CN114530246A - Physiological characteristic signal processing method, electronic device, chip and readable storage medium - Google Patents
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Abstract
The embodiment of the application provides a physiological characteristic signal processing method, and relates to the field of electronic equipment. The method comprises the steps of obtaining a physiological characteristic optimization signal by filtering an acquired original physiological characteristic signal, extracting peak points in the physiological characteristic optimization signal by using a preset peak value extraction algorithm to obtain a first peak point set, carrying out secondary peak extraction optimization processing on the first peak point set based on the original physiological characteristic signal to obtain a second peak point set, and analyzing the second peak point set and the physiological characteristic optimization signal to obtain the physiological characteristics of a target individual. The embodiment of the application also provides electronic equipment, a chip and a computer readable storage medium. According to the method and the device, a secondary peak lifting mechanism is introduced, physiological characteristic data can be collected when the user is in an active state, user experience is improved, and real-time monitoring of the physiological characteristics of the user is achieved.
Description
Technical Field
The present application relates to the field of terminal technologies, and in particular, to a physiological characteristic signal processing method, an electronic device, a chip, and a computer-readable storage medium.
Background
With the emphasis on health, electronic devices with physiological characteristic function measurement are increasingly popular, for example, wearable devices can measure heart rate, blood oxygen, exercise, sleep and other physical signs by collecting photoplethysmography (PPG) of a human body. In order to ensure the accuracy of each measurement, as shown in fig. 1, the signal collected by the wearable device may be subjected to signal filtering, signal peak-lifting, signal quality detection, physical sign measurement, and other processing procedures, in which the signal quality may be evaluated, and once the signal quality is evaluated to be poor, the section of signal collected by the wearable device will not be used for the subsequent physical sign measurement.
The functions realized based on the wearable device are more and more complex, for example, the functions of detecting or even predicting heart problems such as atrial fibrillation and premature beat are more strict on the quality of signals acquired by the wearable device, and sometimes the user is required to keep a static state for a period of time to measure successfully. When a user is in an active state (for example, a walking state), the quality of signals collected by the wearable device is often poor, and the requirement on the software and hardware signal processing capability is relatively high, so that the measurement function of the wearable device cannot be normally used in most active states. Because the interference generated by the user in the active state is dynamically changed, the interference generated by the same action of different users is different, even the interference generated by the same user repeating the same action may be different, and the interference in the active state does not follow a fixed change rule. In order to improve the signal quality, the existing methods generally improve on hardware such as a sensor, or perform algorithm optimization in the stages of signal filtering and signal peak-lifting, but the interference-removing effects of the methods are not obvious.
Disclosure of Invention
In view of the above, there is a need to provide a method for processing physiological characteristic signals, which can overcome the above problems, collect data and perform physical sign measurement when a user is in an active state, and improve user experience.
The embodiment of the application discloses a physiological characteristic signal processing method in a first aspect, which comprises the following steps: filtering the acquired physiological characteristic signal of the target individual to obtain a physiological characteristic optimization signal; extracting peak points in the physiological characteristic optimization signal by using a preset peak extraction algorithm, and constructing a first peak set based on the extracted peak points; optimizing the first peak point set based on the physiological characteristic signal to obtain a second peak point set, wherein the optimizing includes one or more of adding a peak point, deleting the peak point and updating the peak point; and analyzing and obtaining the physiological characteristics of the target individual based on the second peak point set and the physiological characteristic optimization signal.
By adopting the technical scheme, the physiological characteristic data can be acquired and processed when the user is in an active state, the measurement accuracy is high, the user experience is improved, and the real-time monitoring of the physiological characteristics of the user is realized.
In a possible implementation manner, the extracting peak points in the physiological characteristic optimization signal by using a preset peak extraction algorithm, and constructing a first peak set based on the extracted peak points includes: extracting peak points in the physiological characteristic optimization signal by using the preset peak extraction algorithm, and constructing the first peak point set based on the extracted peak points; or extracting the valley points in the physiological characteristic optimization signal by using the preset peak value extraction algorithm, and constructing the first peak value set based on the extracted valley points.
By adopting the technical scheme, the peak point set can be constructed by only using the peak points or the valley points, and the signal processing operation amount is reduced.
In one possible implementation, the optimizing the first peak point set based on the physiological characteristic signal includes: modeling the physiological characteristic signal to obtain a physiological characteristic waveform corresponding to the physiological characteristic signal; splitting the physiological characteristic waveform into an ascending waveform section and a descending waveform section, and selecting the ascending waveform section or the descending waveform section as a target waveform; marking each peak point in the first peak point set on the target waveform; dividing the target waveform into a plurality of waveform windows, and calculating according to a preset interference degree calculation algorithm to obtain an initial interference degree of each waveform window; optimizing the peak point of the waveform window, recalculating the interference degree of the waveform window after optimization processing by using the preset interference degree calculation algorithm until the interference degree of the waveform window obtains the minimum value, and finishing the optimization processing of the waveform window; and summarizing the peak points contained in each waveform window after the optimization processing is completed to obtain the second peak point set.
By adopting the technical scheme, the interference degree of each waveform window obtains the minimum value by performing addition, deletion, modification and optimization processing on the first peak point set, and finally a more accurate peak point set is obtained.
In a possible implementation manner, after selecting the ascending waveform or the descending waveform as the target waveform, the method further includes: and simplifying the curve segment in the target waveform into a straight line segment only comprising head and tail end points.
By adopting the technical scheme, the curve segment in the target waveform can be simplified into a straight-line segment, and the calculation amount of subsequent interference degree calculation is reduced.
In a possible implementation manner, the preset interference calculation algorithm includes: calculating the slope distance between any two straight line segments marked with the peak point in the waveform window, and carrying out normalization processing on the calculated slope distance; calculating the length ratio between any two straight line segments marked with the peak points in the waveform window, and carrying out normalization processing on the calculated length ratio; calculating the absolute value of the transverse distance difference between any two straight line segments marked with the peak point in the waveform window, and normalizing the absolute value of the transverse distance difference obtained through calculation; calculating the absolute value of the longitudinal distance difference between any two straight line segments marked with the peak point in the waveform window, and normalizing the absolute value of the calculated longitudinal distance difference; and obtaining the interference degree of the waveform window based on the normalization result of the slope distance, the normalization result of the length ratio, the normalization result of the absolute value of the transverse distance difference and the normalization result of the absolute value of the longitudinal distance difference.
By adopting the technical scheme, the interference degree of the waveform window can be calculated and obtained based on four dimensions of the slope distance, the length ratio, the transverse distance difference and the longitudinal distance difference.
In a possible implementation manner, the normalizing the calculated slope distance includes: respectively carrying out normalization processing on the plurality of slope distances obtained by calculation, and summarizing the normalization result of each slope distance; or accumulating the plurality of slope distances obtained by calculation to obtain a total slope distance, and carrying out normalization processing on the total slope distance.
By adopting the technical scheme, the slope distance can be normalized so as to convert the slope distance into the interference degree.
In a possible implementation manner, a longer straight-line segment of the two straight-line segments is a denominator of the length ratio, and the normalizing the calculated length ratio includes: respectively carrying out normalization processing on a plurality of length ratios obtained by calculation, and summarizing the normalization result of each length ratio; or accumulating the plurality of calculated length ratios to obtain a total length ratio, and carrying out normalization processing on the total length ratio.
By adopting the technical scheme, the length ratio can be normalized so as to convert the length ratio into the interference degree.
In one possible implementation, the normalizing the absolute value of the calculated lateral distance difference includes: averaging the absolute values of the plurality of calculated transverse distance differences to obtain an average transverse distance difference; and respectively normalizing the absolute value of each transverse distance difference obtained by calculation based on the average transverse distance difference, and summarizing the normalization result of the absolute value of each transverse distance difference.
By adopting the technical scheme, the absolute value of the transverse distance difference can be normalized so as to be converted into the interference degree.
In a possible implementation manner, the normalizing the absolute value of the calculated longitudinal distance difference includes: carrying out averaging operation on the absolute values of the plurality of longitudinal distance differences obtained by calculation to obtain an average longitudinal distance difference; and respectively normalizing the absolute value of each longitudinal distance difference obtained by calculation based on the average longitudinal distance difference, and summarizing the normalization result of the absolute value of each longitudinal distance difference.
By adopting the technical scheme, the normalization processing of the absolute value of the longitudinal distance difference can be realized, so that the absolute value of the longitudinal distance difference can be converted into the interference degree.
In a possible implementation manner, the optimizing the peak point of the waveform window includes: and searching for an area with abnormal slope distance change, abnormal length ratio change, abnormal transverse distance difference change or abnormal longitudinal distance difference change in the waveform window, and optimizing the peak point in the area.
By adopting the technical scheme, the area which is possibly required to be subjected to peak point optimization can be quickly positioned, and the optimization time is saved.
In a possible implementation manner, the optimizing the peak point of the waveform window includes: and when the initial interference degree of the waveform window is greater than or equal to a preset interference degree, optimizing the peak point of the waveform window.
By adopting the technical scheme, the optimization attempt of the waveform window with the space for improving the signal quality can be realized, and the optimization efficiency is improved.
In one possible implementation, the method further includes: and when the initial interference degree of the waveform window is smaller than the preset interference degree, giving up the optimization processing on the peak point of the waveform window.
By adopting the technical scheme, invalid optimization attempt on the waveform window with poor signal quality can be avoided, and the optimization time is saved.
In a possible implementation manner, the obtaining of the physiological characteristic of the target individual based on the second peak point set and the physiological characteristic optimization signal analysis includes: performing signal quality evaluation on the second peak point set and the physiological characteristic optimization signal; and when the signal quality evaluation result is that the signal quality is good, optimizing signal analysis based on the second peak point set and the physiological characteristics to obtain the physiological characteristics of the target individual.
By adopting the technical scheme, the signal quality can be evaluated, and only the signal evaluated as the signal quality number can be used for carrying out the subsequent physical sign measurement.
In a second aspect, embodiments of the present application provide a computer-readable storage medium, which includes computer instructions, when the computer instructions are executed on an electronic device, cause the electronic device to perform the physiological characteristic signal processing method according to the first aspect.
In a third aspect, an embodiment of the present application provides an electronic device, which includes a processor and a memory, where the memory is used to store instructions, and the processor is used to call the instructions in the memory, so that the electronic device executes the physiological characteristic signal processing method according to the first aspect.
In a fourth aspect, embodiments of the present application provide a computer program product, which when run on a computer, causes the computer to execute the physiological characteristic signal processing method according to the first aspect.
In a fifth aspect, an embodiment of the present application provides an apparatus having a function of implementing the behavior of the electronic device in the method provided in the first aspect. The functions may be implemented by hardware, or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above-described functions.
It is to be understood that the computer-readable storage medium of the second aspect, the electronic device of the third aspect, the computer program product of the fourth aspect, and the apparatus of the fifth aspect all correspond to the method of the first aspect, and therefore, the beneficial effects achieved by the apparatus can refer to the beneficial effects of the corresponding methods provided above, and are not repeated herein.
Drawings
Fig. 1 is a schematic flow chart of physiological characteristic signal processing performed by a conventional wearable device;
fig. 2 is a schematic flowchart of a physiological characteristic signal processing method according to an embodiment of the present application;
fig. 3 is a schematic waveform diagram of a segment of PPG signals detected by an electronic device according to an embodiment of the present disclosure;
fig. 4 is a schematic waveform diagram of the PPG signal of fig. 3 retaining only the downhill waveform segment and marked with a first set of peaks;
FIG. 5 is a waveform illustrating the curve segment of the downhill waveform segment of FIG. 4 simplified to include only a straight line segment at the beginning and end points;
fig. 6 is a schematic flowchart of a physiological characteristic signal processing method according to another embodiment of the present application;
fig. 7 is a schematic structural diagram of a possible electronic device according to an embodiment of the present disclosure.
Detailed Description
In the present application, "at least one" means one or more, "and" a plurality "means two or more. "and/or" describes an association relationship of associated objects, meaning that three relationships may exist, e.g., A and/or B may represent: a exists alone, A and B exist simultaneously, and B exists alone, wherein A and B can be singular or plural. The terms "first," "second," "third," "fourth," and the like in the description and in the claims and drawings of the present application, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
For ease of understanding, some descriptions of concepts related to the embodiments of the present application are given by way of illustration and reference.
Referring to fig. 2, the physiological characteristic signal processing method provided in the embodiment of the present application is applied to an electronic device 100, and the electronic device 100 may be a smart watch, a smart bracelet, a physical sign measuring instrument, and other devices having a physiological characteristic measuring function. In this embodiment, the physiological characteristic signal processing method may include:
21. and filtering the acquired physiological characteristic signals of the target individual to obtain physiological characteristic optimized signals.
In some embodiments, the collected physiological characteristic signal is filtered by using a preset filtering method, so that some noise contained in the physiological characteristic signal can be filtered out, and a physiological characteristic optimized signal can be obtained. The preset filtering method may select an existing sign signal filtering algorithm according to actual requirements, for example, the preset filtering method may be a wavelet decomposition algorithm, a frequency domain analysis algorithm, a modal decomposition algorithm (e.g., empirical modal decomposition), an independent component analysis algorithm, a self-adaptive filtering algorithm, or the like.
For example, the physiological characteristic signal is a PPG signal, the target individual is a wearing user of the electronic device 100, and the filtered and optimized PPG signal is obtained by filtering the PPG signal of the target individual acquired by the electronic device 100.
22. And extracting peak points in the physiological characteristic optimization signal, and constructing a first peak point set based on the extracted peak points.
In some embodiments, a preset peak extraction algorithm may also be used to extract a peak point in the physiological characteristic optimization signal, where the peak point may be a peak point or a valley point, and the extracted peak points may be put into a set, so as to obtain a first peak point set. That is, a preset peak extracting algorithm may be used to extract a peak point in the physiological characteristic optimized signal and construct the first peak point set based on the extracted peak point, or a preset peak extracting algorithm may be used to extract a valley point in the physiological characteristic optimized signal and construct the first peak point set based on the extracted valley point. The peak point is exemplified as the peak point.
The preset peak-extracting algorithm can also select the existing sign signal peak-extracting algorithm according to the actual requirement, for example, a bayesian decision classification algorithm, a machine learning classification algorithm, a heuristic algorithm, and the like can be used.
23. And optimizing the first peak point set based on the physiological characteristic signal to obtain a second peak point set.
In some embodiments, the optimization process may include a combination of one or more of adding a peak point process, removing a peak point process, and updating a peak point process. The adding of the peak point may be adding a peak point to the first peak point set, the removing of the peak point may be deleting a peak point from the first peak point set, and the updating of the peak point may be deleting a peak point from the first peak point set and adding a peak point to the first peak point set at the same time.
In some embodiments, the physiological characteristic signal is exemplified as a PPG signal. The PPG signal acquired by the electronic device 100 may be modeled, the PPG signal is converted into a physiological characteristic waveform S1 shown in fig. 3 (a horizontal axis of the physiological characteristic waveform S1 may use time as a dimension, and a vertical axis may use light intensity detected by an optical heart rate sensor of the electronic device 100 as a dimension), the physiological characteristic waveform is split into an ascending waveform segment S11 and a descending waveform segment S12, and then one waveform segment may be arbitrarily selected as a target waveform for subsequent analysis. The following description will be given by taking the example of selecting the downhill waveform segment S12 as the target waveform.
The downhill waveform segment S12 in the physiological signature S1 shown in fig. 3 is selected to obtain the waveform diagram shown in fig. 4. Further, each peak point in the first set of peak points may be labeled on the target waveform shown in fig. 4. Because the target waveform contains more waveform segments, in order to accelerate the signal analysis speed, the target waveform can be divided into a plurality of waveform windows, and then the interference degree of each waveform window is calculated by utilizing a preset interference degree calculation algorithm.
In some embodiments, the target waveform may be sliced into a plurality of waveform windows in a time-slicing dimension, each waveform window containing the same time scale, such as each waveform window containing a 1 second downhill waveform segment. The target waveform may also be sliced into a plurality of waveform windows with the number of waveform segments as a slicing dimension, each waveform window containing the same number of waveform segments, for example, each waveform window containing 30 downhill waveform segments.
In some embodiments, in order to reduce the calculation amount of the interference degree, the curve segment in each waveform window may be simplified to include only straight line segments at the first and last end points, and then the interference degree calculation is performed. As shown in fig. 5, the curve segment shown in fig. 4 is simplified to include only straight line segments at the beginning and end points. Calculating the interference degree of the waveform window using a preset interference degree calculation algorithm may include: a. calculating the slope distance between any two straight line segments marked with the peak point in the waveform window (namely the included angle between any two straight line segments marked with the peak point), and carrying out normalization processing on the calculated slope distance; b. calculating the length ratio between any two straight line segments marked with the peak point in the waveform window (the length value of the longer straight line segment is the denominator of the length ratio), and performing normalization processing on the calculated length ratio; c. calculating the absolute value of the transverse distance difference between any two straight line segments marked with peak points in the waveform window, and normalizing the absolute value of the transverse distance difference obtained by calculation; d. calculating the absolute value of the longitudinal distance difference between any two straight line segments marked with the peak point in the waveform window, and normalizing the absolute value of the longitudinal distance difference obtained through calculation; e. and calculating the interference degree of the waveform window based on the normalization result of the slope distance, the normalization result of the length ratio, the normalization result of the absolute value of the transverse distance difference and the normalization result of the absolute value of the longitudinal distance difference.
In some embodiments, the greater the value of the slope distance, the greater the result of performing the normalization process. The normalization processing of the calculated slope distances may refer to performing normalization processing on the calculated slope distances respectively (the closer the value of the slope distance is to 0 °, the smaller the result obtained by performing the normalization processing is, the closer the value of the slope distance is to 90 °, the larger the result obtained by performing the normalization processing is), converting to obtain corresponding interference degrees, and then summarizing the normalization results, or may refer to performing the normalization processing on the total slope distances by accumulating the calculated slope distances to obtain the total slope distances, and then performing the normalization processing on the total slope distances to convert to obtain the corresponding interference degrees.
In some embodiments, the smaller the value of the length ratio, the greater the result of performing the normalization process. The normalization processing of the calculated length ratios may refer to performing normalization processing on the calculated length ratios respectively (the closer the value of the length ratio is to 0, the larger the result obtained by performing the normalization processing is, the closer the value of the length ratio is to 1, and the smaller the result obtained by performing the normalization processing is), converting to obtain corresponding interference degrees, and then summarizing the normalization results, or may refer to performing accumulation on the calculated slope distances to obtain total slope distances, and then performing normalization processing on the total slope distances to obtain corresponding interference degrees.
In some embodiments, normalizing the absolute values of the calculated lateral distance differences may include: the method comprises the steps of firstly carrying out averaging operation on absolute values of a plurality of transverse distance differences obtained through calculation to obtain an average transverse distance difference of a waveform window, then respectively carrying out normalization processing on the absolute values of the plurality of transverse distance differences obtained through calculation (the smaller the absolute value of the transverse distance difference is close to the average transverse distance difference, the smaller the result obtained through normalization processing is, the larger the result obtained through normalization processing is, the more the absolute value of the transverse distance difference deviates from the average transverse distance difference), converting to obtain corresponding interference degree, and then summarizing the normalization results.
In some embodiments, normalizing the absolute value of the computed longitudinal distance difference may include: the method comprises the steps of firstly carrying out averaging operation on absolute values of a plurality of longitudinal distance differences obtained through calculation to obtain an average longitudinal distance difference of a waveform window, then respectively carrying out normalization processing on the absolute values of the plurality of longitudinal distance differences obtained through calculation (the more the absolute value of the longitudinal distance difference is close to the average longitudinal distance difference, the smaller the result obtained through normalization processing is, the more the absolute value of the longitudinal distance difference deviates from the average longitudinal distance difference, the larger the result obtained through normalization processing is), converting to obtain corresponding interference degrees, and then summarizing the normalization results.
In the process of calculating the interference degree of the waveform window, the interference degree of the waveform window is divided into four dimensions of a slope distance, a length ratio, a transverse distance difference and a longitudinal distance difference to be calculated and normalized, the interference degree of each dimension is obtained through conversion, and then the normalization result of the slope distance, the normalization result of the length ratio, the normalization result of the absolute value of the transverse distance difference and the normalization result of the absolute value of the longitudinal distance difference are accumulated, so that the interference degree of the waveform window can be obtained.
In some embodiments, when the initial interference level of each waveform window is calculated, it may be determined whether the initial interference level is greater than a preset interference level, so as to determine whether it is necessary to adjust the peak point in the waveform window. When the initial interference degree of the waveform window is greater than the preset interference degree, the quality of the peak point in the waveform window is poor, and the existing signal quality verification cannot be passed even through subsequent optimization processing. When the initial interference degree of the waveform window is larger than or smaller than the preset interference degree, the peak point quality in the waveform window is indicated to have an adjustable space, and the existing signal quality check can possibly be passed through by the subsequent optimization processing of the method. The preset interference degree can be set according to actual requirements.
In some embodiments, when the optimization process is attempted on the peak point of the waveform window, the interference degree of the waveform window after the optimization process may be recalculated by using the preset interference degree calculation algorithm, and the optimization process on the waveform window may be stopped by continuously attempting to adjust and continuously repeatedly calculating the interference degree until the interference degree of the waveform window reaches the minimum value. After each waveform window is optimized, the peak points currently included in each waveform window after the optimization can be summarized, and a second peak point set is constructed.
As shown in fig. 5, the attempt to optimize the peak point of the waveform window may be to add a peak point to a straight line segment (the straight line segment is not marked with a peak point previously), to delete a peak point marked on a straight line segment, or to delete a peak point marked on a straight line segment first, and then to add a peak point on another straight line segment (the waveform segment is not marked with a peak point previously).
In some embodiments, since the human body feature changes generally follow an approximately linear change principle, the sudden changes generally do not span large distances. For example, the slope distance, length ratio, lateral distance difference and longitudinal distance difference of several adjacent straight line segments marked with peak points are analyzed, if a certain value is found to change greatly suddenly, it may be necessary to perform optimization processing on the peak point of this region, try to perform processing of adding and/or deleting peak points, and recalculate the interference degree, so as to try to minimize the interference degree of the waveform window, and save the optimization processing time.
In some embodiments, the number of attempts of the optimization process may be limited, and after the preset number of optimization processes is completed, the waveform state with the minimum interference degree is selected.
In some embodiments, a polling adjustment manner may also be adopted to try to perform peak point adding and/or deleting operations on each straight-line segment in the waveform window, and recalculate the interference degree of the waveform window until the interference degree of the waveform window obtains the minimum value, where the processing time is relatively long compared to the above-described optimization processing manner.
24. And analyzing and obtaining the physiological characteristics of the target individual based on the second peak point set and the physiological characteristic optimization signal.
In some embodiments, when the second peak set is obtained, the second peak set and the physiological characteristic optimization signal can be analyzed by using an existing sign measurement analysis method (e.g., the signal quality detection and sign measurement step shown in fig. 1) to obtain the physiological characteristic of the target individual. Such as a physiological characteristic being heart rate, blood pressure, etc.
For example, for heart rate measurement, a heart rate value is obtained by conversion based on the number of peak points in a certain time. If the number of peak points of the physiological characteristic optimization signal obtained by analysis for 5s is N, the heart rate is N x 12.
In some embodiments, the signal quality evaluation may be performed on the second peak point set and the physiological characteristic optimized signal, and if the evaluation result is that the signal quality is poor, the signal segment will not be used for performing subsequent physical sign measurement, and the signal processing is directly ended. If the evaluation result is that the signal quality is good, the section of signal is used for measuring the physical sign, and the second peak point set and the physiological characteristic optimization signal can be analyzed by adopting the existing physical sign analysis method to obtain the physiological characteristic of the target individual.
According to the physiological characteristic signal processing method, the initial peak point set is obtained by utilizing the existing filtering and peak extracting technology, the initial peak point set is optimized by utilizing a secondary peak extracting mechanism to obtain the final peak point set, data can be acquired when a user is in an active state, the user does not need to be deliberately kept in a static state for a long time, the applicable scene of physical sign measurement is increased, the user experience is improved, the real-time monitoring of the physiological characteristics of the user is really realized, and the application scene of wearable equipment can be improved.
Referring to fig. 6, an embodiment of the present application provides a flowchart illustrating a process of implementing physiological characteristic measurement on a target individual by an electronic device 100.
61. And collecting signals of the wearable equipment. The target individual wears the electronic device 100, and the electronic device 100 can acquire the original physiological characteristic signal.
62. And (5) signal filtering processing. The original physiological characteristic signal can be filtered by adopting the existing filtering algorithm to obtain the physiological characteristic optimized signal.
63. And (5) signal peak extraction processing. The peak point extraction operation can be performed on the physiological characteristic optimization signal obtained through filtering processing by adopting the existing peak extraction algorithm to obtain a first peak point set. The peak extraction mode can be to extract only the peak point or only the valley point.
64. And (5) secondary peak extraction treatment. Modeling is carried out on the original physiological characteristic signal, a waveform obtained through modeling is divided into an ascending waveform section and a descending waveform section, then one waveform section is selected randomly to serve as a target waveform for subsequent analysis, peak points contained in the first peak point set are marked on the target waveform, and interference degree calculation is carried out. The interference degree minimization is realized by trying to add peak points, delete peak points and update peak points to the target waveform, and then a second peak point set is constructed based on the peak points in the state of the minimum interference degree.
65. And detecting the signal quality. And evaluating the second peak point set and the physiological characteristic optimization signal by using the existing signal quality detection mode, and if the evaluation result is poor signal quality, directly finishing signal processing without using the section of signal for subsequent physical sign measurement.
66. And measuring physical signs. When the second peak point set and the physiological characteristic optimized signal are evaluated by using the existing signal quality detection method, if the evaluation result is that the signal quality is good, the signal of the section is used for performing physical sign measurement, and the second peak point set and the physiological characteristic optimized signal can be analyzed by using the existing physical sign analysis method to obtain the physiological characteristic of the target individual.
The following description will compare the experimental data for physiological characteristic measurement using the conventional physiological characteristic signal processing method (the method shown in fig. 1) with the experimental data for physiological characteristic measurement using the physiological characteristic signal processing method shown in fig. 6 of the present application.
In order to ensure the accuracy of atrial fibrillation detection in the prior art, the screening of signal quality is strict, and signals acquired in a plurality of active states cannot pass the verification of the signal quality, so that the atrial fibrillation detection function generally requires a user to keep still for about 1 minute before atrial fibrillation detection is performed.
Experiment one:
the experiment no longer enforces the requirement that the collected user remain stationary, and therefore some users may collect signal data in an active state (such as walking). Sample 1: acquiring 9899 sections of PPG signals without atrial fibrillation in static and active states, wherein each section of signals lasts for about 1 minute; sample 2: segments 6740 of PPG signals with episodes of atrial fibrillation are acquired in both resting and active states, each segment lasting about 1 minute.
Processing the sample 1 and the sample 2 by the existing physiological characteristic signal processing method, wherein in the collected 9899 sections of atrial fibrillation-free PPG signals, 6891 sections of signals pass the existing signal quality inspection, most of the signals can be considered to be collected in a static state, and the rest 3008 sections of signals do not pass the existing signal quality inspection, and most of the signals can be considered to be collected in an active state; of the 6740 collected atrial fibrillation-free PPG signals, 3031 signals pass the existing signal quality check, most of the signals are considered to be collected in a static state, and the rest 3709 signals do not pass the existing signal quality check, and most of the signals are considered to be collected in an active state. That is, using the existing physiological characteristic signal processing method, about 6717(3008+3709) segments of PPG signal data cannot pass signal quality verification, and in the data passing signal quality verification, the accuracy of atrial fibrillation measurement is above 95%.
When the physiological characteristic signal processing method shown in fig. 6 of the present application is used to process samples 1 and 2, a signal that does not pass the signal quality check in the previous 3008 period and a signal that passes the signal quality check in the previous 1933 period, a signal that does not pass the signal quality check in the previous 1075 period, a signal that passes the signal quality check in the previous 3709 period and a signal that passes the signal quality check in the previous 1995 period, and a signal that does not pass the signal quality check in the previous 1714 period, that is, the number of PPG signals that cannot pass the signal quality verification is reduced from 6717 period to 2789 period (1075+1714), half or more of the signal data acquired in the active state is recalled, and the accuracy of the atrial fibrillation measurement is still maintained at 95% or more.
Experiment two:
and carrying out atrial fibrillation early warning on the user in a vehicle-mounted (driving or riding) state. The early warning function of current atrial fibrillation generally can not be used under the on-vehicle state at the user, and PPG signal data under the on-vehicle scene has been gathered in this experiment, and in order to guarantee patient safety, do not gather atrial fibrillation patient's on-vehicle data, therefore the sample of reality all is the negative sample, examines the atrial fibrillation false alarm rate (jolting under the on-vehicle state can make the signal fluctuate, makes and produces the wrong report easily in the measuring result of current electronic equipment). Sample 1: acquiring 343 sections of PPG from the left hand of a driver in a vehicle-mounted scene; sample 2: collecting 343 sections of PPG from the right hand of a driver under a vehicle-mounted scene; sample 3: 49 sections of PPG of the driver are acquired in a static scene. That is, a total of 735(343+343+49) PPG segments are acquired, each segment lasting around 1 minute.
In the collected 735 segments of atrial fibrillation-free PPG signal, it can be considered that most of the signals are collected in the active state. When the PPG signals are processed by adopting the existing physiological characteristic signal processing method, 698 sections of PPG signals cannot pass through signal quality verification, 37 sections of PPG signals pass through signal quality verification, and the 37 sections (37/735 is 80%) of PPG signals are analyzed, so that false alarm does not occur in the tremor early warning module.
When the physiological characteristic signal processing method shown in fig. 6 of the application is used, 588 sections (588/735 ≈ 5%) of PPG signals pass signal quality verification, the quantity of the PPG signals which cannot pass signal quality verification is reduced from 698 sections to 147 sections, the recall rate of the PPG signals acquired in an active state is improved by more than ten times (the signal quality passing rate is improved from 5% to 80%), and the 588 sections of PPG signals are analyzed, so that the atrial fibrillation early warning module still has no false alarm.
Fig. 7 is a schematic diagram of a hardware structure of the electronic device 100 according to an embodiment of the present disclosure. As shown in fig. 7, electronic device 100 may include a processor 1001, a memory 1002, and a communication bus 1003. The memory 1002 is used to store one or more computer programs 1004. One or more computer programs 1004 are configured to be executed by the processor 1001. The one or more computer programs 1004 include instructions that may be used to implement the physiological characteristic signal processing method described above in the electronic device 100.
It is to be understood that the illustrated structure of the present embodiment does not constitute a specific limitation to the electronic apparatus 100. In other embodiments, electronic device 100 may include more or fewer components than shown, or combine certain components, or split certain components, or a different arrangement of components.
The processor 1001 may also be provided with a memory for storing instructions and data. In some embodiments, the memory in the processor 1001 is a cache memory. The memory may hold instructions or data that have just been used or recycled by the processor 1001. If the processor 1001 needs to reuse the instruction or data, it can be called directly from the memory. Avoiding repeated accesses reduces the latency of the processor 1001, thereby increasing the efficiency of the system.
In some embodiments, the processor 1001 may include one or more interfaces. The interface may include an integrated circuit (I2C) interface, an integrated circuit built-in audio (I2S) interface, a Pulse Code Modulation (PCM) interface, a universal asynchronous receiver/transmitter (UART) interface, a mobile industry processor interface (mobile industry processor interface, MIPI), a general-purpose-microprocessor input/output (GPIO) interface, a SIM interface, and/or a USB interface, etc.
In some embodiments, the memory 1002 may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The present embodiment also provides a computer storage medium, in which computer instructions are stored, and when the computer instructions are run on an electronic device, the electronic device executes the above related method steps to implement the physiological characteristic signal processing method in the above embodiment.
The present embodiment also provides a computer program product, which when run on a computer, causes the computer to execute the relevant steps described above, so as to implement the physiological characteristic signal processing method in the above embodiments.
In addition, embodiments of the present application also provide an apparatus, which may be specifically a chip, a component or a module, and may include a processor and a memory connected to each other; the memory is used for storing computer execution instructions, and when the device runs, the processor can execute the computer execution instructions stored in the memory, so that the chip can execute the physiological characteristic signal processing method in the above-mentioned method embodiments.
The first electronic device, the computer storage medium, the computer program product, or the chip provided in this embodiment are all configured to execute the corresponding method provided above, so that the beneficial effects achieved by the first electronic device, the computer storage medium, the computer program product, or the chip may refer to the beneficial effects in the corresponding method provided above, and are not described herein again.
Through the above description of the embodiments, it is clear to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional modules is merely used as an example, and in practical applications, the above function distribution may be completed by different functional modules according to needs, that is, the internal structure of the device may be divided into different functional modules to complete all or part of the above described functions.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described device embodiments are merely illustrative, and for example, the division of the module or unit is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or integrated into another device, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may be one physical unit or a plurality of physical units, that is, may be located in one place, or may be distributed to a plurality of different places. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application, or portions of the technical solutions that substantially contribute to the prior art, or all or portions of the technical solutions may be embodied in the form of a software product, where the software product is stored in a storage medium and includes several instructions to enable a device (which may be a single chip, a chip, or the like) or a processor (processor) to execute all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above description is only an embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions within the technical scope of the present disclosure should be covered by the scope of the present application.
Claims (16)
1. A method of physiological characteristic signal processing, comprising:
filtering the acquired physiological characteristic signal of the target individual to obtain a physiological characteristic optimization signal;
extracting peak points in the physiological characteristic optimization signal by using a preset peak extraction algorithm, and constructing a first peak set based on the extracted peak points;
optimizing the first peak point set based on the physiological characteristic signal to obtain a second peak point set, wherein the optimizing includes one or more of adding a peak point, deleting the peak point and updating the peak point; and
and analyzing and obtaining the physiological characteristics of the target individual based on the second peak point set and the physiological characteristic optimization signal.
2. The method as claimed in claim 1, wherein the extracting the peak points in the optimized physiological characteristic signal by using a preset peak value extracting algorithm and constructing the first peak point set based on the extracted peak points comprises:
extracting peak points in the physiological characteristic optimization signal by using the preset peak extraction algorithm, and constructing the first peak point set based on the extracted peak points; or
And extracting valley points in the physiological characteristic optimization signal by using the preset peak value extraction algorithm, and constructing the first peak value set based on the extracted valley points.
3. A method as claimed in claim 1 or 2, wherein said optimizing the first set of peaks based on the physiological characteristic signal comprises:
modeling the physiological characteristic signal to obtain a physiological characteristic waveform corresponding to the physiological characteristic signal;
splitting the physiological characteristic waveform into an ascending waveform section and a descending waveform section, and selecting the ascending waveform section or the descending waveform section as a target waveform;
marking each peak point in the first peak point set on the target waveform;
segmenting the target waveform into a plurality of waveform windows, and calculating according to a preset interference degree calculation algorithm to obtain an initial interference degree of each waveform window;
optimizing the peak point of the waveform window, recalculating the interference degree of the waveform window after optimization processing by using the preset interference degree calculation algorithm until the interference degree of the waveform window obtains the minimum value, and finishing the optimization processing of the waveform window; and
and summarizing the peak points contained in each waveform window after the optimization processing is completed to obtain the second peak point set.
4. The physiological characteristic signal processing method according to claim 3, wherein after the selecting the uphill waveform or the downhill waveform as the target waveform, further comprising:
and simplifying the curve segment in the target waveform into a straight line segment only comprising head and tail end points.
5. The physiological characteristic signal processing method according to claim 4, wherein the preset interference degree calculation algorithm includes:
calculating the slope distance between any two straight line segments marked with the peak points in the waveform window, and carrying out normalization processing on the calculated slope distance;
calculating the length ratio between any two straight line segments marked with the peak points in the waveform window, and carrying out normalization processing on the calculated length ratio;
calculating the absolute value of the transverse distance difference between any two straight line segments marked with the peak point in the waveform window, and normalizing the absolute value of the transverse distance difference obtained through calculation;
calculating the absolute value of the longitudinal distance difference between any two straight line segments marked with the peak point in the waveform window, and normalizing the absolute value of the calculated longitudinal distance difference; and
and obtaining the interference degree of the waveform window based on the normalization result of the slope distance, the normalization result of the length ratio, the normalization result of the absolute value of the transverse distance difference and the normalization result of the absolute value of the longitudinal distance difference.
6. The method of processing a physiological characteristic signal according to claim 5, wherein the normalizing the calculated slope distance comprises:
respectively carrying out normalization processing on the plurality of slope distances obtained by calculation, and summarizing the normalization result of each slope distance; or
And accumulating the plurality of slope distances obtained by calculation to obtain a total slope distance, and carrying out normalization processing on the total slope distance.
7. The physiological characteristic signal processing method according to claim 5, wherein a longer one of the two straight line segments is a denominator of the length ratio, and the normalizing the calculated length ratio includes:
respectively carrying out normalization processing on a plurality of length ratios obtained by calculation, and summarizing the normalization result of each length ratio; or
And accumulating the plurality of calculated length ratios to obtain a total length ratio, and carrying out normalization processing on the total length ratio.
8. The method of processing a physiological characteristic signal according to claim 5, wherein the normalizing the absolute value of the calculated transverse distance difference comprises:
carrying out averaging operation on the absolute values of the plurality of transverse distance differences obtained by calculation to obtain an average transverse distance difference; and
and respectively normalizing the absolute value of each transverse distance difference obtained by calculation based on the average transverse distance difference, and summarizing the normalization result of the absolute value of each transverse distance difference.
9. The method for processing the physiological characteristic signal according to claim 5, wherein the normalizing the absolute value of the calculated longitudinal distance difference includes:
carrying out averaging operation on the absolute values of the plurality of longitudinal distance differences obtained by calculation to obtain an average longitudinal distance difference; and
and respectively normalizing the absolute value of each longitudinal distance difference obtained by calculation based on the average longitudinal distance difference, and summarizing the normalization result of the absolute value of each longitudinal distance difference.
10. The physiological characteristic signal processing method according to claim 5, wherein the optimizing the peak point of the waveform window comprises:
and searching for an area with abnormal slope distance change, abnormal length ratio change, abnormal transverse distance difference change or abnormal longitudinal distance difference change in the waveform window, and optimizing the peak point in the area.
11. The physiological characteristic signal processing method according to claim 3, wherein the optimizing the peak point of the waveform window comprises:
and when the initial interference degree of the waveform window is greater than or equal to a preset interference degree, optimizing the peak point of the waveform window.
12. The physiological characteristic signal processing method of claim 11, further comprising:
and when the initial interference degree of the waveform window is smaller than the preset interference degree, giving up the optimization processing on the peak point of the waveform window.
13. The method according to any one of claims 1 to 12, wherein the obtaining of the physiological characteristic of the target individual based on the second peak point set and the physiological characteristic optimization signal analysis comprises:
performing signal quality evaluation on the second peak point set and the physiological characteristic optimization signal; and
and when the signal quality evaluation result is that the signal quality is good, optimizing signal analysis based on the second peak point set and the physiological characteristics to obtain the physiological characteristics of the target individual.
14. A computer readable storage medium storing computer instructions which, when run on an electronic device, cause the electronic device to perform the physiological characteristic signal processing method of any one of claims 1 to 13.
15. An electronic device, comprising a processor and a memory, wherein the memory is configured to store instructions and the processor is configured to invoke the instructions in the memory to cause the electronic device to perform the physiological characteristic signal processing method of any one of claims 1 to 13.
16. A chip coupled with a memory in an electronic device, wherein the chip is configured to control the electronic device to perform the physiological characteristic signal processing method of any one of claims 1 to 13.
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