Physiological signal motion interference suppression method based on human intelligence and electronic equipment

Abstract

The invention provides a physiological signal motion interference suppression method and device based on human intelligence, electronic equipment and a storage medium, and the method comprises the steps: obtaining a first physiological signal and a motion signal, the first physiological signal being used for representing the physiological signal subjected to motion interference; screening the motion signals based on the correlation between the first physiological signals and the motion signals; an adaptive filtering algorithm is used for conducting adaptive filtering on the screened motion signals and the first physiological signals, second physiological signals are obtained, and the second physiological signals are used for representing the physiological signals after motion interference is restrained. According to the method provided by the invention, the motion interference in the physiological signal can be effectively inhibited, and the relatively accurate original physiological signal can be recovered.

Description

Technical Field

[0001] The present application relates to the field of signal processing technology, and in particular to a method, device, electronic device and storage medium for suppressing physiological signal motion interference based on human factors intelligence. Background Art

[0002] When using smart wearable devices to collect physiological signals, it is very easy to be disturbed by motion, which causes the collected physiological signals to be superimposed with motion interference noise, making it impossible to obtain accurate original physiological signals, resulting in large deviations in the subsequent use of physiological signal analysis to obtain other features.

[0003] The signal motion interference suppression method used in the related art is to input the collected motion signal into an adaptive filter to suppress motion interference. However, the effect of suppressing motion interference is not good, and the goal achieved by this method is to restore a certain feature of the physiological signal, rather than the original physiological signal itself, so other features cannot be obtained using the physiological signal. Taking the photoplethysmography (PPG) signal as an example, the signal motion interference suppression method used in the related art aims to restore a certain feature of the PPG signal (such as heart rate), rather than the original PPG signal itself, so other features (such as HRV, Heart Rate Variability, heart rate variability) cannot be obtained using the PPG signal. Summary of the invention

[0004] The present application provides a method, device, electronic device and storage medium for suppressing motion interference in physiological signals based on human factors intelligence, which can effectively suppress motion interference in physiological signals and restore relatively accurate original physiological signals.

[0005] In a first aspect, the present application provides a method for suppressing physiological signal motion interference based on human intelligence, comprising: Acquiring a first physiological signal and a motion signal, wherein the first physiological signal is used to characterize the physiological signal disturbed by motion; screening the motion signal based on the correlation between the first physiological signal and the motion signal; The filtered motion signal and the first physiological signal are adaptively filtered using an adaptive filtering algorithm to obtain a second physiological signal, which is used to characterize the physiological signal after the motion interference is suppressed.

[0006] In one possible implementation manner, the physiological signal is at least any one of a PPG signal, an electroencephalogram signal, an electrocardiogram signal, and an electromyography signal.

[0007] In one possible implementation manner, the physiological signal is a PPG signal, the first physiological signal is a PPG1 signal, and the second physiological signal is a PPG2 signal.

[0008] In one possible implementation, The motion signal includes M channel signals, and the motion signal is screened based on the correlation between the first physiological signal and the motion signal, including: Calculate the Pearson correlation coefficients of the M channel signals and the PPG1 signal respectively; Among the M channel signals, the channel signals corresponding to the absolute values ​​of the Pearson correlation coefficients that are less than a preset threshold are eliminated to obtain N channel signals, where N≤M.

[0009] In one possible implementation, adaptive filtering is performed on the filtered motion signal and the first physiological signal using an adaptive filtering algorithm to obtain a second physiological signal, including: Among the N channel signals, the channel signals corresponding to the Pearson correlation coefficient being greater than 0 are classified as positive group signals, and the channel signals corresponding to the Pearson correlation coefficient being less than 0 are classified as negative group signals; According to the grouping result, the grouped motion signal and PPG1 signal are adaptively filtered using an adaptive filtering algorithm to obtain a PPG2 signal.

[0010] In one possible implementation, the adaptive filtering algorithm includes a least mean square adaptive filtering algorithm. According to the grouping result, the adaptive filtering algorithm is used to adaptively filter the grouped motion signal and the PPG1 signal to obtain the PPG2 signal, including: If the N channel signals are all divided into positive group signals or negative group signals, then the N channel signals are summed to obtain a first reference signal; Inputting the first reference signal and the PPG1 signal into a minimum mean square adaptive filter for adaptive filtering to obtain a PPG3 signal, where the PPG3 signal is used to characterize the PPG signal after adaptive filtering; The PPG3 signal is phase-aligned according to the PPG1 signal to obtain the PPG2 signal.

[0011] In one possible implementation, phase alignment of the PPG3 signal is performed according to the PPG1 signal, including: Perform convolution calculation on PPG1 signal and PPG3 signal; The PPG3 signal is time-shifted according to the target phase offset corresponding to the maximum value in the convolution calculation result.

[0012] In one possible implementation, the adaptive filtering algorithm includes a least mean square adaptive filtering algorithm. According to the grouping result, the adaptive filtering algorithm is used to adaptively filter the grouped motion signal and the PPG1 signal to obtain the PPG2 signal, including: If the N channel signals are divided into a positive group signal and a negative group signal, the positive group signal and the negative group signal are sorted into signal groups to obtain a first group signal and a second group signal, and the absolute value of the sum of the Pearson correlation coefficients corresponding to the first group signal is greater than the absolute value of the sum of the Pearson correlation coefficients corresponding to the second group signal; summing the channel signals in the first group of signals to obtain a second reference signal; Inputting the second reference signal and the PPG1 signal into a minimum mean square adaptive filter for a first adaptive filtering to obtain a PPG4 signal, where the PPG4 signal is used to characterize the PPG signal after the first adaptive filtering; Phase-align the PPG4 signal according to the PPG1 signal to obtain the PPG5 signal; summing the channel signals in the second group of signals to obtain a third reference signal; Inputting the third reference signal and the PPG5 signal into a minimum mean square adaptive filter for a second adaptive filtering to obtain a PPG6 signal, where the PPG6 signal is used to characterize the PPG signal after the second adaptive filtering; The PPG6 signal is phase-aligned according to the PPG1 signal to obtain the PPG2 signal.

[0013] In one possible implementation manner, before screening the motion signal based on the correlation between the first physiological signal and the motion signal, the method further includes: The PPG1 signal and the motion signal are filtered.

[0014] In a second aspect, the present application provides a physiological signal motion interference suppression device based on human factors intelligence, comprising: An acquisition module, used to acquire a first physiological signal and a motion signal, wherein the first physiological signal is used to characterize a physiological signal disturbed by motion; A screening module, configured to screen the motion signal based on the correlation between the first physiological signal and the motion signal; The adaptive filtering module is used to adaptively filter the filtered motion signal and the first physiological signal using an adaptive filtering algorithm to obtain a second physiological signal, and the second physiological signal is used to characterize the physiological signal after suppressing motion interference.

[0015] In a third aspect, the present application provides an electronic device comprising: a processor and a memory, the memory being used to store a computer program; the processor being used to run the computer program to implement the method for suppressing physiological signal motion interference based on human factors intelligence as in the first aspect.

[0016] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer-readable storage medium is executed on a computer, the computer implements the method for suppressing physiological signal motion interference based on human factors intelligence as in the first aspect.

[0017] Compared with the prior art, this application has at least the following technical effects: The present application provides a method, device, electronic device and storage medium for suppressing motion interference of physiological signals based on human factors intelligence. The method analyzes the correlation between physiological signals affected by motion interference and motion signals, that is, analyzes the degree of influence of motion signals on physiological signals, screens motion signals, removes motion signals with less influence on physiological signals, and then uses an adaptive filtering algorithm to adaptively filter the screened motion signals and physiological signals affected by motion interference to obtain physiological signals with suppressed motion interference. This method can avoid adding motion signals with less influence on physiological signals to adaptive filtering, which generates new noise, resulting in the inability to effectively suppress motion interference in physiological signals and the inability to restore relatively accurate original physiological signals. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A result diagram of a signal motion interference suppression method in the related art; Figure 2 An application scenario architecture diagram provided for an embodiment of the present application; Figure 3 A schematic diagram of a flow chart of a method for suppressing physiological signal motion interference based on human factors intelligence provided in an embodiment of the present application; Figure 4 It is the structural diagram of LMS adaptive filter; FIG. 5( a ) and FIG. 5( b ) are result diagrams of a method for suppressing physiological signal motion interference based on human intelligence provided in an embodiment of the present application; Figure 6 A schematic diagram of the structure of a physiological signal motion interference suppression device based on human factors intelligence provided in an embodiment of the present application; Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] In the embodiments of the present application, unless otherwise specified, the character " / " indicates that the objects before and after the association are in an or relationship. For example, A / B can represent A or B. "And / or" describes the association relationship of the associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone.

[0020] It should be pointed out that the words "first", "second", etc. involved in the embodiments of the present application are only used to distinguish the description purpose, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated, nor can they be understood as indicating or implying order.

[0021] In the embodiments of the present application, "at least one" means one or more, and "more" means two or more. In addition, "at least one of the following" or similar expressions refers to any combination of these items, which may include any combination of single items or plural items. For example, at least one of A, B, or C can represent: A, B, C, A and B, A and C, B and C, or A, B and C. Among them, each of A, B, and C can be an element itself, or a set containing one or more elements.

[0022] In the embodiments of the present application, "exemplary", "in some embodiments", "in another embodiment", etc. are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" in the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present concepts in a concrete way.

[0023] In the embodiments of the present application, "of", "corresponding", and "corresponding" can sometimes be used interchangeably. It should be noted that when the distinction between them is not emphasized, the meanings to be expressed are consistent. In the embodiments of the present application, communication and transmission can sometimes be used interchangeably. It should be noted that when the distinction between them is not emphasized, the meanings to be expressed are consistent. For example, transmission can include sending and / or receiving, which can be a noun or a verb.

[0024] The equal to involved in the embodiments of the present application can be used in conjunction with greater than, and is applicable to the technical solution adopted when greater than, and can also be used in conjunction with less than, and is applicable to the technical solution adopted when less than. It should be noted that when equal to is used in conjunction with greater than, it cannot be used in conjunction with less than; when equal to is used in conjunction with less than, it cannot be used in conjunction with greater than.

[0025] When using smart wearable devices to collect physiological signals, it is very easy to be disturbed by motion, which causes the collected physiological signals to be superimposed with motion interference noise, making it impossible to obtain accurate original physiological signals, resulting in large deviations in the subsequent use of physiological signal analysis to obtain other features.

[0026] Most of the signal motion interference suppression methods used in related technologies use a three-axis gyroscope to record motion, and input the collected motion signal into an adaptive filter to suppress motion interference. However, this method has limited research on the input noise method. The commonly used input method is to average the three-axis gyroscope signal and input it or cascade three adaptive filters, with each stage inputting a signal of one dimension in the three-axis gyroscope signal. However, not all movements will affect the signal, and filtering in this way will introduce new noise. And the goal achieved by this method is to restore a certain feature of the physiological signal, not the original physiological signal itself, so other features cannot be obtained using the physiological signal.

[0027] like Figure 1 As shown, Figure 1 This is a result diagram of the signal motion interference suppression method of the related technology, taking the physiological signal as a PPG signal as an example, Figure 1 The first line is the PPG signal without motion interference, the second line is the PPG signal containing motion interference, the third line is the result of cascade adaptive filtering using the gyroscope signals of the two channels, and the fourth line is the gyroscope signals of the two channels. Figure 1 From the part marked in the middle, we can see that the gyroscope signal in the 4th row has large fluctuations due to motion, but has little effect on the PPG signal in the 2nd row containing motion interference. The PPG signal after adaptive filtering has obvious distortion, that is, the deviation between the PPG signal after adaptive filtering and the original PPG signal (PPG signal in the 1st row) increases.

[0028] In addition, the PPG signal can reflect certain specific states of a person, and the HRV characteristics based on the waveform (RR interval) play an important role in reflecting the state of a person. The signal motion interference suppression method used in related technologies usually aims to restore a certain feature of the original signal, such as suppressing motion interference on the PPG signal to obtain an accurate heart rate, rather than obtaining the signal itself after suppressing motion interference. Therefore, it is impossible to use the PPG signal to obtain other features (such as HRV).

[0029] Based on the above problems, an embodiment of the present application proposes a method for suppressing motion interference in physiological signals based on human factors intelligence, which can effectively suppress motion interference in physiological signals and restore relatively accurate original physiological signals.

[0030] Now combined Figure 2- Figure 5 illustrates the method for suppressing physiological signal motion interference based on human factors intelligence provided in an embodiment of the present application.

[0031] Figure 2 This is an application scenario architecture diagram provided by the embodiment of this application. Figure 2 As shown, the above application scenarios include physiological signal acquisition equipment, motion signal acquisition equipment and electronic equipment. The physiological signal acquisition equipment can be a PPG signal acquisition equipment (such as a PPG sensor) for collecting the PPG signal of the subject; it can also be an EEG (Electroencephalogram) electroencephalogram signal acquisition equipment, an ECG (Electrocardiogram) electrocardiogram signal acquisition equipment, an EMG (Electromyogram) electromyography signal acquisition equipment, etc.; the motion signal acquisition equipment can be a gyroscope sensor, an accelerometer, an inertial measurement unit, etc., which is used to detect and quantify the movement of the subject's body, such as rotation, tilt, etc., to obtain motion signals. The physiological signal acquisition equipment and the motion signal acquisition equipment transmit the collected signals to the electronic equipment by wireless or wired means, and the electronic equipment implements the physiological signal motion interference suppression method based on human intelligence provided in the embodiment of the present application. It should be noted that the embodiment of the present application does not limit the type of electronic equipment, and the electronic equipment can be a desktop computer, a tablet computer, a laptop computer, etc.

[0032] Figure 3 The flowchart of the method for suppressing physiological signal motion interference based on human intelligence provided in the embodiment of the present application specifically includes the following steps: Step S31, acquiring a first physiological signal and a motion signal.

[0033] Specifically, a physiological signal acquisition device (which may be a portable bio-information acquisition device) is used to acquire the physiological signal of the subject to obtain a first physiological signal, wherein the first physiological signal is used to characterize the physiological signal disturbed by motion, and the first physiological signal includes a real physiological signal and a physiological signal corresponding to a motion artifact. The implementation goal of the embodiment of the present application is to remove the physiological signal corresponding to the motion artifact and obtain a real physiological signal, that is, to restore the original physiological signal. A motion signal acquisition device (which may be a portable bio-information acquisition device) is used to acquire the motion signal of the subject, such as using a sensor combining a three-axis gyroscope and a three-axis accelerometer to acquire information on the subject's moving parts (such as arms, elbows, etc.), and obtain motion signals of 6 channels, which are respectively used express.

[0034] Optionally, the physiological signal is any one of a PPG signal, an electroencephalogram signal, an electrocardiogram signal, an electromyography signal, an electrooculogram signal, a blood oxygen saturation, a respiratory signal, blood pressure, etc.

[0035] The following embodiments take the physiological signal as a PPG signal, the first physiological signal as a PPG1 signal, and the second physiological signal as a PPG2 signal as an example to illustrate the physiological signal motion interference suppression method based on human factor intelligence of the present application. It is understandable that other physiological signals such as EEG signals, ECG signals, and EMG signals are also applicable to the method provided in the present application.

[0036] Step S32: screening the motion signal based on the correlation between the first physiological signal and the motion signal.

[0037] Specifically, the motion signal includes M channel signals, and the Pearson correlation coefficients of the M channel signals and the PPG1 signal are calculated respectively; among the M channel signals, the channel signals corresponding to the absolute values ​​of the Pearson correlation coefficients less than a preset threshold are eliminated to obtain N channel signals, where N≤M.

[0038] The present application performs Person correlation analysis on the motion signal and the PPG1 signal to obtain the Pearson correlation coefficient, which can effectively measure the correlation between the motion signal and the PPG signal containing motion interference, and quantify the influence of the motion signal on the PPG1 signal.

[0039] Taking the motion signals of the above 6 channels as an example, the Pearson correlation coefficients of the 6 channels and the PPG1 signal are calculated respectively to obtain the correlation coefficients The value range of the correlation coefficient is between -1 and 1, where 1 indicates a completely positive correlation, -1 indicates a completely negative correlation, and 0 indicates no linear correlation. If the absolute value of the correlation coefficient of a channel signal in the motion signal is close to 1, it means that there is a strong correlation between the channel signal and the PPG1 signal, indicating that the channel signal may be the main source of motion artifacts in the PPG signal and has the most obvious interference with the PPG signal.

[0040] Next, among the 6 channel signals, the channel signals corresponding to the absolute values ​​of the Pearson correlation coefficients less than the preset threshold are eliminated, and the preset threshold value range is [0,1]. For example, the channel signals whose absolute values ​​of the Pearson correlation coefficients are less than 0.1 are eliminated. The purpose of this processing process is to screen the 6 channel signals, eliminate the channel signals that have little impact on the PPG1 signal, and select the channel signals that have a greater impact on PPG1, so as to avoid adding motion signals that have little impact on the PPG signal to the adaptive filter, generating new noise, causing the PPG signal after the adaptive filter to have obvious distortion, affecting the motion interference suppression effect.

[0041] It should be noted that in addition to the Pearson correlation coefficient, the Spearman correlation coefficient, Kendall rank correlation coefficient, etc. can also be used to analyze the correlation between the PPG1 signal and the motion signal, and the signals with weak correlation with the PPG1 signal in the motion signal can be eliminated to avoid generating new noise in the adaptive filtering process and affecting the motion interference suppression effect.

[0042] In some optional embodiments, before step S32, the PPG1 signal and the motion signal may be filtered. Specifically, since the effective frequency band of the real PPG signal is [0.5, 8] Hz, and the signals in other frequency bands are mostly noise, the collected PPG1 signal is subjected to a [0.5, 8] Hz bandpass filter. It is also necessary to filter according to the movement conditions (such as movement frequency). For example, when the movement frequency is fast, a bandpass filter of [10, 15] Hz can be used, and when the movement frequency is slow, a bandpass filter of [1, 2] Hz can be used. Usually, a bandpass filter of [1, 4] Hz is selected to filter the motion signal, which can filter out high-frequency motion artifacts while retaining the low-frequency components related to the PPG signal.

[0043] By filtering the PPG1 signal and the motion signal, the effective frequency band information in the PPG1 signal is retained, and the frequency components related to the PPG signal in the motion signal are retained. When the correlation analysis of the PPG1 signal and the motion signal is subsequently performed, the accuracy of the correlation analysis can be increased.

[0044] Step S33: Adaptively filter the filtered motion signal and the first physiological signal using an adaptive filtering algorithm to obtain a second physiological signal.

[0045] Specifically, among the N channel signals obtained after screening, the channel signals corresponding to the Pearson correlation coefficient greater than 0 are divided into positive group signals, and the channel signals corresponding to the Pearson correlation coefficient less than 0 are divided into negative group signals; according to the grouping results, the grouped motion signals and PPG1 signals are adaptively filtered using an adaptive filtering algorithm to obtain PPG2 signals. Among them, the adaptive filtering algorithm can be the least mean square (LMS) algorithm, the recursive least squares algorithm (RLS), the normalized least mean squares algorithm (NLMS), etc. This application takes the LMS adaptive filtering algorithm as an example for explanation, such as Figure 4 As shown, Figure 4 This is the structural diagram of the LMS adaptive filter. The LMS adaptive filter has two inputs, one is the signal containing noise, and the other is the reference signal.

[0046] In one embodiment, if N channel signals are all divided into positive group signals or negative group signals, the N channel signals are summed to obtain a first reference signal; the first reference signal and the PPG1 signal are input into a minimum mean square adaptive filter for adaptive filtering to obtain a PPG3 signal, and the PPG3 signal is used to characterize the PPG signal after adaptive filtering; the PPG3 signal is phase aligned according to the PPG1 signal to obtain a PPG2 signal.

[0047] With the above 6 channels For example, the Pearson correlation coefficient of the six channels is obtained by filtering the motion signal. The absolute value of is greater than or equal to the preset threshold, and If all are greater than 0, the 6 channels constitute a positive group signal ,Right now Included in [ ]. The minimum mean square adaptive filter requires two inputs, namely the observation signal containing the noise signal and the reference signal as the target. The PPG1 signal is taken as the signal containing the noise. The six channel signals in the group are summed as the first reference signal, and the first reference signal and the PPG1 signal are input into the minimum mean square adaptive filter for adaptive filtering to obtain the PPG3 signal, which is used to characterize the PPG signal after adaptive filtering.

[0048] Since the filter has different phase shifts for signals of different frequencies during the filtering process, the output signal will have a certain phase shift. In order to ensure that the PPG3 signal is phase-aligned with the original PPG signal and avoid deviation, the PPG signal after adaptive filtering needs to be phase-aligned. Specifically, by performing convolution calculation on the PPG1 signal and the PPG3 signal, a signal representing the similarity between the two signals can be obtained, and its maximum value corresponds to the most similar point between the two signals.

[0049] The calculation formula is as follows: Wherein, t represents time and τ represents phase offset.

[0050] According to the target phase offset τ corresponding to the maximum value in the convolution calculation result, the PPG3 signal is shifted on the time axis to achieve the alignment of PPG3 and PPG1 on the time axis. Specifically, when τ>0, the data of the PPG3 signal is shifted to the left (i.e., it moves in the negative direction of the time axis), and when τ<0, the data of the PPG3 signal is shifted to the right (i.e., it moves in the positive direction of the time axis). If the data length of the PPG3 signal is insufficient due to the movement (i.e., the beginning or end of the PPG3 signal is missing data after the movement), these missing parts are supplemented with 0, and the PPG2 signal is finally obtained after phase alignment. Through phase alignment, the accuracy of subsequent analysis of the PPG2 signal and extraction of a certain feature (such as heart rate, HRV, etc.) is improved.

[0051] In another embodiment, if N channel signals are divided into positive group signals and negative group signals, the positive group signals and the negative group signals are sorted into signal groups to obtain a first group of signals and a second group of signals, and the absolute value of the sum of the Pearson correlation coefficients corresponding to the first group of signals is greater than the absolute value of the sum of the Pearson correlation coefficients corresponding to the second group of signals; the channel signals in the first group of signals are summed to obtain a second reference signal; the second reference signal and the PPG1 signal are input into a minimum mean square adaptive filter for a first adaptive filtering to obtain a PPG4 signal, and the PPG4 signal is used to characterize the PPG signal after the first adaptive filtering; the PPG4 signal is phase-aligned according to the PPG1 signal to obtain a PPG5 signal; the channel signals in the second group of signals are summed to obtain a third reference signal; the third reference signal and the PPG5 signal are input into a minimum mean square adaptive filter for a second adaptive filtering to obtain a PPG6 signal, and the PPG6 signal is used to characterize the PPG signal after the second adaptive filtering; the PPG6 signal is phase-aligned according to the PPG1 signal to obtain a PPG2 signal.

[0052] Similarly, with the above 6 channels For example, the Pearson correlation coefficient of the six channels is obtained by filtering the motion signal. The absolute value of is greater than or equal to the preset threshold, and Greater than 0, then Positive group signal ,Right now Included in [ ]; Less than 0, then Negative group signal ,Right now Included in [ Next, align the group signal and negative group signal Sum the Pearson correlation coefficients in and then find the absolute value, that is, calculate ,as well as , the group with the largest absolute value is recorded as the first group of signals , and the other group is recorded as the second group of signals According to the calculation results, the first group of signals is obtained for , the second group of signals for .

[0053] right Channel signals within the group The sum is performed to obtain a second reference signal, and the second reference signal and the PPG1 signal are input into a minimum mean square adaptive filter for a first adaptive filtering to obtain a PPG4 signal, which is used to characterize the PPG signal after the first adaptive filtering. The motion signal in the group is phase-aligned with the PPG1 signal. To align the motion signals within the group, it is necessary to phase align the PPG4 signal according to the PPG1 signal, that is, to perform convolution calculation on the PPG4 and PP1 signals, and to perform time axis translation on the PPG4 signal according to the target phase offset corresponding to the maximum value in the convolution calculation result, to obtain the PPG5 signal. The specific implementation principle refers to the above-mentioned steps of phase aligning the PPG3 signal with the original PPG signal, which will not be repeated here.

[0054] Next, Channel signals within the group The sum is performed to obtain a third reference signal, and the third reference signal and the PPG5 signal are input into the minimum mean square adaptive filter for a second adaptive filtering to obtain a PPG6 signal, which is used to characterize the PPG signal after the second adaptive filtering. Based on the same idea, in order to ensure that the PPG6 signal is phase-aligned with the original PPG signal and avoid deviation, it is necessary to phase-align the PPG signal after adaptive filtering, and phase-align the PPG6 signal according to the PPG1 signal, that is, convolution calculation is performed on the PPG6 and PP1 signals, and the PPG6 signal is time-shifted according to the target phase offset corresponding to the maximum value in the convolution calculation result to obtain the PPG2 signal, that is, the PPG signal after the motion interference is finally suppressed. For the specific implementation principle, please refer to the above-mentioned steps of phase alignment of the PPG3 signal with the original PPG signal, which will not be repeated here.

[0055] By grouping the running signals after threshold screening, the channel signals corresponding to the Pearson correlation coefficient greater than 0 are divided into positive group signals, and the channel signals corresponding to the Pearson correlation coefficient less than 0 are divided into negative group signals. If the motion signals can be divided into positive group signals or negative group signals, the motion signals and the PPG signals disturbed by motion are input into the adaptive filter for one adaptive filtering and one phase alignment to obtain the PPG signal after the motion interference is suppressed; if the motion signals can be divided into positive group signals and negative group signals, it is necessary to sort the signal groups of the positive group signals and the negative group signals, and then perform two adaptive filtering and two phase alignments to obtain the PPG signal after the motion interference is suppressed. This method can effectively suppress the motion interference in the signal.

[0056] In the present application, by analyzing the correlation between the physiological signal disturbed by motion and the motion signal, that is, analyzing the influence of the motion signal on the physiological signal, the Pearson correlation coefficients of multiple channel signals in the motion signal and the physiological signal disturbed by motion are calculated respectively, and the motion signal is screened, and the channel signal corresponding to the absolute value of the Pearson correlation coefficient less than the preset threshold is eliminated, that is, the channel signal with less influence on the physiological signal is eliminated. Then, the adaptive filtering algorithm is used to adaptively filter the screened motion signal and the physiological signal disturbed by motion to obtain the physiological signal after the motion interference is suppressed. This can avoid adding the motion signal with less influence on the physiological signal to the adaptive filtering, which generates new noise, resulting in the inability to effectively suppress the motion interference in the physiological signal and the inability to restore the relatively accurate original physiological signal.

[0057] As shown in FIG. 5(a) and FIG. 5(b), FIG. 5(a) and FIG. 5(b) are result diagrams of the method for suppressing physiological signal motion interference based on human factor intelligence provided in an embodiment of the present application. Taking the physiological signal as a PPG signal as an example, the first line in FIG. 5(a) and FIG. 5(b) is a PPG signal not disturbed by motion, the second line is a PPG signal containing motion interference, and the third line is a PPG signal using two groups of motion signals ( ) is the result of cascade adaptive filtering, and the fourth row is two groups of motion signals. As shown in the result diagrams shown in Figures 5(a) and 5(b), it can be seen that the waveform of the PPG signal after filtering in the third row is close to that of the original PPG signal in the first row, and the number of peaks is the same, indicating that the method provided in the embodiment of the present application can effectively suppress motion interference in the signal.

[0058] Based on the same idea, the embodiment of the present application also provides a physiological signal motion interference suppression device based on human intelligence, such as Figure 6 The schematic diagram of the structure of the physiological signal motion interference suppression device based on human factors intelligence provided in the embodiment of the present application is as follows. The physiological signal motion interference suppression device based on human factors intelligence 60 mainly includes: An acquisition module 61, configured to acquire a first physiological signal and a motion signal, wherein the first physiological signal is used to characterize a physiological signal disturbed by motion; A screening module 62, configured to screen the motion signal based on the correlation between the first physiological signal and the motion signal; The adaptive filtering module 63 is used to use an adaptive filtering algorithm to adaptively filter the filtered motion signal and the first physiological signal to obtain a second physiological signal, wherein the second physiological signal is used to characterize the physiological signal after suppressing motion interference.

[0059] In one possible implementation manner, the physiological signal is at least any one of a PPG signal, an electroencephalogram signal, an electrocardiogram signal, and an electromyography signal.

[0060] In one possible implementation, the physiological signal is a PPG signal, the first physiological signal is a PPG1 signal, and the second physiological signal is a PPG2 signal. In one possible implementation, the motion signal includes M channel signals, and the upper screening module 62 may also be used for: Calculate the Pearson correlation coefficients of the M channel signals and the PPG1 signal respectively; Among the M channel signals, the channel signals corresponding to the absolute values ​​of the Pearson correlation coefficients that are less than a preset threshold are eliminated to obtain N channel signals, where N≤M.

[0061] In one possible implementation, the adaptive filtering module 63 may also be used to: Among the N channel signals, the channel signals corresponding to the Pearson correlation coefficient being greater than 0 are classified as positive group signals, and the channel signals corresponding to the Pearson correlation coefficient being less than 0 are classified as negative group signals; According to the grouping result, the grouped motion signal and PPG1 signal are adaptively filtered using an adaptive filtering algorithm to obtain a PPG2 signal.

[0062] In one possible implementation, the adaptive filtering algorithm includes a least mean square adaptive filtering algorithm, and the adaptive filtering module 63 may also be used to: If the N channel signals are all divided into positive group signals or negative group signals, then the N channel signals are summed to obtain a first reference signal; Inputting the first reference signal and the PPG1 signal into a minimum mean square adaptive filter for adaptive filtering to obtain a PPG3 signal, where the PPG3 signal is used to characterize the PPG signal after adaptive filtering; The PPG3 signal is phase-aligned according to the PPG1 signal to obtain the PPG2 signal.

[0063] In one possible implementation, phase alignment of the PPG3 signal is performed according to the PPG1 signal, including: Perform convolution calculation on PPG1 signal and PPG3 signal; The PPG3 signal is time-shifted according to the target phase offset corresponding to the maximum value in the convolution calculation result.

[0064] In one possible implementation, the adaptive filtering algorithm includes a least mean square adaptive filtering algorithm, and the adaptive filtering module 63 may also be used to: If the N channel signals are divided into a positive group signal and a negative group signal, the positive group signal and the negative group signal are sorted into signal groups to obtain a first group signal and a second group signal, and the absolute value of the sum of the Pearson correlation coefficients corresponding to the first group signal is greater than the absolute value of the sum of the Pearson correlation coefficients corresponding to the second group signal; summing the channel signals in the first group of signals to obtain a second reference signal; Inputting the second reference signal and the PPG1 signal into a minimum mean square adaptive filter for a first adaptive filtering to obtain a PPG4 signal, where the PPG4 signal is used to characterize the PPG signal after the first adaptive filtering; Phase-align the PPG4 signal according to the PPG1 signal to obtain the PPG5 signal; summing the channel signals in the second group of signals to obtain a third reference signal; Inputting the third reference signal and the PPG5 signal into a minimum mean square adaptive filter for a second adaptive filtering to obtain a PPG6 signal, where the PPG6 signal is used to characterize the PPG signal after the second adaptive filtering; The PPG6 signal is phase-aligned according to the PPG1 signal to obtain the PPG2 signal.

[0065] In one possible implementation manner, the apparatus 60 further includes a filtering module, which is used to filter the first physiological signal and the motion signal before filtering the motion signal based on the correlation between the first physiological signal and the motion signal.

[0066] Figure 6 The physiological signal motion interference suppression device 60 based on human factors intelligence provided in the illustrated embodiment can be used to execute the technical solution of the method embodiment shown in the present application. Its implementation principle and technical effects can be further referred to the relevant description in the method embodiment.

[0067] It should be understood that the above Figure 6The division of the modules of the physiological signal motion interference suppression device 60 based on human intelligence shown is only a division of logical functions. In actual implementation, they can be fully or partially integrated into one physical entity, or they can be physically separated. And these modules can all be implemented in the form of software calling through processing elements; they can also be all implemented in the form of hardware; some modules can also be implemented in the form of software calling through processing elements, and some modules can be implemented in the form of hardware. For example, the acquisition module can be a separately established processing element, or it can be integrated in a chip of an electronic device. The implementation of other modules is similar. In addition, all or part of these modules can be integrated together, or they can be implemented independently. In the implementation process, each step of the above method or each of the above modules can be completed by an integrated logic circuit of hardware in the processor element or instructions in the form of software.

[0068] For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as one or more application specific integrated circuits (ASIC), or one or more microprocessors (DSP), or one or more field programmable gate arrays (FPGA). For another example, these modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0069] In the above embodiments, the processor involved may include, for example, a CPU, a DSP, a microcontroller or a digital signal processor, and may also include a GPU, an embedded neural network processor (Neural-network Process Units; hereinafter referred to as: NPU) and an image signal processor (Image Signal Processing; hereinafter referred to as: ISP). The processor may also include necessary hardware accelerators or logic processing hardware circuits, such as ASIC, or one or more integrated circuits for controlling the execution of the program of the technical solution of the present application. In addition, the processor may have the function of operating one or more software programs, and the software programs may be stored in a storage medium.

[0070] An embodiment of the present application also provides a computer-readable storage medium, in which a computer program is stored. When the computer-readable storage medium is run on a computer, the computer executes the method provided by the embodiment shown in the present application.

[0071] Combine the following Figure 7The exemplary electronic device provided in the embodiments of the present application is further introduced. Figure 7 A schematic structural diagram of an electronic device 7000 is shown.

[0072] The electronic device 7000 may include: at least one processor; and at least one memory communicatively connected to the above-mentioned processor, wherein: the above-mentioned memory stores program instructions (such as computer programs) that can be executed by the above-mentioned processor, and the processor calls and executes the above-mentioned program instructions to implement the physiological signal motion interference suppression method based on human factors intelligence provided in the embodiment shown in the present application.

[0073] Figure 7 A block diagram of an exemplary electronic device 7000 suitable for implementing the embodiments of the present application is shown. Figure 7 The electronic device 7000 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0074] like Figure 7 As shown, the electronic device 7000 is in the form of a general computing device. The components of the electronic device 7000 may include, but are not limited to: one or more processors 7010, a memory 7020, a communication bus 7040 connecting different system components (including the memory 7020 and the processor 7010), and a communication interface 7030.

[0075] Communication bus 7040 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor or a local bus using any of a variety of bus structures. For example, these architectures include but are not limited to Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnection (PCI) bus.

[0076] The electronic device 7000 typically includes a variety of computer system readable media, which can be any available media that can be accessed by the electronic device, including volatile and nonvolatile media, removable and non-removable media.

[0077] The memory 7020 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory. The electronic device may further include other removable / non-removable, volatile / non-volatile computer system storage media. Figure 7 Not shown, a disk drive for reading and writing a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing a removable non-volatile optical disk (e.g., a compact disc read only memory (CD-ROM), a digital versatile disc read only memory (DVD-ROM), or other optical media) may be provided. In these cases, each drive may be connected to the communication bus 7040 via one or more data medium interfaces. The memory 7020 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present application.

[0078] A program / utility having a set (at least one) of program modules may be stored in the memory 7020, such program modules including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. The program modules generally perform the functions and / or methods of the embodiments described herein.

[0079] The electronic device 7000 may also communicate with one or more external devices (e.g., keyboard, pointing device, display, etc.), one or more devices that enable a user to interact with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (e.g., network card, modem, etc.). Such communication may be performed via the communication interface 7030. Furthermore, the electronic device 7000 may also communicate with the network adapter ( Figure 7 The network adapter may communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via the communication bus 7040. It should be understood that although Figure 7Not shown, other hardware and / or software modules may be used in conjunction with electronic device 7000, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, disk arrays (Redundant Arrays of Independent Drives; hereinafter referred to as: RAID) systems, tape drives, and data backup storage systems.

[0080] The processor 7010 executes various functional applications and data processing by running the programs stored in the memory 7020, such as implementing the method provided in the embodiment of the present application.

[0081] It is understandable that the interface connection relationship between the modules illustrated in the embodiment of the present application is only a schematic illustration and does not constitute a structural limitation on the electronic device 7000. In other embodiments of the present application, the electronic device 7000 may also adopt different interface connection methods in the above embodiments, or a combination of multiple interface connection methods.

[0082] In the above embodiments, the processor involved may include, for example, a CPU (Central Processing Unit), a DSP (Digital Signal Processor), a microcontroller, and may also include a GPU (Graphics Processing Unit), an embedded neural network processor (Neural-network Process Units; hereinafter referred to as: NPU) and an image signal processor (Image Signal Processing; hereinafter referred to as: ISP). The processor may also include necessary hardware accelerators or logic processing hardware circuits, such as ASIC (Application-Specific Integrated Circuit, application-specific integrated circuits), or one or more integrated circuits for controlling the execution of the program of the technical solution of the present application. In addition, the processor may have the function of operating one or more software programs, and the software programs may be stored in a storage medium.

[0083] Those of ordinary skill in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented in a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0084] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0085] In several embodiments provided in the present application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory; hereinafter referred to as: ROM), random access memory (Random Access Memory; hereinafter referred to as: RAM), disk or optical disk, and other media that can store program codes.

[0086] The above is only a specific implementation of the present application. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. The protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for suppressing physiological signal motion interference based on human intelligence, characterized in that: The method comprises: Acquire a first physiological signal and a motion signal, wherein the first physiological signal is used to characterize the physiological signal disturbed by motion; screening the motion signal based on the correlation between the first physiological signal and the motion signal; The filtered motion signal and the first physiological signal are adaptively filtered using an adaptive filtering algorithm to obtain a second physiological signal, where the second physiological signal is used to characterize the physiological signal after motion interference is suppressed.

2. The method according to claim 1, characterized in that The physiological signal is at least any one of a PPG signal, an electroencephalogram signal, an electrocardiogram signal, and an electromyography signal.

3. The method according to claim 2, characterized in that The physiological signal is a PPG signal, the first physiological signal is a PPG1 signal, and the second physiological signal is a PPG2 signal.

4. The method according to claim 3, characterized in that The motion signal includes M channel signals, and the screening of the motion signal based on the correlation between the first physiological signal and the motion signal includes: Calculate the Pearson correlation coefficients of the M channel signals and the PPG1 signal respectively; Among the M channel signals, channel signals corresponding to the absolute values ​​of the Pearson correlation coefficients that are less than a preset threshold are eliminated to obtain N channel signals, where N≤M.

5. The method according to claim 4, characterized in that The step of adaptively filtering the filtered motion signal and the first physiological signal using an adaptive filtering algorithm to obtain a second physiological signal includes: Among the N channel signals, the channel signals corresponding to the Pearson correlation coefficient being greater than 0 are classified as positive group signals, and the channel signals corresponding to the Pearson correlation coefficient being less than 0 are classified as negative group signals; According to the grouping result, the grouped motion signal and the PPG1 signal are adaptively filtered using an adaptive filtering algorithm to obtain a PPG2 signal.

6. The method according to claim 5, characterized in that The adaptive filtering algorithm includes a least mean square adaptive filtering algorithm. According to the grouping result, the adaptive filtering algorithm is used to adaptively filter the grouped motion signal and the PPG1 signal to obtain the PPG2 signal, including: If the N channel signals are all divided into positive group signals or negative group signals, summing the N channel signals to obtain a first reference signal; Inputting the first reference signal and the PPG1 signal into a minimum mean square adaptive filter for adaptive filtering to obtain a PPG3 signal, wherein the PPG3 signal is used to characterize the PPG signal after the adaptive filtering; The PPG3 signal is phase-aligned according to the PPG1 signal to obtain a PPG2 signal.

7. The method according to claim 6, characterized in that The phase aligning the PPG3 signal according to the PPG1 signal includes: Performing convolution calculation on the PPG1 signal and the PPG3 signal; The PPG3 signal is time-axis shifted according to the target phase offset corresponding to the maximum value in the convolution calculation result.

8. The method according to claim 5, characterized in that The adaptive filtering algorithm includes a least mean square adaptive filtering algorithm. According to the grouping result, the adaptive filtering algorithm is used to adaptively filter the grouped motion signal and the PPG1 signal to obtain the PPG2 signal, including: If the N channel signals are divided into a positive group signal and a negative group signal, the positive group signal and the negative group signal are sorted into signal groups to obtain a first group signal and a second group signal, and the absolute value of the sum of the Pearson correlation coefficients corresponding to the first group signal is greater than the absolute value of the sum of the Pearson correlation coefficients corresponding to the second group signal; summing the channel signals in the first group of signals to obtain a second reference signal; Inputting the second reference signal and the PPG1 signal into a minimum mean square adaptive filter for a first adaptive filtering to obtain a PPG4 signal, wherein the PPG4 signal is used to characterize the PPG signal after the first adaptive filtering; Performing phase alignment on the PPG4 signal according to the PPG1 signal to obtain a PPG5 signal; summing the channel signals in the second group of signals to obtain a third reference signal; Inputting the third reference signal and the PPG5 signal into a minimum mean square adaptive filter for a second adaptive filtering to obtain a PPG6 signal, wherein the PPG6 signal is used to characterize the PPG signal after the second adaptive filtering; The PPG6 signal is phase-aligned according to the PPG1 signal to obtain a PPG2 signal.

9. The method according to any one of claims 3 to 8, characterized in that: Before screening the motion signal based on the correlation between the first physiological signal and the motion signal, the method further includes: The PPG1 signal and the motion signal are filtered.

10. A physiological signal motion interference suppression device based on human intelligence, characterized in that: The device comprises: An acquisition module, used for acquiring a first physiological signal and a motion signal, wherein the first physiological signal is used for representing a physiological signal disturbed by motion; a screening module, configured to screen the motion signal based on the correlation between the first physiological signal and the motion signal; The adaptive filtering module is used to adaptively filter the filtered motion signal and the first physiological signal using an adaptive filtering algorithm to obtain a second physiological signal, wherein the second physiological signal is used to characterize the physiological signal after suppressing motion interference.

11. An electronic device, characterized in that: include: A processor and a memory, the memory being used to store a computer program; the processor being used to run the computer program to implement the method for suppressing physiological signal motion interference based on human factors intelligence as described in any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed on a computer, the method for suppressing physiological signal motion interference based on human factors intelligence as described in any one of claims 1 to 9 is implemented.

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