an acquisition means for acquiring a first pulse wave signal based on a change in the amount of reflected light or transmitted light of light of a first wavelength applied to living tissue, and a fundamental frequency from the first pulse wave signal. and a signal processing means for extracting and outputting a component of, the signal processing means extracting a fundamental frequency component from the first pulse wave signal using an adaptive bandpass filter, and the adaptive bandpass filter passes A biosignal analysis apparatus characterized in that the center frequency follows the fundamental frequency by updating a coefficient that controls the center frequency of the band to be adjusted by an adaptive algorithm that uses the state variable of the adaptive bandpass filter. achieved by

With such a configuration, according to the present invention, it is possible to provide a biological signal analysis apparatus capable of extracting the fundamental frequency component of a pulse wave signal without obtaining the pulse rate, and a control method thereof.

1 is a block diagram showing a functional configuration example of an arterial blood oxygen saturation measuring device as an example of a biosignal analyzing device according to an embodiment of the present invention; FIG. 2 is a block diagram showing a functional configuration example of a signal processing unit in FIG. 1; FIG. 3 is a diagram showing a configuration example of an adaptive noise removal filter in FIG. 2; FIG. It is a figure which shows the operation example of the adaptive noise removal filter which concerns on embodiment. It is a figure which shows the operation example of the adaptive noise removal filter which concerns on embodiment. It is a figure which shows the operation example of the adaptive noise removal filter which concerns on embodiment.

Exemplary embodiments of the invention will now be described in detail with reference to the accompanying drawings. The following embodiments are not intended to limit the present invention according to the claims, and not all the configurations described in the present embodiments are essential to the present invention.

Here, an arterial blood oxygen saturation (SpO ₂ ) measuring device will be described as an example of the biological signal analyzing device according to the present invention. However, the present invention can be applied to any electronic device capable of acquiring, as a pulse wave signal, changes in the intensity (light amount) of light reflected by or transmitted through living tissue. Such electronic devices include medical devices such as biological information monitors, sleep evaluation devices, sphygmomanometers, and pulse wave monitors. It also includes, but is not limited to, general-purpose computer equipment (smartphones, tablet terminals, media players, smartwatches, game consoles, etc.) capable of acquiring pulse wave signals through external sensors, networks, and the like.

FIG. 1 is a block diagram showing a functional configuration example of an SpO ₂ measuring device according to one embodiment of the present invention.
The sensor unit 100 includes a first light emitting unit 101 that emits light of a first wavelength, a second light emitting unit 102 that emits light of a second wavelength, and a light receiving unit 103 that outputs an electrical signal corresponding to the amount of received light. have. The light-receiving unit 103 receives the light emitted by the first light-emitting unit 101 and the light emitted by the second light-emitting unit 102 as transmitted through body tissue (transmitted light) or reflected by body tissue (reflected light). are placed in

As the first and second light emitting units 101 and 102, LEDs that emit red light and infrared light are generally used in the SpO ₂ measuring device. However, the _wavelengths and the types _{of light} sources _are not limited to these _. and a2 _λ2 can be used (eg, a combination of a red light source and an infrared or green light source, etc.) that produces light of any wavelength λ1, λ2 that are significantly different from each other. In this embodiment, as an example, an LED that emits red light with a wavelength of 660 nm is used as the first light emitting unit 101 and an LED that emits infrared light with a wavelength of 900 nm is used as the second light emitting unit 102 .

In the case of the configuration for detecting the amount of transmitted light, the light receiving section 103 is arranged at a position facing the first and second light emitting sections 101 and 102 across the living tissue of the measurement site (earlobe, fingertip, etc.). Moreover, in the case of the configuration for detecting the amount of reflected light, the first light emitting unit 101, the light receiving unit 103, and the second light emitting unit 102 are arranged close to each other. Regardless of whether the amount of transmitted light is detected or the amount of reflected light is detected, the first and second light-emitting sections 101 and 102 are arranged close to each other, and the light-receiving section 103 is separated from the first and second light-emitting sections, respectively. It is arranged so as to receive transmitted light or reflected light of the light irradiated to the surface under similar conditions (for example, distance and angle).

The light receiving unit 103 receives transmitted light or reflected light of the light emitted by the first light emitting unit 101 and the second light emitting unit 102, and outputs an electric signal according to the amount of received light. The light-receiving unit 103 may be a light-receiving sensor, such as a photodiode or a phototransistor, whose sensitivity wavelength is the wavelength of transmitted light or reflected light to be detected. The light receiving unit 103 detects the first and second pulse wave signals as changes in the amount of transmitted light or reflected light depending on the measurement site for the light of the first and second wavelengths.

Signal processing unit 130 applies signal processing such as amplification, A/D conversion, and noise removal to the signal output from light receiving unit 103 , extracts a fundamental frequency component from the pulse wave signal, and outputs the extracted fundamental frequency component to control unit 110 . Signal processing section 130 extracts and outputs the fundamental frequency component of each of the first and second pulse wave signals output from light receiving section 103 . The fundamental frequency component is a band component having a center frequency of fundamental frequency of pulse wave [Hz]=pulse rate [bpm]/60 and having a predetermined bandwidth.

The control unit 110 has, for example, a programmable processor, a nonvolatile memory (ROM), and a volatile memory (RAM), and controls each unit by reading a program stored in the ROM into the RAM and executing it, thereby making the SpO ₂ measuring device to realize the function of At least part of the operation of the control unit 110 may be implemented by a hardware circuit such as a programmable logic array.

Driving section 120 drives first and second light emitting sections 101 and 102 in accordance with the light emission amount and light emission timing instructed by control section 110 . In order to detect the amount of transmitted light or the amount of reflected light for two wavelengths using one light receiving unit 103, the control unit 110 causes the first light emitting unit 101 and the second light emitting unit to alternately emit light for a predetermined time. Control timing.

Since the first light emitting unit 101 and the second light emitting unit 102 do not emit light at the same time, strictly speaking, the acquisition timings of the first pulse wave signal and the second pulse wave signal are different. However, by making the light emission frequencies of the first light emitting unit 101 and the second light emitting unit 102 sufficiently higher than the frequency component of the pulse wave, the measured values obtained by sampling the first pulse wave signal and the second pulse wave signal at the same timing can be treated as a group. Therefore, in the following description, it is assumed that the first pulse wave signal and the second pulse wave signal are obtained at the same timing.

The recording unit 140 is, for example, a non-volatile memory, and may be a removable recording medium such as a memory card. The control unit 110 records the pulse wave signal, the _SpO2 measurement value, and the like in the recording unit 140 in association with bibliographic items such as the date and time of measurement.
The display unit 150 is, for example, a liquid crystal display, and displays the SpO ₂ measurement value, the operating state of the SpO ₂ measuring device, the setting menu screen, etc. under the control of the control unit 110 .

The operation unit 160 includes buttons, switches, keys, etc. for the user to input instructions to the SpO ₂ measuring device. When display unit 150 is a touch display, the touch panel portion is included in operation unit 160 .
An external interface (I/F) 170 is a communication interface for wired or wireless communication with an external device.

FIG. 2 is a block diagram showing a functional configuration example of the signal processing section 130 in FIG. The AD conversion section 131 samples the analog signal output from the light receiving section 103 at a predetermined sampling frequency and converts it into a digital signal. The output section of the AD conversion section 131 is provided with a distribution circuit that distributes and outputs the digital signal into the first pulse wave signal derived from the first light emitting section 101 and the second pulse wave signal derived from the second light emitting section 102. be done.

The preprocessing unit 132 applies filter processing to the first and second pulse wave signals to remove power supply noise (hum noise), high frequency components and low frequency components that are clearly different from the frequency components of the pulse wave, etc. , apply preprocessing such as logarithmic transformation of signal values and normalization of amplitude values. Note that these processes are merely examples of processes that can be executed as preprocessing, and are not essential. Other treatments may also be applied as pretreatments. Further, normalization of the amplitude value normalizes the values of the second pulse wave signal and the first pulse wave signal so that the maximum value of the second pulse wave signal (signal derived from infrared light) is 1, for example. It may be a process to convert

The adaptive noise removal filter 133 extracts the fundamental frequency component of each of the first signal and the second signal output by the preprocessing section 132, and outputs them as the first signal and the second signal. The adaptive noise elimination filter 133 is a bandpass filter having a narrow passband width, with the fundamental frequency of the pulse wave signal as the center frequency of the passband. Further, the center frequency of the passband of the adaptive noise elimination filter 133 is adaptively controlled so as to follow fluctuations in the fundamental frequency of the pulse wave that depend on the characteristics of the living body. By applying such an adaptive bandpass filter, subject-derived noise such as body movement and breathing can be effectively removed with a simple configuration.

FIG. 3 is a diagram showing a circuit configuration example of an adaptive bandpass filter that can be used as the adaptive noise removal filter 133. As shown in FIG. In order to achieve a narrow pass band in this embodiment, an adaptive bandpass filter is implemented using an adaptive notch filter. An adaptive notch filter is used as a filter that adaptively estimates the frequency of a narrowband signal contained in an input signal and blocks the narrowband signal.

The adaptive noise removal filter 133 includes an adaptive notch filter for generating an output signal y(n) that rejects a narrow band centered on the fundamental frequency of the input signal u(n), and an adaptive notch filter for generating an output signal y(n) from the input signal u(n). a subtractor for subtracting (n) and outputting u(n)-y(n).

In this embodiment, Simplified Lattice Algorithm (SLA) is used as an adaptive algorithm for estimating the frequency of a narrowband signal blocked by an adaptive notch filter (that is, the fundamental frequency of a pulse wave signal). See, for example, P. A. Regalia, "An Improved Lattice-Based Adaptive IIR Notch Filter", IEEE Trans. Signal Process., Vol. 39, No. 9, pp. 2124-2128, Sept., 1991. sea bream. However, other adaptive algorithms such as the Lattice Gradient Algorithm (LGA), LMS algorithm, NLMS algorithm, RLS algorithm, etc. may be used to estimate the fundamental frequency of the pulse wave.

とすることにより、中心周波数ω、帯域幅Ｑの周波数成分を阻止するノッチフィルタとして機能する。αは帯域幅Ｑによって定まる固定値である。このノッチフィルタの伝達関数は以下の通りである。

そして、このノッチフィルタのβについて適応アルゴリズムを適用することで、適応ノッチフィルタを実現する。ここでは適応アルゴリズムとしてＳＬＡを用いるため、フィルタの状態変数である適応信号ｘ（ｎ）を用いてβを更新することにより、阻止する帯域の中心周波数（ノッチ周波数）を入力信号の基本周波数に追従させる。 The adaptive notch filter used by the adaptive noise reduction filter 133 does not use the adaptive signal x(n),

As a result, it functions as a notch filter that blocks the frequency component of the center frequency ω and the bandwidth Q. α is a fixed value determined by the bandwidth Q; The transfer function of this notch filter is:

Then, by applying an adaptive algorithm to β of this notch filter, an adaptive notch filter is realized. Since SLA is used as the adaptive algorithm here, the center frequency (notch frequency) of the band to be blocked follows the fundamental frequency of the input signal by updating β using the adaptive signal x(n), which is the state variable of the filter. Let

そして、βを以下の様に適応させることにより適応ノッチフィルタが阻止する中心周波数を、入力信号ｕ（ｎ）の基本周波数に追従させることができる。

ここで、ステップサイズμは固定値であり、例えば実験的に求めることができる。なお、βは、ＳＬＡ以外の適応アルゴリズムを用いて更新させてもよい。 The transfer function of the adaptive notch filter portion of the adaptive bandpass filter in FIG. 3 is as follows.

Then, by adapting β as follows, the center frequency blocked by the adaptive notch filter can be made to follow the fundamental frequency of the input signal u(n).

Here, the step size μ is a fixed value and can be obtained experimentally, for example. Note that β may be updated using an adaptive algorithm other than SLA.

As shown in equation (5), the center frequency β of the passband of the adaptive noise reduction filter 133 of this embodiment is sequentially updated based on the output signal y, the adaptive signal x, and the step size μ. It is not necessary to calculate the pulse rate or evaluate the reliability of the calculated pulse rate in order to update the center frequency β.

The adaptive noise reduction filter 133 separately applies filtering with the same filter coefficient to the first pulse wave signal and the second pulse wave signal, extracts the fundamental frequency component, and generates the first signal and the second signal. . The center frequency β is calculated from the output signal y of one of the first pulse wave signal or the second pulse wave signal and the adaptive signal x. By applying the processing, the same filtering process is applied to the first pulse wave signal and the second pulse wave signal. Then, the first signal and the second signal generated by adaptive noise removal filter 133 are supplied from signal processing section 130 to control section 110 .

The control unit 110 can apply a known method to the first signal derived from red light and the second signal derived from infrared light to acquire (calculate) biological information such as pulse rate and _SpO2 . For example, the control unit 110 first cuts out the first signal and the second signal by one beat section under a predetermined condition. Then, the control unit 110 determines the peak value (maximum value in the interval where the amplitude value is positive) and its position, and the bottom value (minimum value in the interval where the amplitude value is negative) and its position for at least one signal for each beat interval. are detected respectively.

The control unit 110 calculates the instantaneous pulse rate (beats/minute) using the time difference between the peak (or bottom) positions in the adjacent two-beat interval as the period of one beat. Further, the control unit 110 calculates the maximum amplitude values of the first and second signals from the difference between the peak value and the bottom value, and the ratio R=maximum amplitude value of the first signal/maximum value of the second signal. Calculate the amplitude value. The control unit 110 can obtain the _SpO2 for each beat by referring to a table for converting the ratio R into _SpO2 , which is pre-stored in the ROM, for example. For the pulse rate and _SpO2 , a moving average value for a predetermined number of continuous beats may be obtained, and the moving average value may be used for display.

In this way, in this embodiment, in addition to environmental noise, the adaptive noise removal filter 133 removes subject-derived noise such as body movement and respiration, and based on the fundamental frequency component of the pulse wave, the pulse rate and SpO ₂ of the subject's body are detected. You can ask for information. Therefore, accurate values can be obtained.

Also, the fundamental frequency of the pulse wave is estimated by an adaptive algorithm based on the input value to the filter, the output value of the filter, and the state variable of the filter, and the pass band of the adaptive noise removal filter 133 follows the fundamental frequency of the pulse wave. . Therefore, adaptive processing can be executed as part of filtering processing, and there is no need to perform special processing other than filtering processing. For example, compared to obtaining the fundamental frequency component of the pulse wave by frequency analysis such as wavelet transform, the calculation cost can be significantly reduced. Furthermore, since noise is removed using a narrow band filter, noise removal performance is high. As a result, the accuracy of finally obtained biometric information can be improved.

(evaluation)
4 to 6 show evaluation results of the noise removal effect of the adaptive noise removal filter 133 according to this embodiment. Here, the notch frequency is 1 Hz, the bandwidth Q is 0.1 Hz (that is, the initial value of the stop frequency band is 0.9 Hz to 1.1 Hz), and the initial values of α and β are determined and shown in FIG. Signal waveforms before and after application of the adaptive noise removal filter 133 of the configuration are shown. Note that the sampling rate in the AD converter 131 was set to 250 Hz. Also, the measurement was performed by attaching the sensor unit 100 to the tip of the index finger.

Here, after a certain amount of time has elapsed from the start of measurement of the pulse wave signal, the fingertip on which the sensor unit 100 is attached is operated to generate noise, and the noise removal effect is evaluated. The noise-generating motions were the motion of tapping the fingertip (Tapping), the motion of scratching with the fingertip (Scratching), and the motion of squeezing the sponge with the hand (Squeezing). 4, 5, and 6 show the output waveform of the AD converter 131 when these operations are performed during measurement for the second pulse wave signal derived from infrared light. The waveform of the second signal extracted at 133 is shown at the bottom. Also, in each figure, the arrow indicates the period during which the operation for generating noise is performed.

From FIGS. 4 to 6, it can be seen that in the adaptive noise removal filter 133 of the present embodiment, the frequency of the second signal is almost the same even during the period when the noise is mixed with the period before the noise is mixed. This indicates that the fundamental frequency of the second pulse wave signal can be accurately estimated and extracted even when noise is mixed. Therefore, it is possible to accurately detect the pulse rate based on the second signal.

In addition, the biological signal analysis apparatus according to the present invention is a program that causes a generally available general-purpose information processing apparatus such as a personal computer to realize the operations of the control unit 110, the driving unit 120, and the signal processing unit 130 described above. It can also be implemented as (application software). Therefore, such programs and recording media storing the programs (optical recording media such as CD-ROMs and DVD-ROMs, magnetic recording media such as magnetic discs, semiconductor memory cards, etc.) also constitute the present invention. . Moreover, the present invention is not limited to the above-described embodiments, and various modifications and changes are possible within the scope of the gist of the present invention.

DESCRIPTION OF SYMBOLS 100... Sensor part 101... 1st light emission part 102... 2nd light emission part 103... Light-receiving part 110... Control part 120... Drive part 130... Signal processing part

Claims (11)

Acquisition means for acquiring a first pulse wave signal based on a change in the amount of reflected light or transmitted light of the light of the first wavelength applied to the living tissue;
and signal processing means for extracting and outputting a fundamental frequency component from the first pulse wave signal,
The signal processing means extracts the fundamental frequency component from the first pulse wave signal using an adaptive band-pass filter, and sets a coefficient for controlling a center frequency of a band passed by the adaptive band-pass filter to the adaptive band-pass filter. causing the center frequency to track the fundamental frequency by updating with an adaptive algorithm using state variables of the bandpass filter;
A biological signal analysis device characterized by: The adaptive bandpass filter is
an adaptive notch filter that blocks a band centered on the fundamental frequency of the first pulse wave signal;
a subtractor that subtracts the output signal of the adaptive notch filter from the first pulse wave signal;
2. The biological signal analysis apparatus according to claim 1, wherein the component of the fundamental frequency is extracted using 3. The biological signal analysis apparatus according to claim 2, wherein said adaptive notch filter has fixed coefficients and said updated coefficients. 4. The biological signal analysis apparatus of claim 3, wherein said adaptive algorithm further updates said coefficients using said output signal. The transfer coefficient of the adaptive notch filter is

5. The biological signal analysis apparatus according to any one of claims 2 to 4 , wherein α is a fixed coefficient, and β is the updated coefficient. The living body according to any one of claims 1 to 5 , wherein the adaptive algorithm is any one of Simplified Lattice Algorithm (SLA), Lattice Gradient Algorithm (LGA), LMS algorithm, NLMS algorithm, and RLS algorithm. Signal analyzer. 7. The apparatus according to any one of claims 1 to 6, further comprising pulse rate obtaining means for obtaining a pulse rate based on the fundamental frequency component of said first pulse wave signal extracted by said signal processing means. The biological signal analysis device according to 1. The acquiring means further acquires a second pulse wave signal based on a change in the amount of reflected light or transmitted light of the second wavelength light irradiated to the living tissue,
The signal processing means further extracts and outputs a fundamental frequency component from the second pulse wave signal using the adaptive bandpass filter.
The biological signal analysis apparatus according to any one of claims 1 to 7, characterized in that: Further, SpO for acquiring arterial blood oxygen saturation (SpO ₂ ) based on the fundamental frequency component of the first pulse wave signal and the fundamental frequency component of the second pulse wave signal extracted by the signal processing means 9. The biological signal analysis apparatus according to claim 8, further comprising ₂ acquisition means. A control method for a biosignal analysis device executed by the biosignal analysis device, comprising:
an acquisition step of acquiring a first pulse wave signal based on a change in the amount of reflected light or transmitted light of the light of the first wavelength applied to the living tissue;
a signal processing step of extracting and outputting a fundamental frequency component from the first pulse wave signal;
In the signal processing step, an adaptive band-pass filter is used to extract the fundamental frequency component from the first pulse wave signal , and a coefficient for controlling the center frequency of the band passed by the adaptive band-pass filter is applied to the adaptive band-pass filter. update by an adaptive algorithm using the state variables of the bandpass filter;
A control method for a biological signal analysis device, characterized by: A program that causes a computer to realize the operation of at least the signal processing means of the biological signal analysis apparatus according to any one of claims 1 to 9.

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