CN111643053A - Method and system for reducing motion artifacts in pulse wave signals - Google Patents
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Abstract
The invention relates to the technical field of digital signal processing, and discloses a method for reducing motion artifacts in pulse wave signals, which comprises the following steps: acquiring an original pulse wave signal and a reference signal; according to a self-adaptive filter based on a least mean square algorithm, taking the original pulse wave signal as input, taking the reference signal as expected output, and acquiring a motion artifact signal in the original pulse wave signal; and acquiring a pure pulse wave signal according to the original pulse wave signal and the motion artifact signal. The invention also discloses a corresponding system. The technical scheme of the invention can effectively reduce the motion noise in the PPG signal and obtain a pure pulse wave signal.
Description
Technical Field
The invention relates to the technical field of digital signal processing, in particular to a method and a system for reducing motion artifacts in pulse wave signals.
Background
Photoplethysmography is a technique for detecting a change in blood volume in a living tissue by means of a photoelectric signal. When light with specific wavelength is projected to the tested tissue, the light beam is received by the photosensitive period after passing through the tissue in a reflection or transmission mode. The human tissue can absorb the passing light beam, so the received light intensity can be attenuated certainly, wherein the attenuation effects of skin, bones, muscles and the like on the light beam are always the same, but the blood volume of arterial blood vessels can be changed periodically along with the contraction and the relaxation of ventricles, so that the corresponding periodic change of the emergent light intensity is caused, and the light intensity change is converted into an electric signal which is a pulse wave signal. The heart rate is calculated according to the pulse wave signals, and the method is widely applied to detection of human body movement heart rate and heart rate variability.
More common intelligent wearing equipment that utilizes can real-time detection pulse wave signal, and then acquire heart rate information. However, a contact gap exists between the pulse wave acquisition equipment and the skin, so that a measurement light path is changed in the movement process, the waveform of the pulse wave is superimposed with movement interference noise, and the subsequent calculation of the physiological characteristic information by using the pulse wave is difficult, so that the subsequent calculation is deviated. Therefore, in the motion state, the pulse wave signal usually has strong motion artifact, and the elimination of the motion artifact interference has been a major problem to be solved in the pulse wave signal processing.
In the prior art, an acceleration signal is used as a reference signal, and the relationship between acceleration and motion is utilized to assist in reducing motion artifacts. The patent application with publication number CN108937878A discloses a method for eliminating pulse wave signal motion noise, acquires pulse wave signal and acceleration signal containing noise, adopts parallel adaptive filter, and is based on the acceleration signal is right the pulse wave signal containing noise filters to through Fourier transform, obtain acceleration frequency spectrum and pulse wave frequency spectrum, and subtract the two, obtain pure pulse wave signal. In actual practice, the true acceleration cannot be detected. The sensor can only acquire the combined acceleration of the acceleration and the gravity acceleration, and the information of the acceleration is inaccurate. Even if a true acceleration is obtained, the relative motion and relationship between the device and the skin cannot be effectively reflected. For example, when the device is well-fitted to the skin surface and strongly bound, the motion does not necessarily bring about corresponding motion artifacts. For another example, when the device is bound on the skin surface by the elastic material, the motion track and direction of the device may not be consistent with the motion direction of the human body due to the stress of the elastic material, and there may be temporal desynchronization, and the device with the acceleration sensor may not well remove the motion interference and correctly calculate the heart rate information.
Therefore, how to reduce or even eliminate the motion artifact in the pulse wave to obtain a pure pulse wave signal is a problem that needs to be solved urgently.
Disclosure of Invention
The invention aims to provide a method and a system for reducing motion artifacts in pulse wave signals, which can effectively reduce motion noise in the pulse wave signals and obtain pure pulse wave signals.
To achieve the above object, the present invention provides a method of reducing motion artifacts in a pulse wave signal, the method comprising: acquiring an original pulse wave signal and a reference signal; according to a self-adaptive filter based on a least mean square algorithm, taking the original pulse wave signal as input, taking the reference signal as expected output, and acquiring a motion artifact signal in the original pulse wave signal; and acquiring a pure pulse wave signal according to the original pulse wave signal and the motion artifact signal. The reference signal can be better utilized to reduce the motion artifact signal from the pulse wave signal, so that the accuracy of the heart rate signal is higher.
Optionally, the step S1 includes: setting a reference light which is close to the irradiation area of the light of the pulse wave signal and has no response or weak response to the heart rate signal; the reference light is synchronized with the light of the pulse wave signal in real time and the two are relatively close to each other in response to the same degree of movement. And simultaneously irradiating the light of the pulse wave signal and the reference light, and obtaining an original pulse wave signal and a reference signal after reflection.
Optionally, before the step S2, the method further includes: and respectively filtering the original pulse wave signal and the reference signal through the same filter to obtain a filtered pulse wave signal and a filtered reference signal. The filter is a band-pass filter, and the frequency of the band-pass filter is 0.5Hz-5 Hz. The filter is formed by connecting a high-pass filter and a low-pass filter in series, and the cut-off frequency of the low-pass filter is greater than that of the high-pass filter. Various noise signals are filtered, out-of-band information is filtered, and problem complication caused by the out-of-band information is avoided.
Optionally, the step S2 specifically includes: intercepting M data from a current point (including the current point) of the original pulse wave signal to form an input vector X (n), taking the reference signal as an expected output d (n), setting a learning rate eta, and randomly initializing a weight vector W (n);
the least mean square algorithm iterative formula is as follows:
the filtered output signal is: y (n) w (n) x (n);
wherein, x (n) ═ x (n) ….. x (n-M +1) ], M is the number of intercepted data;
error signal: e (n) ═ d (n) -y (n);
updating the weight coefficient: w (n +1) ═ W (n) + η x (n) e (n);
and if the error e (n) is larger, updating the weight value of the weight vector W (n), and recalculating y (n) and e (n) until the iteration is terminated when the value of e (n) is the minimum, wherein the obtained y (n) is the motion artifact signal in the original pulse wave signal. The technical scheme of the self-adaptive filter based on the least mean square algorithm can align the phases, and allows the reference signal and the pulse wave signal to be out of synchronization for a certain time. Adaptive filtering can be compatible with amplitude inconsistencies, selecting the degree of convergence adaptively.
Optionally, the step S3 includes: and subtracting the motion artifact signal from the original pulse wave signal to obtain a pure pulse wave signal.
Optionally, the method further includes: when the number of the paths of the original pulse wave signals is more than 1, acquiring pure pulse wave signals corresponding to each path of the original pulse wave signals according to a self-adaptive filter based on a least mean square algorithm; and summing the pure pulse wave signals corresponding to all the paths and taking the average value of the pure pulse wave signals, wherein the average value is the obtained final pure pulse wave signal. The convergence speed and accuracy rate of the self-adaptive filtering are improved through the multiple paths of original pulse wave signals and the reference signal.
The invention provides a system for reducing motion artifacts in pulse wave signals, the system comprising: the acquisition module is used for acquiring a path of original pulse wave signal and a path of reference signal; the minimum mean square module is used for taking the original pulse wave signal as input and the reference signal as expected output according to a self-adaptive filter based on a minimum mean square algorithm to obtain a motion artifact signal in the original pulse wave signal; and the calculation module is used for acquiring a pure pulse wave signal according to the original pulse wave signal and the motion artifact signal.
Compared with the prior art, the method and the system for reducing the motion artifact in the pulse wave signal have the following beneficial effects: the motion artifact signals in the pulse wave signals are obtained based on the adaptive filtering and the reference signals of the least mean square algorithm, and then pure pulse wave signals are obtained, so that the reference signals can be better utilized, the motion artifact signals are reduced from the pulse wave signals, and the accuracy of the heart rate signals is higher.
Drawings
Fig. 1 is a flowchart illustrating a method for reducing motion artifacts in a pulse wave signal according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of an adaptive filter according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of a filtered pulse wave signal and a filtered reference signal according to an embodiment of the invention.
Fig. 4 is a schematic diagram of acquiring a clean pulse wave signal according to an embodiment of the invention.
Fig. 5 is a block diagram of a system for reducing motion artifacts in pulse wave signals according to an embodiment of the present invention.
Detailed Description
The present invention will be described in detail below with reference to specific embodiments shown in the drawings. In the drawings, structurally identical elements are represented by like reference numerals, and structurally or functionally similar elements are represented by like reference numerals throughout the several views. The size and thickness of each component shown in the drawings are arbitrarily illustrated, and the present invention is not limited to the size and thickness of each component. The thickness of the components may be exaggerated where appropriate in the figures to improve clarity.
As shown in fig. 1, an embodiment of the present invention provides a method for reducing motion artifacts in pulse wave signals, the method comprising:
s1, acquiring an original pulse wave signal and a reference signal;
s2, taking the original pulse wave signal as input and the reference signal as expected output according to a self-adaptive filter based on a least mean square algorithm, and acquiring a motion artifact signal in the original pulse wave signal;
and S3, acquiring a pure pulse wave signal according to the original pulse wave signal and the motion artifact signal.
The pulse wave signal is actually a reflection signal of light, and the motion artifact signal is also introduced in the form of a light signal, so that the motion artifact signal can be directly considered as the light signal. Taking the measurement of the heart rate signal as an example, the pulse wave signal is a superposition of the heart rate signal and the motion artifact signal. Therefore, a reference light is designed which is close to the irradiation region of the light of the pulse wave signal and has no response or a weak response to the heart rate signal, and can be used as the reference signal. The pure pulse wave signal can be obtained by subtracting the pulse wave signal from the reference signal and calculating the difference, so that the motion artifact signal is inhibited. However, in practice, the two signals still have a certain difference, the signal magnitudes of the two signals are different due to the difference of the light sources, and also have a certain difference due to the elasticity of the human body, and the direct subtraction may not necessarily obtain an ideal motion artifact filtering effect. Therefore, the motion artifact signal in the pulse wave signal is obtained based on the adaptive filtering of the least mean square algorithm and the reference signal, and the pure pulse wave signal is further obtained, so that the reference signal can be better utilized to reduce the motion artifact signal from the pulse wave signal.
Step S1 is to obtain at least one original PPG signal and one reference signal. Specifically, the reference signal is generated based on reference light which is close to the irradiation region of the light of the pulse wave signal and has no response or weak response to the heart rate signal. Such as red light. The reference light is synchronized with the light of the pulse wave signal in real time and the two are relatively close to each other in response to the same degree of movement. And simultaneously irradiating the light of the pulse wave signal and the reference light, and obtaining an original pulse wave signal and a reference signal after reflection.
In a specific embodiment of the present invention, the original pulse wave signal and the reference signal are filtered by the same filter, so as to obtain a filtered pulse wave signal and a filtered reference signal. The original pulse wave signals and the reference signals are filtered, various noise signals are filtered, out-of-band information is filtered, and problem complication caused by the out-of-band information is avoided.
In a specific embodiment of the present invention, the filter is a band-pass filter, and the frequency of the band-pass filter is 0.5Hz to 5 Hz. The band-pass filter has a cut-off frequency of a minimum of 0.5Hz and a maximum of 5Hz, the band-pass containing all possible frequencies of the heart rate. In another embodiment of the present invention, the filter is a high pass filter and a low pass filter connected in series, and the cut-off frequency of the low pass filter is greater than the cut-off frequency of the high pass filter, and the frequency of the filter is between the cut-off frequency of the high pass filter and the cut-off frequency of the low pass filter. The cut-off frequency of the low-pass filter is 5Hz, and the cut-off frequency of the high-pass filter is 0.5 Hz.
The adaptive filter is composed of a digital filter with adjustable parameters and an adaptive algorithm, as shown in fig. 2. The principle of the method is that an input signal x (n) passes through a parameter-adjustable digital filter to generate an output signal y (n), the output signal y (n) is compared with an expected signal d (n) to form an error signal e (n), filter parameters are adjusted through an adaptive algorithm, the mean square value of e (n) is finally minimized, and adaptive filtering can automatically adjust filter parameters at the current moment by using the result of the filter parameters obtained at the previous moment to adapt to the statistical characteristic that signal noise is unknown or changes along with time, so that optimal filtering is realized. In the invention, the adopted self-adaptive algorithm is a least mean square algorithm. The least mean square algorithm is a gradient-based algorithm, the application criterion is a mean square error function minimization principle, and the filter weight coefficients are continuously adjusted in iterative operation until the mean square error function reaches a minimum value. The basic idea is to use some relation between e (n) and x (n) to continuously update the weight coefficients of the adaptive filter, so as to minimize the mean square error.
Step S2 is to obtain a motion artifact signal in the original pulse wave signal by using the original pulse wave signal as an input and the reference signal as an expected output according to an adaptive filter based on a least mean square algorithm. According to a preferred embodiment of the present invention, according to the above embodiment, the original pulse wave signal and the reference signal are filtered to obtain a filtered pulse wave signal and a filtered reference signal, in this embodiment, the filtered pulse wave signal is used as an input, the filtered reference signal is used as an expected output, and a motion artifact signal in the original pulse wave signal is obtained. The least mean square algorithm specifically includes: the parameters and variables in the iterative process of the least mean square algorithm are set as follows: m is the filter order, eta is the learning rate, W is the weight vector of M dimension, X (n) is the input vector, d (n) is the expected output, y (n) is the actual output, e (n) is the error, and n is the point order number, namely the iteration number. Specifically, the original pulse wave signal is intercepted from a current point (including the current point) by M data to form an input vector x (n), the reference signal is an expected output d (n), a learning rate η is set, a weight vector w (n) is initialized randomly, and an iterative formula of the least mean square algorithm is as follows:
the filtered output signal is: y (n) w (n) x (n);
wherein, x (n) ═ x (n) ….. x (n-M +1) ], M is the number of intercepted data;
error signal: e (n) ═ d (n) -y (n);
updating the weight coefficient: w (n +1) ═ W (n) + η x (n) e (n);
specifically, an iteration process is performed, a weight vector W (n) is initialized randomly, y (n) is obtained through calculation according to the iteration formula, the difference value between d (n) and y (n), namely the value of the error e (n), is compared, if the error e (n) is larger, the weight value of the weight vector W (n) is updated again, y (n) and e (n) are calculated again, and the iteration is terminated until the value of e (n) is the minimum value, namely d (n) and y (n) are close to each other. At this time, y (n) is obtained as the motion artifact signal in the original pulse wave signal.
In step S3, a pure pulse wave signal is obtained according to the original pulse wave signal and the motion artifact signal. Specifically, the motion artifact signal is subtracted from the original pulse wave signal to obtain a pure pulse wave signal. For example, the input signal x (n) is subtracted by the output signal y (n) at the end of the iteration, which is the heart rate signal after the motion artifact signal is suppressed in the original pulse wave signal.
The technical scheme of the self-adaptive filter based on the least mean square algorithm can carry out phase alignment and allows the reference signal and the pulse wave signal to have certain time asynchronization. Adaptive filtering can be compatible with amplitude inconsistencies, selecting the degree of convergence adaptively.
In an embodiment of the present invention, as shown in fig. 3, fig. 3 is a diagram illustrating the results of the original pulse wave signal and the reference signal after being filtered by the band-pass filter. As can be seen from the figure, the upper curve is the filtered pulse wave signal data and the lower curve is the filtered reference signal data. As can be seen from the above curves, the motion artifact signal is mixed in the filtered pulse wave signal, and the signal is relatively cluttered, but the fluctuation law of the signal is consistent with that of the underlying reference signal. As shown in FIG. 4, a graph of a pure pulse wave signal after using the present invention is shown. As shown in the figure, the upper curve is the result of subtracting the pulse wave signal from the motion artifact signal, the lower curve is the pure pulse wave signal graph obtained after the adaptive filter based on the least mean square algorithm in the present invention is used, and the two curves are compared, and the pulse wave signals obtained by using the technical scheme of the present invention are relatively regular.
According to an embodiment of the present invention, in the least mean square algorithm, overshoot is easily generated when η is too large, and overshoot is easily generated when η is too small, the convergence rate of the adaptive filtering is slow, and it is difficult to effectively suppress the motion artifact signal when the motion interference is strong. Therefore, according to an embodiment of the present invention, a plurality of original pulse wave signals and a plurality of reference signals are collected, and the convergence rate and accuracy rate of the adaptive filtering are improved by the plurality of original pulse wave signals and the plurality of reference signals. Specifically, when the number of paths of the original pulse wave signals is greater than 1, for each path of original pulse wave signals, obtaining a pure pulse wave signal corresponding to each path according to a self-adaptive filter based on a least mean square algorithm; and summing the pure pulse wave signals corresponding to all the paths and taking the average value of the pure pulse wave signals, wherein the average value is the obtained final pure pulse wave signal. In the following, two original pulse wave signals are taken as an example for explanation, and the iterative formula of the least mean square algorithm is as follows:
the 1 st path of filtering output signals are: y1(n) ═ W1(n) X1 (n);
path 1 error signal: e1(n) ═ d (n) -y1 (n);
updating the 1 st path weight coefficient: w1(n +1) ═ W1(n) + η X1(n) e1 (n);
the 2 nd filtered output signal is: y2(n) ═ W2(n) X2 (n);
and 2, updating the weight coefficient of the path: w2(n +1) ═ W2(n) + η X2(n) e2 (n);
the final pure pulse wave signal is [ (y1(n) -X1(n)) + (y2(n) -X2(n)) ]/2.
The method and the device for obtaining the heart rate signals based on the motion artifact signals obtain the motion artifact signals in the pulse wave signals based on the adaptive filtering and the reference signals of the least mean square algorithm, further obtain pure pulse wave signals, can better utilize the reference signals, reduce the motion artifact signals from the pulse wave signals, and enable the accuracy of the heart rate signals to be higher.
Fig. 5 shows a system for reducing motion artifacts in a pulse wave signal, the system comprising:
the acquisition module 50 is configured to acquire an original pulse wave signal and a reference signal;
a least mean square module 51, configured to take the original pulse wave signal as an input and the reference signal as an expected output according to an adaptive filter based on a least mean square algorithm, and obtain a motion artifact signal in the original pulse wave signal;
a calculating module 52, configured to obtain a pure pulse wave signal according to the original pulse wave signal and the motion artifact signal.
The acquisition module acquires an original pulse wave signal and a reference signal. Specifically, the reference signal is generated based on reference light which is close to the irradiation region of the light of the pulse wave signal and has no response or weak response to the heart rate signal. Such as red light. The reference light is synchronized with the light of the pulse wave signal in real time and the two are relatively close to each other in response to the same degree of movement. And simultaneously irradiating the light of the pulse wave signal and the reference light, and obtaining an original pulse wave signal and a reference signal after reflection.
And the least mean square module takes the original pulse wave signal as input and the reference signal as expected output according to the self-adaptive filter based on the least mean square algorithm to acquire a motion artifact signal in the original pulse wave signal. The least mean square algorithm specifically includes: the parameters and variables in the iterative process of the least mean square algorithm are set as follows: m is the filter order, eta is the learning rate, W is the weight vector of M dimension, X (n) is the input vector, d (n) is the expected output, y (n) is the actual output, e (n) is the error, and n is the point order number, namely the iteration number. Specifically, the original pulse wave signal is intercepted from a current point (including the current point) by M data to form an input vector x (n), the reference signal is an expected output d (n), a learning rate η is set, a weight vector w (n) is initialized randomly, and an iterative formula of the least mean square algorithm is as follows:
the filtered output signal is: y (n) w (n) x (n);
wherein, x (n) ═ x (n) ….. x (n-M +1) ], M is the number of intercepted data;
error signal: e (n) ═ d (n) -y (n);
updating the weight coefficient: w (n +1) ═ W (n) + η x (n) e (n);
specifically, an iteration process is performed, a weight vector W (n) is initialized randomly, y (n) is obtained through calculation according to the iteration formula, the difference value between d (n) and y (n), namely the value of the error e (n), is compared, if the error e (n) is larger, the weight value of the weight vector W (n) is updated again, y (n) and e (n) are calculated again, and until the value of e (n) is the minimum value, namely d (n) and y (n) are very close. At this time, y (n) is obtained as the motion artifact signal in the original pulse wave signal.
And the calculation module acquires a pure pulse wave signal according to the original pulse wave signal and the motion artifact signal. Specifically, the motion artifact signal is subtracted from the original pulse wave signal to obtain a pure pulse wave signal. For example, the input signal x (n) is subtracted by the output signal y (n) at the end of the iteration, which is the heart rate signal after the motion artifact signal is suppressed in the original pulse wave signal.
According to the technical scheme, the motion artifact signals in the pulse wave signals are obtained based on the adaptive filtering and the reference signals of the least mean square algorithm, and then pure pulse wave signals are obtained, so that the reference signals can be better utilized, the motion artifact signals are reduced from the pulse wave signals, and the accuracy of the heart rate signals is higher.
While the invention has been described in detail in the foregoing with reference to the drawings and examples, such illustration and description are to be considered illustrative or exemplary and not restrictive. The invention is not limited to the disclosed embodiments. In the claims, the word "comprising" does not exclude other elements or steps, and the word "a" or "an" or "a particular plurality" should be understood to mean at least one or at least a particular plurality. Any reference signs in the claims shall not be construed as limiting the scope. Other variations to the above-described embodiments can be understood and effected by those skilled in the art without inventive faculty, from a study of the drawings, the description and the appended claims, which will still fall within the scope of the invention as claimed.
Claims (10)
1. A method of reducing motion artifacts in pulse wave signals, the method comprising:
s1, acquiring an original pulse wave signal and a reference signal;
s2, taking the original pulse wave signal as input and the reference signal as expected output according to a self-adaptive filter based on a least mean square algorithm, and acquiring a motion artifact signal in the original pulse wave signal;
and S3, acquiring a pure pulse wave signal according to the original pulse wave signal and the motion artifact signal.
2. The method for reducing motion artifact in pulse wave signals as claimed in claim 1, wherein said step S1 includes:
setting a reference light which is close to the irradiation area of the light of the pulse wave signal and has no response or weak response to the heart rate signal;
the reference light is synchronized with the light of the pulse wave signal in real time and the two are relatively close to each other in response to the same degree of movement.
3. The method for reducing motion artifact in pulse wave signals as claimed in claim 2, wherein said step S1 further comprises:
and simultaneously irradiating the light of the pulse wave signal and the reference light, and obtaining an original pulse wave signal and a reference signal after reflection.
4. The method for reducing motion artifacts in pulse wave signals according to claim 1, further comprising, before said step S2:
and respectively filtering the original pulse wave signal and the reference signal through the same filter to obtain a filtered pulse wave signal and a filtered reference signal.
5. The method of reducing motion artifact in pulse wave signals according to claim 4, wherein said filter is a band pass filter having a frequency of 0.5Hz-5 Hz.
6. The method according to claim 4, wherein the filter is a high-pass filter and a low-pass filter connected in series, and the cut-off frequency of the low-pass filter is greater than the cut-off frequency of the high-pass filter.
7. The method for reducing motion artifacts in pulse wave signals according to claim 1, wherein said step S2 specifically includes:
intercepting M data from a current point (including the current point) of the original pulse wave signal to form an input vector X (n), taking the reference signal as an expected output d (n), setting a learning rate eta, and randomly initializing a weight vector W (n);
the least mean square algorithm iterative formula is as follows:
the filtered output signal is: y (n) w (n) x (n);
wherein, x (n) ═ x (n) ….. x (n-M +1) ], M is the number of intercepted data;
error signal: e (n) ═ d (n) -y (n);
updating the weight coefficient: w (n +1) ═ W (n) + η x (n) e (n);
and if the error e (n) is larger, updating the weight value of the weight vector W (n), and recalculating y (n) and e (n) until the iteration is terminated when the value of e (n) is the minimum, wherein the obtained y (n) is the motion artifact signal in the original pulse wave signal.
8. The method for reducing motion artifact in pulse wave signals as claimed in claim 7, wherein said step S3 includes:
and subtracting the motion artifact signal from the original pulse wave signal to obtain a pure pulse wave signal.
9. The method of reducing motion artifact in a pulse wave signal as set forth in claim 8, further comprising:
when the number of the paths of the original pulse wave signals is more than 1, acquiring pure pulse wave signals corresponding to each path of the original pulse wave signals according to a self-adaptive filter based on a least mean square algorithm;
and summing the pure pulse wave signals corresponding to all the paths and taking the average value of the pure pulse wave signals, wherein the average value is the obtained final pure pulse wave signal.
10. A system for reducing motion artifacts in pulse wave signals, the system comprising: the acquisition module is used for acquiring a path of original pulse wave signal and a path of reference signal;
the minimum mean square module is used for taking the original pulse wave signal as input and the reference signal as expected output according to a self-adaptive filter based on a minimum mean square algorithm to obtain a motion artifact signal in the original pulse wave signal;
and the calculation module is used for acquiring a pure pulse wave signal according to the original pulse wave signal and the motion artifact signal.
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