CN111528825A - Photoelectric volume pulse wave signal optimization method - Google Patents
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Abstract
The invention discloses a method for optimizing a photoplethysmography signal, which comprises the following steps: s01, synchronously acquiring photoplethysmographic signals and motion state signals of the tested person; s02, performing a first round of optimization on the pulse wave signals, namely judging whether the tested person is in a static state or a slow walking state or a violent movement state according to the movement state signals, and if the tested person is in the violent movement state, rejecting the pulse wave signals in a corresponding time period; s03, denoising the pulse wave signals after the first round of optimization; s04, extracting characteristic indexes of the de-noised pulse wave signals; and S05, comparing the extracted characteristic indexes with a threshold range, and when at least one characteristic index falls outside the threshold range, marking the pulse wave period corresponding to the characteristic index as an abnormal period and removing the abnormal period to finally obtain the optimized pulse wave signal. This scheme is applicable to wearing formula medical equipment.
Description
Technical Field
The invention relates to the field of human blood pressure parameter processing, in particular to a method for optimizing a photoplethysmography signal.
Background
Photoplethysmography (PPG) is obtained by detecting a change in blood volume based on Beer-Lamber Law using a photoelectric means, and is called PPG for short. It contains a great deal of useful information related to physiological systems, and is widely applied to clinical blood vessel assessment and monitoring of physiological parameters such as cardiac output, blood pressure, respiratory rate, heart rate, blood oxygen saturation and the like.
After light with a fixed wavelength is vertically irradiated to the surface of the skin through the LED, the light intensity obtained from the photoelectric receiver is weakened due to the absorption of the light by the skin, muscle, blood, bone, and the like, the blood volume change caused by the pressure can be detected by measuring the light intensity change transmitted or reflected to the receiver, and then the blood volume change is drawn as a curve, namely, photoplethysmography (PPG).
The PPG signal acquired by the front-end hardware usually contains a lot of noise, which generally includes high-frequency noise caused by ambient light, dark current, power frequency interference, electromagnetic interference, myoelectric interference, etc., low-frequency baseline drift noise caused by physiological activities such as human respiration, and motion interference.
Disclosure of Invention
The invention mainly solves the technical problem that a photoplethysmography (PPG) signal in the prior art contains more noise, and provides a photoplethysmography optimization method for eliminating and reducing interference and noise.
The invention mainly solves the technical problems through the following technical scheme: a method for optimizing a photoplethysmography signal comprises the following steps:
s01, synchronously acquiring photoplethysmographic signals and motion state signals of the tested person;
s02, performing a first round of optimization on the pulse wave signals, namely judging whether the tested person is in a static state or a slow walking state or a violent movement state according to the movement state signals, and if the tested person is in the violent movement state, rejecting the pulse wave signals in a corresponding time period;
s03, denoising the pulse wave signals after the first round of optimization;
s04, extracting characteristic indexes of the de-noised pulse wave signals;
and S05, comparing the extracted characteristic indexes with a threshold range, and when at least one characteristic index falls outside the threshold range, marking the pulse wave period corresponding to the characteristic index as an abnormal period and removing the abnormal period to finally obtain the optimized pulse wave signal.
Preferably, in step S01, the motion state signal is obtained by a three-axis acceleration sensor fixed to the non-limb of the subject.
Preferably, in step S02, the step of determining whether the subject is in a strenuous exercise state according to the exercise state signal includes:
the resultant acceleration in three directions is calculated:
in the formula, atIndicates the resultant acceleration ax、ayAnd azRespectively representing acceleration values of the three-axis acceleration sensor in an X axis, a Y axis and a Z axis;
when the resultant acceleration is smaller than a first threshold upper limit and larger than a first threshold lower limit, the detected person is judged to be in a static state, the first threshold upper limit is 1.1g, the first threshold lower limit is 0.9g, and the signal is a normal signal at the moment;
when the resultant acceleration is smaller than a second threshold lower limit or larger than a second threshold upper limit, the detected person is judged to be in a violent motion state, the second threshold lower limit is 0.8g, the second threshold upper limit is 1.5g, and the signal is an abnormal signal and is discarded;
and when the combined acceleration is less than or equal to the first lower threshold and greater than or equal to the second lower threshold, or the combined acceleration is less than or equal to the second upper threshold and greater than the first upper threshold, determining that the detected person is in a slow walking state, and the signal is a secondary normal signal at the moment.
Because the coordinate system of the three-axis acceleration sensor can change along with the change of the posture of the testee, the judgment of the motion strength only by using the single-axis change can generate larger errors and cannot well reflect the motion state, and the acceleration of three axes can increase the complexity of the algorithm. Therefore, in order to better reflect the exercise intensity and make the calculation simpler, the scheme selects and uses the acceleration integrating three directions to judge the exercise intensity. The judgment of the normal signal, the sub-normal signal and the abnormal signal is completed at one time by adopting the two upper threshold limits and the two lower threshold limits, so that the subsequent processing of blood pressure estimation model construction and the like is facilitated.
Preferably, in step S03, the denoising processing performed on the pulse wave signal after the first round of optimization specifically includes:
s301, removing high-frequency noise through a dual-density wavelet threshold method, specifically:
decomposing the pulse wave signals after the first round of optimization through a filtering system to obtain a low-frequency coefficient and two high-frequency coefficients; each layer of filtering system comprises a low-pass filter h0(n) and two high-pass filters h1(n) and h2(n)；
The low-frequency coefficient passes through the filtering system again, and the process is repeated for three times so as to complete the three-layer decomposition of the dual-density wavelet;
processing the wavelet coefficients (i.e. the signals decomposed by the low and high frequency functions) by a threshold function;
carrying out inverse transformation on the processed wavelet coefficient, and reconstructing to obtain a denoised signal;
and S302, removing the baseline drift noise by a cubic spline interpolation method.
Preferably, the threshold function is:
x>0,sgn(x)＝1
x＝0,sgn(x)＝0
x<0,sgn(x)＝-1
in the formula, s is the processed wavelet coefficient, x is the unprocessed wavelet coefficient, and T is the denoising threshold.
Preferably, the denoising threshold T is determined by the following formula:
where σ is the estimated noise variance, N is the signal length, ω isbObtained by the following process:
squaring the wavelet coefficients of a certain layer, and then sequencing the wavelet coefficients from small to large to obtain a sequence W;
W＝[ω1,ω2,…ωN-1]
calculating the risk value of each element in W in turn, wherein the k element omegakThe risk value of (a) is:
k＝0,1,…N-1；
the minimum risk value obtained by calculating the wavelet coefficient of the layer is omegab；
The parameters θ and μ are obtained from the following equations:
θ＝(W-n)/n
preferably, the step S302 of removing the baseline wander noise by the cubic spline interpolation method specifically includes:
and identifying a valley point of the pulse wave signal through a findpeaks function, fitting through a cubic spline interpolation method to obtain a base drift of the section of signal, and finally subtracting the base drift from the pulse wave signal to obtain a signal with the base drift removed.
Preferably, in step S04, the extracting the feature index of the denoised pulse wave signal specifically includes:
s401, obtaining a local peak value of a pulse wave signal through a findpeaks function;
s402, if the time difference between the two local peak values is less than 0.5 second, deleting the point with the smaller amplitude; the rest points are the main wave peak points of each period;
s403, multiplying the main wave peak by-1 to obtain a starting point of a period where the main wave peak is located, and subtracting 1 from the starting point to obtain an end point of a previous period;
s404, calculating the characteristic index by taking the period as a unit, wherein the calculation formula is as follows:
H1＝Hmain wave peak point-HStarting point
H2＝HMain wave peak point-HEnd point
ST＝TMain wave peak point-TStarting point
DT＝TEnd point-TMain wave peak point
Wherein H1 is the rising amplitude of the period, H2 is the falling amplitude of the period, ST is the contraction time of the period, DT is the relaxation time of the period, K is the kurtosis of the period, xiRepresents the value of the current pulse wave signal,
represents the mean of the signal and std represents the standard deviation of the signal. H denotes a position point, and T denotes a time point.
Preferably, the threshold range in step S05 is determined by:
s501, selecting template data of a static state and a slow-walking state of a detected person with relatively low noise after the detected person is subjected to denoising algorithm processing;
s502, calculating characteristic index sequences of the template data, and respectively obtaining a median line of each characteristic index sequence by using median filtering with a window length of 50;
s503, for each characteristic index sequence, the upper limit threshold is the sum of the median line and the offset of the upper threshold, the lower limit threshold is the sum of the median line and the offset of the lower threshold, and the range between the upper limit threshold and the lower limit threshold is a threshold range; upper threshold offset is μseq+3σseqThe lower threshold offset is museq-3σseq，μseqIs a normal mean of the characteristic index sequence, 3 σseqIt is the normal standard deviation.
The invention has the substantial effects that abnormal signals seriously interfered by movement are removed, and the quality of PP signals is ensured; high-frequency noise is removed through practical dual-density wavelet transformation, baseline drift noise is removed through a cubic spline interpolation method, noise is effectively removed, and waveform characteristics are well kept.
Drawings
FIG. 1 is a flow chart of the present invention;
fig. 2 is a schematic diagram of a dual density wavelet transform of the present invention.
Detailed Description
The technical scheme of the invention is further specifically described by the following embodiments and the accompanying drawings.
Example (b): as shown in fig. 1, the method for optimizing a pulse wave signal of a photoplethysmography according to the present embodiment includes the following steps:
s01, synchronously acquiring photoplethysmographic signals and motion state signals of the tested person;
s02, performing a first round of optimization on the pulse wave signals, namely judging whether the tested person is in a static state or a slow walking state or a violent movement state according to the movement state signals, and if the tested person is in the violent movement state, rejecting the pulse wave signals in a corresponding time period;
s03, denoising the pulse wave signals after the first round of optimization;
s04, extracting characteristic indexes of the de-noised pulse wave signals;
and S05, comparing the extracted characteristic indexes with a threshold range, and when at least one characteristic index falls outside the threshold range, marking the pulse wave period corresponding to the characteristic index as an abnormal period and removing the abnormal period to finally obtain the optimized pulse wave signal.
In step S01, the motion state signal is obtained by a three-axis acceleration sensor fixed to the non-limb of the subject.
In step S02, the specific steps of determining whether the subject is in a strenuous exercise state according to the exercise state signal are: the resultant acceleration in three directions is calculated:
in the formula, atIndicates the resultant acceleration ax、ayAnd azRespectively representing acceleration values of the three-axis acceleration sensor in an X axis, a Y axis and a Z axis;
when the resultant acceleration is smaller than a first threshold upper limit and larger than a first threshold lower limit, the detected person is judged to be in a static state, the first threshold upper limit is 1.1g, the first threshold lower limit is 0.9g, and the signal is a normal signal at the moment;
when the resultant acceleration is smaller than a second threshold lower limit or larger than a second threshold upper limit, the detected person is judged to be in a violent motion state, the second threshold lower limit is 0.8g, the second threshold upper limit is 1.5g, and the signal is an abnormal signal and is discarded;
and when the combined acceleration is less than or equal to the first lower threshold and greater than or equal to the second lower threshold, or the combined acceleration is less than or equal to the second upper threshold and greater than the first upper threshold, determining that the detected person is in a slow walking state, and the signal is a secondary normal signal at the moment.
Because the coordinate system of the three-axis acceleration sensor can change along with the change of the posture of the testee, the judgment of the motion strength only by using the single-axis change can generate larger errors and cannot well reflect the motion state, and the acceleration of three axes can increase the complexity of the algorithm. Therefore, in order to better reflect the exercise intensity and make the calculation simpler, the scheme selects and uses the acceleration integrating three directions to judge the exercise intensity. The judgment of the normal signal, the sub-normal signal and the abnormal signal is completed at one time by adopting the two upper threshold limits and the two lower threshold limits, so that the subsequent processing of blood pressure estimation model construction and the like is facilitated.
In step S03, the denoising processing performed on the first-round optimized pulse wave signal specifically includes:
s301, removing high-frequency noise through a dual-density wavelet threshold method, specifically:
the pulse wave signals after the first round of optimization are decomposed by a filtering system to obtain a low frequency systemA number and two high frequency coefficients; each layer of filtering system comprises a low-pass filter h0(n) and two high-pass filters h1(n) and h2(n)；
The low-frequency coefficient passes through the filtering system again, and the process is repeated for three times so as to complete the three-layer decomposition of the dual-density wavelet; FIG. 2 is a schematic diagram of this process;
processing the wavelet coefficients (i.e. the signals decomposed by the low and high frequency functions) by a threshold function;
carrying out inverse transformation on the processed wavelet coefficient, and reconstructing to obtain a denoised signal;
and S302, removing the baseline drift noise by a cubic spline interpolation method.
The threshold function is:
x>0,sgn(x)＝1
x＝0,sgn(x)＝0
x<0,sgn(x)＝-1
in the formula, s is the processed wavelet coefficient, x is the unprocessed wavelet coefficient, and T is the denoising threshold.
The denoising threshold value T is determined by the following formula:
where σ is the estimated noise variance, N is the signal length, ω isbObtained by the following process:
squaring the wavelet coefficients of a certain layer, and then sequencing the wavelet coefficients from small to large to obtain a sequence W;
W＝[ω1,ω2,…ωN-1]
calculating the risk value of each element in W in turn, wherein the k element omegakThe risk value of (a) is:
k＝0,1,…N-1；
the minimum risk value obtained by calculating the wavelet coefficient of the layer is omegab；
The parameters θ and μ are obtained from the following equations:
θ＝(W-n)/n
the step S302 of removing the baseline wander noise by the cubic spline interpolation method specifically includes:
and identifying a valley point of the pulse wave signal through a findpeaks function, fitting through a cubic spline interpolation method to obtain a base drift of the section of signal, and finally subtracting the base drift from the pulse wave signal to obtain a signal with the base drift removed.
In step S04, the specific steps of extracting the feature index of the denoised pulse wave signal include:
s401, obtaining a local peak value of a pulse wave signal through a findpeaks function;
s402, if the time difference between the two local peak values is less than 0.5 second, deleting the point with the smaller amplitude; the rest points are the main wave peak points of each period;
s403, multiplying the main wave peak by-1 to obtain a starting point of a period where the main wave peak is located, and subtracting 1 from the starting point to obtain an end point of a previous period;
s404, calculating the characteristic index by taking the period as a unit, wherein the calculation formula is as follows:
H1＝Hmain wave peak point-HStarting point
H2＝HMain wave peak point-HEnd point
ST＝TMain wave peak point-TStarting point
DT＝TEnd point-TMain wave peak point
Wherein H1 is the rising amplitude of the period, H2 is the falling amplitude of the period, ST is the contraction time of the period, DT is the relaxation time of the period, K is the kurtosis of the period, xiRepresents the value of the current pulse wave signal,
represents the mean of the signal and std represents the standard deviation of the signal. H denotes a position point, and T denotes a time point.
The threshold range in step S05 is determined by:
s501, selecting template data of a static state and a slow-walking state of a detected person with relatively low noise after the detected person is subjected to denoising algorithm processing;
s502, calculating characteristic index sequences of the template data, and respectively obtaining a median line of each characteristic index sequence by using median filtering with a window length of 50;
s503, for each characteristic index sequence, the upper limit threshold is the sum of the median line and the offset of the upper threshold, the lower limit threshold is the sum of the median line and the offset of the lower threshold, and the range between the upper limit threshold and the lower limit threshold is a threshold range; upper threshold offset is μseq+3σseqThe lower threshold offset is museq-3σseq，μseqIs a normal mean of the characteristic index sequence, 3 σseqIt is the normal standard deviation.
The finally obtained optimized PPG signal can be used for blood pressure estimation and other purposes.
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.
Although terms such as dual density wavelet thresholding, feature index extraction, etc. are used more herein, the possibility of using other terms is not excluded. These terms are used merely to more conveniently describe and explain the nature of the present invention; they are to be construed as being without limitation to any additional limitations that may be imposed by the spirit of the present invention.
Claims (9)
1. A method for optimizing a photoplethysmography signal is characterized by comprising the following steps:
s01, synchronously acquiring photoplethysmographic signals and motion state signals of the tested person;
s02, performing a first round of optimization on the pulse wave signals, namely judging whether the tested person is in a static state or a slow walking state or a violent movement state according to the movement state signals, and if the tested person is in the violent movement state, rejecting the pulse wave signals in a corresponding time period;
s03, denoising the pulse wave signals after the first round of optimization;
s04, extracting characteristic indexes of the de-noised pulse wave signals;
and S05, comparing the extracted characteristic indexes with a threshold range, and when at least one characteristic index falls outside the threshold range, marking the pulse wave period corresponding to the characteristic index as an abnormal period and removing the abnormal period to finally obtain the optimized pulse wave signal.
2. The method for optimizing photoplethysmography signals according to claim 1, wherein in step S01, the motion status signal is obtained by a three-axis acceleration sensor fixed on the non-limb of the subject.
3. The method for optimizing photoplethysmography signals according to claim 2, wherein in step S02, the step of determining whether the subject is in a severe exercise state according to the exercise state signal includes: the resultant acceleration in three directions is calculated:
in the formula, atIndicates the resultant acceleration ax、ayAnd azRespectively representing acceleration values of the three-axis acceleration sensor in an X axis, a Y axis and a Z axis;
when the resultant acceleration is smaller than a first threshold upper limit and larger than a first threshold lower limit, the detected person is judged to be in a static state, the first threshold upper limit is 1.1g, and the first threshold lower limit is 0.9 g;
when the resultant acceleration is smaller than a second threshold lower limit or larger than a second threshold upper limit, the detected person is judged to be in a violent motion state, the second threshold lower limit is 0.8g, and the second threshold upper limit is 1.5 g;
and when the combined acceleration is less than or equal to the first lower threshold and greater than or equal to the second lower threshold, or the combined acceleration is less than or equal to the second upper threshold and greater than the first upper threshold, determining that the tested person is in a slow walking state.
4. The method for optimizing a photoplethysmographic signal according to claim 1, wherein in step S03, the denoising processing on the pulse wave signal after the first round of optimization specifically comprises:
s301, removing high-frequency noise through a dual-density wavelet threshold method, specifically:
decomposing the pulse wave signals after the first round of optimization through a filtering system to obtain a low-frequency coefficient and two high-frequency coefficients; each layer of filtering system comprises a low-pass filter h0(n) and two high-pass filters h1(n) and h2(n)；
The low-frequency coefficient passes through the filtering system again, and the process is repeated for three times so as to complete the three-layer decomposition of the dual-density wavelet;
processing the wavelet coefficient through a threshold function;
carrying out inverse transformation on the processed wavelet coefficient, and reconstructing to obtain a denoised signal;
and S302, removing the baseline drift noise by a cubic spline interpolation method.
5. The method for optimizing photoplethysmographic signals according to claim 4, wherein said threshold function is:
x>0,sgn(x)＝1
x＝0,sgn(x)＝0
x<0,sgn(x)＝-1
in the formula, s is the processed wavelet coefficient, x is the unprocessed wavelet coefficient, and T is the denoising threshold.
6. The method for optimizing photoplethysmographic signals according to claim 5, wherein said denoising threshold T is determined by the following formula:
where σ is the estimated noise variance, N is the signal length, ω isbObtained by the following process:
squaring the wavelet coefficients of a certain layer, and then sequencing the wavelet coefficients from small to large to obtain a sequence W;
W＝[ω1,ω2,…ωN-1]
calculating the risk value of each element in W in turn, wherein the k element omegakThe risk value of (a) is:
the minimum risk value obtained by calculating the wavelet coefficient of the layer is omegab；
The parameters θ and μ are obtained from the following equations:
θ＝(W-n)/n
7. the method for optimizing a photoplethysmographic signal according to claim 4, 5 or 6, wherein the step S302 of removing the baseline wander noise by cubic spline interpolation specifically comprises:
and identifying a valley point of the pulse wave signal through a findpeaks function, fitting through a cubic spline interpolation method to obtain a base drift of the section of signal, and finally subtracting the base drift from the pulse wave signal to obtain a signal with the base drift removed.
8. The method for optimizing a photoplethysmographic signal according to claim 7, wherein in step S04, the extracting the characteristic index of the denoised sphygographic signal specifically comprises:
s401, obtaining a local peak value of a pulse wave signal through a findpeaks function;
s402, if the time difference between the two local peak values is less than 0.5 second, deleting the point with the smaller amplitude; the rest points are the main wave peak points of each period;
s403, multiplying the main wave peak by-1 to obtain a starting point of a period where the main wave peak is located, and subtracting 1 from the starting point to obtain an end point of a previous period;
s404, calculating the characteristic index by taking the period as a unit, wherein the calculation formula is as follows:
H1＝Hmain wave peak point-HStarting point
H2＝HMain wave peak point-HEnd point
ST＝TMain wave peak point-TStarting point
DT＝TEnd point-TMain wave peak point
Wherein H1 is the rising amplitude of the period, H2 is the falling amplitude of the period, ST is the contraction time of the period, DT is the relaxation time of the period, K is the kurtosis of the period, xiRepresents the value of the current pulse wave signal,
represents the mean of the signal and std represents the standard deviation of the signal.
9. The photoplethysmography signal optimization method of claim 8, wherein the threshold range is determined by:
s501, selecting template data of a static state and a slow-walking state of a detected person with relatively low noise after the detected person is subjected to denoising algorithm processing;
s502, calculating characteristic index sequences of the template data, and respectively obtaining a median line of each characteristic index sequence by using median filtering with a window length of 50;
s503, for each characteristic index sequence, the upper limit threshold is the sum of the median line and the offset of the upper threshold, the lower limit threshold is the sum of the median line and the offset of the lower threshold, and the range between the upper limit threshold and the lower limit threshold is a threshold range; upper threshold offset is μseq+3σseqThe lower threshold offset is museq-3σseq，μseqIs a normal mean of the characteristic index sequence, 3 σseqIt is the normal standard deviation.
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