KR101714927B1 - Movement noise detection and cancellation algorithm using multiplexing signal receiving - Google Patents

Abstract

The present invention provides a method for determining and removing motion noise using multi-signal reception which can remove a problem caused by high frequency noise and motion noise in measuring a PPG signal which is one of body signals. The method for determining and removing motion noise using multi-signal reception comprises: a step of measuring a PPG signal through multiple channels; a step of generating a signal where high frequency noise is removed from the measured PPG signal; a step of setting a reference signal in the signal where the high frequency noise is removed; a step of applying a cross correlation technique to compensate for a difference between multiplexed signals to remove motion noise from the multichannel PPG signal based on the reference signal; a step of selecting a base signal based on the set reference signal; a step of using the base signal to perform wavelet conversion; and a step of using a wavelet converted value to determine and remove the motion noise.

Description

[0001] MOVEMENT NOISE DETECTION AND CANCELLATION ALGORITHM USING MULTIPLEXING SIGNAL RECEIVING [0002]

The present invention relates to a method and apparatus for determining and removing motion noise using multiple signals, and more particularly, to a method and apparatus for determining whether motion noise is contained in a PPG signal, which is one of biological signals, using multiple signal reception and time synchronization, And more particularly, to a method and apparatus for determining and removing motion noise using multiple signal reception.

As the incidence of various diseases has increased recently, there is an increasing interest in methods for preventing the health and various diseases of modern people. Many medical devices that determine the presence or absence of a disease in the body and treat the disease are also developed and can be identified by a simple method. However, these methods need to be directly inspected by the hospital, and society members seek a way to check their body condition in real time rather than go to the hospital, so that they can receive medical services without restriction of time and space. Research on U-healthcare technology, which is a ubiquitous and remote healthcare service utilizing technology, is actively being conducted.

Electroencephalography (EEG), electrocardiography (EMG), electromyography (PPG), photodynamic pulse wave, and electroogeography (EOG)

The signal that is easy to measure and monitor in various biological signal processing is PPG. In the case of EEG and ECG, the electrodes should be attached to the scalp and chest, and movement is very limited. However, since PPG uses one sensor attached to the body end region such as finger, earlobe, and toe, It is advantageous in that the user's motion can be secured relative to the EEG. However, because of the problem that the signal is easily distorted due to the noise of the measurement environment itself and the movement of the user, it is necessary to determine whether the noise is included in the signal processing and to remove the noise.

Korean Patent Publication No. 10-2010-0008239

Disclosure of Invention Technical Problem [8] Accordingly, the present invention has been made to solve the above-mentioned problems occurring in the prior art, and it is an object of the present invention to provide a method for determining whether or not motion noise is included in a PPG signal, The purpose is to do.

In order to achieve the above object, the present invention provides a method and apparatus for motion noise determination and removal using multiple signal reception capable of eliminating problems caused by high frequency noise and motion noise occurring in PPG signal measurement, Measuring a PPG signal through the first antenna; Generating a signal from which the high frequency noise is removed from the measured PPG signal; Setting a reference signal in the signal from which the high-frequency noise is removed; Correcting a difference between the multiplexed signals by applying a cross-correlation technique to remove motion noise from the multi-channel PPG signal based on the reference signal; Selecting a base signal based on the set reference signal; Performing wavelet transform using the base signal; And determining and removing motion noise using the wavelet-transformed value.

According to the motion noise determination and removal method using the multi-signal reception according to the present invention, it is possible to effectively determine whether motion noise is included in the PPG signal, which is one of biological signals.

1 is a signal graph showing a PPG signal detected through a PPG sensor.
2 is a signal graph showing a PPG signal including high frequency noise and dynamic noise.
3 is a graph showing time-synchronized signals and time-synchronized signals according to the present invention.
4 is a diagram illustrating a process of wavelet-transforming and decomposing an original signal according to the present invention.
FIG. 5 is a flowchart illustrating a method of determining and removing whether a PPG signal includes motion artifacts according to the present invention.
Fig. 6 is a flowchart showing the step of determining the number of wavelet transforms according to the present invention.
7 is a flow chart showing steps of a method for determining motion noise according to the present invention.
8 is a graph showing the result of the determination of the dynamic noise according to the present invention.
FIG. 9 is a graph showing the results of dynamic noise removal according to the present invention.

It is noted that the technical terms used in the present invention are used only to describe specific embodiments and are not intended to limit the present invention. In addition, the technical terms used in the present invention should be construed in a sense generally understood by a person having ordinary skill in the art to which the present invention belongs, unless otherwise defined in the present invention, Should not be construed to mean, or be interpreted in an excessively reduced sense.

In addition, when a technical term used in the present invention is an erroneous technical term that does not accurately express the concept of the present invention, it should be understood that technical terms can be understood by those skilled in the art. In addition, the general terms used in the present invention should be interpreted according to a predefined or prior context, and should not be construed as being excessively reduced.

Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings, wherein like reference numerals refer to like or similar elements throughout the several views, and redundant description thereof will be omitted.

In the following description, well-known functions or constructions are not described in detail since they would obscure the invention in unnecessary detail. It is to be noted that the accompanying drawings are only for the purpose of facilitating understanding of the present invention, and should not be construed as limiting the scope of the present invention with reference to the accompanying drawings.

1 is a signal graph showing a PPG signal detected through a PPG sensor. Generally, the PPG signal period is used to detect the heart rate and the PPI (Peak to Peak Interval) is used to detect the period, which means the time interval between the maximum value of one periodic signal and the maximum value of the next periodic signal . Thus, the PPI of the PPG signal can be used to determine the heart rate.

Generally, a person has about 70 heartbeats per minute, but heart rate can be changed by physical activity or external stimuli. When the body is tensed due to external stimuli, the heart rate increases and the PPI becomes smaller. In the relaxed state, the heart rate decreases and the PPI increases. Such a change in the PPI can be used to determine the psychological state of a measurer or to determine the presence or absence of a disease.

2 is a signal graph showing a PPG signal including high frequency noise and dynamic noise. When high-frequency noise is included in the PPG signal, distortion of the signal occurs as shown in Fig. 2 (a). High-frequency noise does not make a big difference in the signal itself, but it should be removed as it is an obstacle to detection of PPI. A typical PPG signal is activated in the frequency range of 0 to 4 Hz, but high frequency noise is activated in a larger frequency band and can be removed using a low pass filter.

When the PPG signal includes motion noise, the distortion of the signal can not be detected as shown in Fig. 2 (b). Such motion artifacts occur in various frequency bands and can not be removed by simple filters because they affect the frequency range of 0-4 Hz where the PPG signal is activated. Since the motion noise is a serious obstacle when detecting a PPI, that is, a period of a signal, a signal processing technique for solving motion noise is required.

3 is a graph showing time-synchronized signals and time-synchronized signals according to the present invention. As shown in FIG. 3 (a), there is a difference in measurement time and size due to the difference in distance between the measurement position and the heart of the multiple measured signals before the motion noise is removed using the signals received from the multiple channels . Fig. 3 (b) shows two signals that are not time synchronized, and Fig. 3 (c) shows two signals that are time synchronized.

Since the difference in the measurement time is represented by the phase difference in the process of replacing the signal including the motion noise with the normal signal, it is preferable to correct the time difference between the respective channels for effective signal processing. Also, it is desirable to correct the magnitude of the measured signal in order to adopt the optimized signal by analyzing multiple signals of the same time in the motion noise removal process.

In order to correct the magnitude of the measured signal, measure the magnitude of the reference signal of the measured signal at each position and set it to have the same magnitude as the signal having the largest magnitude. In order to correct the time difference of multiple received signals, the time difference of the two signals is calculated by applying the cross-correlation technique and the signal is shifted by the calculated time difference to correct the time difference.

Any two signals are assumed to illustrate how to calibrate. The distance from the first receiver to the heart is different from the distance from the second receiver to the heart. Therefore, a time difference due to the distance difference occurs. In the process of replacing the noise-generated signal with the normal signal, the phase difference does not coincide due to the time difference between the two signals. Therefore, in order to solve this problem, the time difference between the two signals must be corrected. The time difference between the two signals is calculated using Equation 1 below using the cross-correlation technique.

...수학식 1

... Equation 1

은 첫 번째 측정부위의 신호 x와 두 번째 측정부위의 신호 y를 상호상관기법을 적용한 결과이다. 신호 x와 신호 y가 N의 길이를 가진 데이터라고 할 때

는 -(N-1)에서 N-1의 길이를 가진 데이터가 된다.

의 m번째 데이터는 부호에 따라 달리 계산된다. m이 양수일 경우

와 같이 계산되며 m이 음수일 경우에는 m이 양수일 경우의 데이터가 시간이 반전된 형태이다.here

Is the result of applying the cross-correlation technique between the signal x of the first measurement site and the signal y of the second measurement site. Assuming that the signal x and the signal y are data having a length of N

Becomes data having a length of N-1 in - (N-1).

M < / RTI > of data is calculated differently depending on the sign. If m is positive

If m is a negative number, the data in the case where m is a positive number is inverted in time.

4 is a diagram illustrating a process of wavelet-transforming and decomposing an original signal according to the present invention. In the discrete wavelet transform, the original signal S can be represented by a sum of a signal that has passed through the low-pass filter and a signal that has passed through the high-pass filter. The original signal passes through the low-pass filter and the high-pass filter and then the approximation coefficients A [n] are output from the high-pass filter and the detail coefficients D [n] from the low-pass filter. Each output has a frequency band that is half the original signal. The original signal is the sum of the divided detailed coefficients and the approximate coefficients and can be expressed by the following equation.

S [n] = A [n] + D [n]

Each time the wavelet transform is performed, the frequency band is divided into two bands, the low frequency band component A [n] and the high frequency band component D [n].

When the signal A [n] that has passed through the low-pass filter is subjected to wavelet transformation in such a manner, signals are separated in the form as shown in FIG. 3, and the signal after the sixth wavelet transform can be expressed by the following equation.

S = D1 + D2 + D3 + D4 + D5 + D6 + A6

FIG. 5 is a flowchart illustrating a method of determining whether a motion noise is included in a PPG signal according to an embodiment of the present invention and removing the motion noise. As shown in FIG. 5, a method of determining whether or not a PPG signal according to the present invention includes motion noise and removing motion noise includes measuring a PPG signal through a plurality of channels (S110) A step S130 of removing a noise, a step S130 of setting a reference signal, and a step S230 of applying a cross-correlation technique to remove motion noise in the multi-channel PPG signal based on the set reference signal, A step S160 of correcting the difference and a step S140 of selecting a similar base signal, a step S160 of performing wavelet transformation using a base signal, and a step S160 of determining and removing motion noise (S170).

First, the user measures the PPG signal without moving for a predetermined period of time in a stable state (S110). In the embodiments of the present invention, signals are measured at the hand and foot corresponding to the body end, but the present invention is not limited thereto. In addition, the signal is measured at two or more places on both hands and feet in order to effectively remove the noise included in the signal.

If the user has a disease that affects blood flow changes such as hand and foot coldness in determining the body terminal site to measure the signal, it is preferable to measure the signal by adopting the site with the smallest disease influence.

To set the correct reference signal, the user measures the signal without movement for a certain period of time in the stable state at the beginning of the signal measurement. In this case, since the baseband signal to be used in the subsequent wavelet transform is determined using the measured signal, it is measured in an environment where the maximum noise is not included.

There is a high frequency band signal noise due to mechanical and physical characteristics in the PPG signal measured in the PPG signal measuring step (S110) even if the noise is not included in the measurement. Since the signal noise of this high frequency band is an obstacle to the detection of the period of the PPG signal, the high frequency noise should be removed using a low pass filter. Since wavelet transform is performed using FWT (Fast Wavelet Transform) based on a finite impulse response (FIR) filter, wavelet transform is used to remove high frequency noise using an appropriate FIR filter.

Various filters such as a moving average filter, a Bartlett filter, a Hanning filter, and a Hamming filter can be used as the FIR filter. In the present invention, the Hamming filter having the largest minimum band-stop attenuation is used, but the present invention is not limited thereto. In the embodiment of the present invention, the cutoff frequency is set to 6 Hz, but the present invention is not limited thereto.

Next, a reference signal is set (S130) in order to set a base signal to be used in wavelet transformation in a signal in which high frequency noises are removed. The reference signal shall be measured by the user in a stable state and shall have no motion during the measurement. Even if a signal is measured under these conditions, a signal smaller than the size of a user's general pulse wave is initially detected at the characteristic of the sensor, and amplitude and size are not uniform. Therefore, since the signal at the initial stage of measurement can not be used as a reference signal, a reference signal is set using a normal signal after a predetermined time elapses.

In this case, since the period of the general PPG signal has a period of 0.6 to 1.2 seconds, the first period after 3 seconds corresponding to about 3 periods can be set as the reference signal. PPG signals have different characteristics and shapes for different people and have different shapes depending on the region to be measured. It is easy to judge the motion noise by selecting the reference signal reflecting the pulse wave characteristics of each user and determining the wavelet transform base signal based on the reference signal.

In the step of S110, there are differences in the measurement time and the size due to the difference in the distance between the measurement position of each signal and the heart. Since the difference of the measurement time is represented by a phase difference in the process of replacing the signal including the motion noise with the normal signal, the time and size difference between the respective channels are corrected in order to perform effective signal processing (S140).

In order to correct the time difference of multiple received signals, the time difference of the two signals is calculated by applying the cross-correlation technique and the signal is shifted by the calculated time difference to correct the time difference.

In addition, the size of the measured signal is also corrected in order to adopt the optimized signal by analyzing multiple signals of the same time in the process of determining and eliminating motion noise. In order to correct the magnitude of the measured signal, measure the magnitude of the reference signal of the signal measured at each position and set it to have the same magnitude as the signal having the largest magnitude.

Next, a similar base signal to be used for wavelet transform is selected using the reference signal of the user set in step S130 (S150). At this time, the base signal adopts the most similar function among the base functions by using the reference signal of the user, the basis function and the cross-correlation technique. There are several base functions used for wavelet transform. Of these, there are scaling functions and FIR filter can be applied to fit FWT. The basic functions 'Harr (harr)', 'Daubechies (dbN)', , 'Symbols (symN)', 'Coiflets (coifN)', 'Biothogonal wavelets (biorN)', 'Reverse biothogonal wavelets (rbioN)' and 'Discrete approximation of Meyer wavelet The reference signals are compared using a cross-correlation technique.

The base function closest to the reference signal among the above-mentioned basis functions compared is set as the number of wavelet transform sub-frames to be used later. In selecting the baseline function most similar to the reference signal, the maximum value of each cross-correlation technique is compared by applying the cross-correlation technique to the basis function and the reference signal, and the largest value is selected as the base signal. In order to quantify the similarity, the cross-correlation function of matlab is used to find the result of the cross-correlation method, and the maximum value is found by using the max function, and the maximum value among the values corresponding to the maximum values of the respective base signals is obtained The base signal is determined in a manner that measures again.

Next, the wavelet transform (S160) is performed using the basis function set in S150. A [n] is a low frequency band, and D [n] is a high frequency band. In this case, the signal is divided into an approximation (A [n]) part and detail (D [n]) part. At this time, the wavelet transform is repeated so that A [n] has a frequency band of 0 to 4 Hz. The reason for this repetition is that the normal heartbeat is less than 4 Hz, and the conversion step is set to include an interval of less than 4 Hz in order to easily detect one period. In general, the sampling frequency is used in the conversion step determination and the wavelet conversion frequency determination method will be described with reference to FIG.

Fig. 6 is a flowchart showing the step of determining the number of wavelet transforms according to the present invention. Fig. 6 is a flowchart showing the step of determining the number of wavelet transforms according to the present invention. The initial value of a is 1 (S161), and it is determined in step S162 whether fs? 2 ^a-1 is satisfied. If it is not satisfied, 1 is added to the value of a (S163), and the step of S162 is repeated.

fs is the number of times the smallest a-3 that satisfies the sampling frequency fs < = 2 ^{a-1 is} to be repeated. A [n] is wavelet-transformed by the number of times of the smallest a-3 that satisfies fs?? 2 ^a-1 (S164). Frequency domain and low-frequency domain by the wavelet transform, it is divided into A [n] and D [n] by a relative standard.

In the embodiment of the present invention, the wavelet transform is performed on A [n], but the present invention is not limited thereto. The values of A [n] and D [n] may vary depending on which part is transformed, It is also possible to divide A [n] by setting D [n] as a conversion target as well as continuously dividing it.

Specifically, in order to obtain a desired band, it is possible to determine which signal A [n] and D [n] is selected and divided at the time of one-time division. For example, if you want to obtain a signal in the (32-48) frequency band, you can use (0-128), (128-256), (0-64), (64-128) (128- 256) (A [n] conversion), three conversions (0-32), (32-64), (64-128) Since the frequency is divided into (0-32), (32-48), (48-64), (64-128), (128-256) . In this manner, it is possible to select which signal is selected and divided each time the wavelet transform is performed once, and the signal is divided into A [n] and D [n] each time the wavelet transform is performed.

Since the sampling frequency of the signal used in the embodiment of the present invention is 256 Hz, it includes the section where A [n] converted from the sixth order is 0 to 4 Hz. Table 1 shows the frequency distribution of a signal obtained by wavelet-transforming the original signal by the method shown in Fig.

n One 2 3 4 5 6 A [n] 0-128 0-64 0-32 0-16 0-8 0-4 D [n] 128-256 64-128 32-64 16-32 8-16 4-8

The sum of D [1] + D [2] + D [3] + D [4] + D [5] + D [6] + A [6] as in Equation Frequency signal.

Next, a period of the user's pulse wave is detected using a period in which the heartbeat is activated, and whether the motion noise is included in the detected signal is detected using the period, and then removed (S170). A method of determining whether to include the motion noise is shown in FIG.

7 is a flow chart showing steps of a method for determining motion noise according to the present invention. When detecting a period using a heartbeat activated interval, the signal period of the signal is detected using the PPI of the signal (S171).

If there is no dynamic noise, PPI appears at regular intervals, but if dynamic noise is included, the PPI changes. The normal heartbeat is about 0.8 seconds, and the period in which the motion noise occurs is rapidly changed from 0.3 to 1.6 seconds. Therefore, it is determined whether the period is less than 0.3 seconds (S172), and if it is small, it is determined that motion noise is included (S176). In step S172, the period of the determination reference is set to 0.3 seconds, but it may be set differently depending on the situation.

In determining the motion noise, a frequency band that is affected by heartbeat but whose degree is smaller than other frequency bands and whose motion noise is more affected than heartbeat is used. The portion corresponding to this frequency band corresponds to about 32 to 64 Hz. The heartbeat period is about 0.8 seconds on average, and if the component value of this frequency band is large, it is judged that the signal contains motion noise.

In the determination of the dynamic noise, the power of the signal of each period and the signal of the frequency band of 32 to 64 Hz of the reference signal are summed (S173), and it is determined whether the signal of each period is more than a certain multiple of the reference signal (S174) If it is more than a predetermined multiple, it is determined that the motion noise is included in the period (S176). If the period is less than the certain period, it is determined that the motion signal is a normal signal (S175). In the process of determining whether the reference signal is more than a certain multiple of the reference signal, the multiple may be set to 1.4 to 1.6, preferably 1.5.

If it is determined in step S176 that the multiple received signals contain motion noise, the motion artifact is removed using the selective combining technique. The selective combining method is a method of outputting an ideal signal by outputting the best signal for each section among signals obtained from each channel.

The present invention relates to a method and apparatus for determining a dynamic noise for each period of a plurality of received signals and outputting a signal having the largest signal among normal signals when the normal signal including no motion noise is more than one signal, In case of signal, it outputs the signal with the smallest influence of motion noise among all signals.

The SNS of each signal is calculated using Equation (4) to compare the degree of influence of the dynamic noise of each cycle. The SNS is calculated using the signal of the frequency band (32-64 Hz) used to determine whether or not the motion noise is included. The signal whose SNS value is closest to 1 is the signal with the smallest influence of motion noise. SNS and selective combining techniques can be used to effectively remove motion noise.

SNS = power of signal and noise / power of reference signal.

8 is a graph showing the result of the determination of the dynamic noise according to the present invention. The PPG signal generating the motion noise can be confirmed in three short PPIs in FIG. 8 (a). After the reference signal is set and the basis function is set, the wavelet transform is performed so that the A [n] section in the range of 0 to 4 Hz and the D [n] section in the frequency range of 32 to 64 Hz appear, ] Period is equal to or more than 1.5 times the power of the corresponding section of the reference signal is determined as the period in which the dynamic noise occurs.

The amplitude of the reference signal may not be sufficiently large because the reference signal is set at the initial signal of the signal measurement. Even if the user does not move, there may be a change of heartbeat due to psychological reasons such as change of emotions or tension. It is preferable to set it to 1.5 times.

As a result of the actual experiment, when the dynamic noise is judged based on 1.2 times, the normal signal is judged to be the motion noise as well as the motion noise, and when it is judged based on 1.7 times, the motion noise can not be normally discriminated Therefore, the experiment was conducted based on the most appropriate 1.5 times.

FIG. 8 (b) shows the result of adding the powers of 0 to 4 Hz, 8 (c) of 32 to 64 Hz, and 8 (d) of 32 to 64 Hz of each cycle in order from the beginning of one cycle to the next cycle. As shown in Fig. 8 (c), in the case of a normal pulse wave, the power of the D [n] portion appears to be very small, and a large value is shown when motion noise is included.

8 (b), the signal magnitude is calculated for each period as shown in FIG. 8 (d), and the signal magnitude is compared with the reference signal magnitude to determine the degree of inclusion of the motion noise (SNS) . 8 (e), when it is determined that there is actually a motion noise, 0 is output during the corresponding period, and when it is determined that motion noise is not included, 1 is output during the corresponding period. It can be confirmed that it is accurately judged. The SNS is calculated for each channel, and the signals are compared with each other to determine whether or not the dynamic noise is included in each cycle. The result of adopting a signal containing less dynamic noise for each cycle is shown in FIG.

FIG. 9 is a graph showing the results of dynamic noise removal according to the present invention. In Case 1 and Case 2 in FIG. 9, there is a case where motion noise occurs in one signal. In Case 3, both signals have no motion noise. In Case 4, both signals have no motion noise. If a signal contains dynamic noise, it outputs a period that does not contain dynamic noise. If both signals do not contain dynamic noise, the signal of the larger signal size is included in both signals. The SNS is calculated and a signal with a small influence of motion noise is output from the two signals.

FIG. 9 shows that a signal including motion noise for 20% time and a motion noise for channel 2 for 15% time is determined by using wavelet transform, and channel 1 is 19.4% Of the time, and 14.9% of the time motion noise was included in the time. As a result, it can be confirmed that the motion noise ratio is improved to 4.29% in the entire signal.

As shown in Table 2, the influence of the noise on the signal is reduced.

Channel 1 Channel 2 Selected signal Noise (%) Detected noise (%) Noise (%) Detected noise (%) Noise (%) A Hand 20 19.4 Hand 15 14.9 4.29 78.55 Hand 20 19.12 Hand 10 11.2 7.33 63.35 Hand 30 29.78 Hand 30 29.77 5.18 82.73 Hand 20 18.35 Foot 15 14.69 2.89 85.55 B Hand 20 20.39 Hand 15 14.44 5.18 74.10 Hand 20 20.33 Hand 10 9.54 5.34 73.30 Hand 30 30.61 Hand 30 29.34 4.59 84.70 Hand 20 19.65 Foot 15 16.12 3.78 81.10 C Hand 20 19.87 Hand 15 16.31 7.52 62.40 Hand 20 18.63 Hand 10 11.26 4.26 78.70 Hand 30 28.65 Hand 30 31.09 9.43 68.57 Hand 20 20.83 Foot 15 14.69 6.25 68.75 D Hand 20 21.13 Hand 15 15.68 6.24 68.80 Hand 20 20.96 Hand 10 9.86 6.69 66.55 Hand 30 30.42 Hand 30 28.96 8.37 72.10 Hand 20 19.58 Foot 15 14.76 4.75 76.25

The features, structures, effects and the like described in the foregoing embodiments are included in at least one embodiment of the present invention and are not necessarily limited to one embodiment. Further, the features, structures, effects, and the like illustrated in the embodiments may be combined or modified in other embodiments by those skilled in the art to which the embodiments belong.

Therefore, it should be understood that the present invention is not limited to these combinations and modifications. While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is clearly understood that the same is by way of illustration and example only and is not to be construed as limiting the scope of the present invention. It can be seen that various modifications and applications are possible. For example, each component specifically shown in the embodiments may be modified and implemented. It is to be understood that the present invention may be embodied in many other specific forms without departing from the spirit or essential characteristics thereof.

Claims (6)

Measuring a PPG signal through multiple channels;
Generating a signal from which the high frequency noise is removed from the measured PPG signal;
Setting a reference signal in the signal from which the high-frequency noise is removed;
Correcting a difference between the multiplexed signals by applying a cross-correlation technique to remove motion noise from the multi-channel PPG signal based on the reference signal;
Selecting a base signal based on the set reference signal;
Performing wavelet transform using the base signal; And
And determining and removing the motion noise using the wavelet-transformed values.
The method according to claim 1,
And a low-pass filter is used in the step of generating the signal from which the high-frequency noise is removed.
The method according to claim 1,
Wherein the step of judging the dynamic noise includes a step of judging whether or not a sum of powers of a predetermined period of the frequency band is equal to or greater than a predetermined multiple of the reference signal. .
The method according to claim 1,
Wherein the step of correcting the difference between the signals corrects at least one of time difference and magnitude of the PPG signal measured through the multiple channels.
The method according to claim 1,
Wherein the base signal is selected by applying a cross-correlation technique to a plurality of base functions and a reference signal capable of performing discrete wavelet transform and comparing the maximum value of each cross-correlation technique, and selecting the largest value as a base signal A method for determining and eliminating motion noise using multiple signal reception.
The method according to claim 1,
Wherein the wavelet transform using the base signal repeats the wavelet transform so that the frequency of the low frequency band has a frequency band of 0 to 4 Hz.

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