WO2022095796A1 - Procédé de traitement de signal de caractéristique physiologique, dispositif électronique, puce et support de stockage lisible - Google Patents

Procédé de traitement de signal de caractéristique physiologique, dispositif électronique, puce et support de stockage lisible Download PDF

Info

Publication number
WO2022095796A1
WO2022095796A1 PCT/CN2021/127382 CN2021127382W WO2022095796A1 WO 2022095796 A1 WO2022095796 A1 WO 2022095796A1 CN 2021127382 W CN2021127382 W CN 2021127382W WO 2022095796 A1 WO2022095796 A1 WO 2022095796A1
Authority
WO
WIPO (PCT)
Prior art keywords
physiological characteristic
waveform
peak point
peak
signal
Prior art date
Application number
PCT/CN2021/127382
Other languages
English (en)
Chinese (zh)
Inventor
李露平
陈茂林
Original Assignee
华为技术有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 华为技术有限公司 filed Critical 华为技术有限公司
Publication of WO2022095796A1 publication Critical patent/WO2022095796A1/fr

Links

Images

Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/08—Feature extraction
    • G06F2218/10—Feature extraction by analysing the shape of a waveform, e.g. extracting parameters relating to peaks

Definitions

  • the present application relates to the field of terminal technology, and in particular, to a physiological characteristic signal processing method, electronic device, chip, and computer-readable storage medium.
  • wearable devices can collect the human body's photoplethysmography (PPG). Measurement of signs such as blood oxygen, exercise, sleep, etc.
  • PPG photoplethysmography
  • the signal collected by the wearable device will go through processing processes such as signal filtering, signal peaking, signal quality detection, and physical sign measurement, in which the signal quality can be evaluated. , once the signal quality is assessed as poor, the signal collected by the wearable device will not be used for subsequent physical measurements.
  • the functions based on wearable devices are becoming more and more complex, such as the detection and even prediction of heart problems such as atrial fibrillation and premature beats. These functions have stricter requirements on the quality of signals collected by wearable devices, and sometimes require users to maintain a certain period of time.
  • the static state can be measured successfully.
  • the user is in an active state (for example, walking)
  • the signal quality collected by the wearable device is often poor, and the requirements for the signal processing capabilities of software and hardware are relatively high, resulting in the measurement function of the wearable device cannot be used normally in most active states.
  • the interference generated by the user in the active state changes dynamically, the interference generated by the same action of different users is different, and even the interference generated by the same user repeating the same action may also be different.
  • the interference in the active state does not follow fixed change rules.
  • the existing methods are generally to improve the hardware such as sensors, or to optimize the algorithm in the signal filtering and signal peaking stages, but the anti-interference effect of these methods is not obvious.
  • a physiological characteristic signal processing method which can overcome the above-mentioned problems, and can collect data and perform physical sign measurement when the user is in an active state, so as to improve the user experience.
  • a first aspect of the embodiments of the present application discloses a physiological feature signal processing method, which includes: filtering a collected physiological feature signal of a target individual to obtain a physiological feature optimization signal; extracting the physiological feature by using a preset peak extraction algorithm optimizing the peak points in the signal, and constructing a first peak point set based on the extracted peak points; performing optimization processing on the first peak point set based on the physiological characteristic signal to obtain a second peak point set, wherein the optimization
  • the processing includes one or more of processing of adding peak points, processing of deleting peak points, and processing of updating peak points; and optimizing the signal analysis based on the second peak point set and the physiological characteristics to obtain the physiological characteristics of the target individual feature.
  • the physiological characteristic data can be collected and processed when the user is in an active state, the measurement accuracy is high, the user experience is improved, and the user's physiological characteristics can be monitored in real time.
  • the using a preset peak extraction algorithm to extract the peak points in the physiological characteristic optimization signal, and constructing a first peak point set based on the extracted peak points includes: using the preset peak points The peak extraction algorithm extracts the peak points in the physiological characteristic optimization signal, and constructs the first peak point set based on the extracted peak points; or extracts the trough in the physiological characteristic optimization signal by using the preset peak extraction algorithm points, and the first set of peak points is constructed based on the extracted trough points.
  • the performing optimization processing on the first set of peak points based on the physiological characteristic signal includes: modeling the physiological characteristic signal to obtain a corresponding value of the physiological characteristic signal.
  • Physiological characteristic waveform ; splitting the physiological characteristic waveform into an up-slope waveform segment and a down-slope waveform segment, and selecting an up-slope waveform segment or a down-slope waveform segment as the target waveform;
  • the peak point is marked on the target waveform;
  • the target waveform is divided into a plurality of waveform windows, and the initial interference degree of each waveform window is calculated according to a preset interference degree calculation algorithm;
  • the peak point of the window is optimized, and the preset interference degree calculation algorithm is used to recalculate the interference degree of the waveform window after the optimization process, until the interference degree of the waveform window reaches the minimum value, and the waveform is completed.
  • window optimization processing and aggregating peak points included in each of the waveform windows that have completed the optimization processing to obtain the second peak point set.
  • the method further includes: simplifying the curve segment in the target waveform to a straight line segment including only head and tail endpoints.
  • the curve segment in the target waveform can be simplified into a straight segment, and the calculation amount of the subsequent interference degree calculation can be reduced.
  • the preset interference degree calculation algorithm includes: calculating a slope distance between any two straight line segments marked with the peak point in the waveform window, and comparing the calculated slope distance Perform normalization processing; calculate the length ratio between any two straight line segments marked with the peak point in the waveform window, and perform normalization processing on the calculated length ratio; calculate any arbitrary length ratio in the waveform window.
  • the absolute value of the lateral distance difference between the two straight line segments marked with the peak point, and the calculated absolute value of the lateral distance difference is normalized;
  • the absolute value of the longitudinal distance difference between the straight line segments of the peak point, and the calculated absolute value of the longitudinal distance difference is normalized; and based on the normalization result of the slope distance, the length ratio From the normalization result, the normalization result of the absolute value of the horizontal distance difference, and the normalization result of the absolute value of the vertical distance difference, the interference degree of the waveform window is obtained.
  • the interference degree of the waveform window can be calculated based on the four dimensions of slope distance, length ratio, horizontal distance difference and vertical distance difference.
  • performing normalization processing on the calculated slope distances includes: performing normalization processing on the plurality of calculated slope distances respectively, and summarizing the normalization of each of the slope distances result; or accumulating multiple calculated slope distances to obtain a total slope distance, and performing normalization processing on the total slope distance.
  • the slope distance can be normalized to convert the slope distance into the interference degree.
  • the longer straight line segment of the two straight line segments is the denominator of the length ratio
  • the normalizing the calculated length ratio includes: Normalize each length ratio separately, and summarize the normalization results of each length ratio; or accumulate multiple length ratios calculated to obtain a total length ratio, and normalize the total length ratio processing.
  • the length ratio can be normalized to convert the length ratio into the interference degree.
  • the normalizing the calculated absolute values of the lateral distance differences includes: performing an average operation on the absolute values of multiple calculated lateral distance differences to obtain an average lateral distance difference. distance difference; and normalizing the calculated absolute value of each lateral distance difference based on the average lateral distance difference, and summarizing the normalized result of the absolute value of each lateral distance difference.
  • the absolute value of the lateral distance difference can be normalized, so as to convert the absolute value of the lateral distance difference into the degree of interference.
  • performing normalization processing on the calculated absolute values of longitudinal distance differences includes: performing an average operation on the absolute values of multiple calculated longitudinal distance differences to obtain an average longitudinal distance difference. distance difference; and normalizing the calculated absolute value of each longitudinal distance difference separately based on the average longitudinal distance difference, and summarizing the normalized result of the absolute value of each longitudinal distance difference.
  • the absolute value of the longitudinal distance difference can be normalized, so as to convert the absolute value of the longitudinal distance difference into the degree of interference.
  • the performing optimization processing on the peak point of the waveform window includes: searching for abnormal changes in slope distance, abnormal length ratio changes, abnormal changes in horizontal distance difference, or abnormal changes in vertical distance in the waveform window.
  • the area where the distance difference changes abnormally, and the peak point in the area is optimized.
  • the performing optimization processing on the peak point of the waveform window includes: when the initial interference degree of the waveform window is greater than or equal to a preset interference degree, performing optimization processing on the peak value of the waveform window. Click to optimize.
  • the method further includes: when the initial interference degree of the waveform window is less than the preset interference degree, abandoning the optimization process for the peak point of the waveform window.
  • the optimizing the signal analysis based on the second peak point set and the physiological characteristics to obtain the physiological characteristics of the target individual includes: analyzing the second peak point set and the physiological characteristics Perform signal quality evaluation on the characteristic optimization signal; and when the signal quality evaluation result is good signal quality, analyze and obtain the physiological characteristic of the target individual based on the second peak point set and the physiological characteristic optimization signal.
  • the signal quality can be evaluated, and only the signal evaluated as the signal quality number will be used for the subsequent physical measurement.
  • an embodiment of the present application provides a computer-readable storage medium, including computer instructions, which, when the computer instructions are executed on an electronic device, cause the electronic device to execute the physiological characteristic signal processing method described in the first aspect.
  • an embodiment of the present application provides an electronic device, the electronic device includes a processor and a memory, the memory is used to store instructions, and the processor is used to call the instructions in the memory, so that the electronic device The physiological characteristic signal processing method according to the first aspect is performed.
  • an embodiment of the present application provides a computer program product, which, when the computer program product runs on a computer, causes the computer to execute the physiological characteristic signal processing method described in the first aspect.
  • an embodiment of the present application provides an apparatus, and the apparatus has a function of implementing the behavior of the electronic device in the method provided in the first aspect.
  • the functions can be implemented by hardware, or by executing corresponding software by hardware.
  • the hardware or software includes one or more modules corresponding to the above functions.
  • the computer-readable storage medium described in the second aspect, the electronic device described in the third aspect, the computer program product described in the fourth aspect, and the apparatus described in the fifth aspect are all the same as the above-mentioned first aspect.
  • the method of the aspect corresponds to, therefore, the beneficial effects that can be achieved can be referred to the beneficial effects of the corresponding methods provided above, which will not be repeated here.
  • FIG. 1 is a schematic flow chart of an existing wearable device performing physiological feature signal processing
  • FIG. 2 is a schematic flowchart of a physiological characteristic signal processing method provided by an embodiment of the present application
  • FIG. 3 is a schematic waveform diagram of a segment of a PPG signal detected by an electronic device provided by an embodiment of the present application
  • FIG. 4 is a schematic diagram of a waveform in which the PPG signal of FIG. 3 only retains a down-slope waveform segment and is marked with a first set of peak points;
  • FIG. 5 is a waveform schematic diagram in which the curve segment in the downslope waveform segment of FIG. 4 is simplified into a straight line segment including only the head and tail endpoints;
  • FIG. 6 is a schematic flowchart of a physiological characteristic signal processing method provided by another embodiment of the present application.
  • FIG. 7 is a schematic structural diagram of a possible electronic device provided by an embodiment of the present application.
  • a physiological feature signal processing method provided by an 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, etc., and has a physiological feature measuring function device of.
  • the physiological characteristic signal processing method may include:
  • a preset filtering method to filter the collected physiological characteristic signal, some noises contained in the physiological characteristic signal can be filtered out to obtain an optimized physiological characteristic signal.
  • the preset filtering method can select an existing sign signal filtering algorithm according to actual needs, such as a wavelet decomposition algorithm, a frequency domain analysis algorithm, a modal decomposition algorithm (such as empirical mode decomposition), an independent component analysis algorithm, Adaptive filtering algorithms, etc.
  • the physiological characteristic signal is a PPG signal
  • the target individual is the wearer of the electronic device 100
  • the filtered and optimized PPG signal can be obtained by filtering the PPG signal of the target individual collected by the electronic device 100 .
  • a preset peak-lifting algorithm may also be used to extract the peak points in the physiological characteristic optimization signal, the peak points may be peak points or trough points, and the extracted peak points may be grouped into a set, Then, the first peak point set is obtained. That is, a preset peak-lifting algorithm may be used to extract the peak points in the physiological characteristic optimization signal, and a first set of peak points may be constructed based on the extracted peak points, or a preset peak-lifting algorithm may be used to extract the peak points in the physiological characteristic optimization signal. , and construct the first set of peak points based on the extracted trough points. The following takes the peak point as the peak point as an example for illustration.
  • the preset peak-lifting algorithm may also select an existing physical-signal signal peak-lifting algorithm according to actual needs, such as a Bayesian decision classification algorithm, a machine learning classification algorithm, a heuristic algorithm, and the like.
  • the optimization process may include one or a combination of processing of adding a peak point, deleting a peak point, and updating a peak point.
  • the processing of adding a peak point may refer to adding a peak point to the first peak point set
  • the processing of deleting a peak point may refer to deleting a peak point from the first peak point set
  • the peak point processing may refer to 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.
  • the physiological characteristic signal is an example of a PPG signal for illustration.
  • the PPG signal collected by the electronic device 100 can be modeled, and the PPG signal can be converted into the physiological characteristic waveform S1 shown in FIG.
  • the light intensity detected by the optical heart rate sensor is the dimension), and then the physiological characteristic waveform is divided into an up-slope waveform segment S11 and a down-slope waveform segment S12, and then one waveform segment can be arbitrarily selected as the target waveform for subsequent analysis.
  • the following is an example of selecting the down-slope waveform segment S12 as the target waveform.
  • each peak point in the first peak point set may be marked on the target waveform shown in FIG. 4 . Since the target waveform contains many waveform segments, in order to speed up the signal analysis, the target waveform can be divided into multiple waveform windows, and then the interference degree of each waveform window can be calculated by using the preset interference degree calculation algorithm.
  • the target waveform may be divided into multiple waveform windows with time as the segmentation dimension, and each waveform window contains the same time scale, for example, each waveform window contains a 1-second downslope waveform segment.
  • the target waveform can also be divided into multiple waveform windows with the number of waveform segments as the segmentation dimension, and each waveform window contains the same number of waveform segments, for example, each waveform window contains 30 downslope waveform segments.
  • the curve segment in each waveform window may be simplified to a straight line segment including only the head and tail endpoints, and then the interference degree calculation is performed.
  • the curve segment shown in FIG. 4 is simplified to a straight line segment including only the head and tail end points.
  • Using a preset interference degree calculation algorithm to calculate the interference degree of the waveform window may include: a. Calculate the slope distance between any two straight line segments marked with the peak point in the waveform window (that is, any two straight line segments marked with the peak point) The included angle between the straight line segments), and normalize the calculated slope distance; b.
  • the larger the value of the slope distance the larger the result of the normalization process.
  • Performing normalization processing on the calculated slope distances may refer to performing normalization processing on multiple calculated slope distances respectively (the closer the value of the slope distances is to 0°, the smaller the result obtained by performing the normalization processing, the smaller the slope distance is.
  • the closer the value of the distance is to 90°, the larger the result obtained from the normalization process) convert to obtain the corresponding degree of interference, and then summarize the normalized results, which can also mean that the calculated slope distances are accumulated first. The total slope distance is obtained, and then the total slope distance is normalized to obtain the corresponding interference degree.
  • Performing normalization processing on the calculated length ratios may refer to performing normalization processing on multiple calculated length ratios respectively (the closer the value of the length ratios is to 0, the larger the result obtained by performing the normalization processing, and the longer the length ratios are. The closer the value is to 1, the smaller the result obtained by normalization), convert to obtain the corresponding degree of interference, and then summarize the normalized results, which can also mean that the calculated slope distances are first accumulated to obtain the total The slope distance is then normalized to the total slope distance, and the corresponding interference degree is obtained by conversion.
  • normalizing the calculated absolute value of the lateral distance difference may include: firstly averaging the absolute values of multiple calculated lateral distance differences to obtain the average lateral distance difference of the waveform window , and then normalize the absolute values of the multiple calculated lateral distance differences (the closer the absolute value of the lateral distance difference is to the average lateral distance difference, the smaller the result obtained by normalization, and the absolute value of the lateral distance difference is smaller. The more the value deviates from the average lateral distance difference, the larger the result obtained by normalization), the corresponding interference degree is obtained by conversion, and then the normalization results are summarized.
  • normalizing the calculated absolute value of the longitudinal distance difference may include: first averaging the absolute values of the plurality of calculated longitudinal distance differences to obtain the average longitudinal distance difference of the waveform window , and then normalize the calculated absolute values of the multiple longitudinal distance differences respectively (the closer the absolute value of the longitudinal distance difference is to the average longitudinal distance difference, the smaller the result obtained by normalization, and the absolute value of the longitudinal distance difference is smaller. The more the value deviates from the average longitudinal distance difference, the larger the result obtained by normalization), the corresponding interference degree is obtained by conversion, and then the normalization results are summarized.
  • the interference degree of the waveform window is divided into four dimensions: slope distance, length ratio, horizontal distance difference, and vertical distance difference for calculation and normalization. Interference degree, and then accumulate the normalized results of the slope distance, the normalized results of the length ratio, the normalized results of the absolute value of the horizontal distance difference, and the normalized results of the absolute value of the vertical distance difference, you can get The noise level of this waveform window.
  • the initial interference degree of each waveform window when the initial interference degree of each waveform window is obtained by calculation, it may be determined whether the initial interference degree is greater than a preset interference degree to determine whether it is necessary to adjust the peak point in the waveform window.
  • a preset interference degree When the initial interference degree of the waveform window is greater than the preset interference degree, it indicates that the quality of the peak points in the waveform window is poor, and even through subsequent optimization processing, the existing signal quality cannot be verified.
  • the initial interference degree of the waveform window is greater than or less than the preset interference degree, it indicates that the quality of the peak points in the waveform window has room for adjustment, and the existing signal quality check may be passed through the subsequent optimization processing of the present application.
  • the size of the preset interference degree can be set according to actual needs.
  • the preset interference degree calculation algorithm may be used to recalculate the interference degree of the optimized waveform window. Repeat the calculation of the interference degree until the interference degree of the waveform window reaches the minimum value, and stop optimizing the waveform window. After the optimization process is completed for each waveform window, the peak points currently included in each waveform window for which the optimization process has been completed can be aggregated to construct a second peak point set.
  • an attempt to optimize the peak point of the waveform window can be to add a peak point to a straight line segment (the straight line segment was not marked with a peak point before), or delete the peak point marked by a straight line segment. , or delete the peak point marked by a straight line segment, and then add a new peak point on another straight line segment (the waveform segment was not marked with a peak point before).
  • the sudden change generally does not have a large span. For example, to analyze the slope distance, length ratio, horizontal distance difference, and vertical distance difference of several adjacent straight line segments marked with peak points, if it is found that a certain value suddenly changes greatly, it may be necessary to analyze the peak point in this area. Perform optimization processing, try to add peak points and/or delete peak points, and recalculate the interference degree to try to minimize the interference degree of the waveform window and save the optimization processing time.
  • the number of attempts of the optimization process may also be limited, and after completing the preset number of optimization processes, a waveform state with a minimum interference degree is selected.
  • a polling adjustment method can also be used to try to add and/or delete peak points for 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 is reached.
  • the processing time is relatively long compared to the optimized processing method described in the previous description.
  • an existing physical sign measurement and analysis method (such as the signal quality detection and physical sign measurement steps in FIG. 1 ) can be used to optimize the signal for the second peak point set and physiological characteristics
  • the analysis is carried out to obtain the physiological characteristics of the target individual.
  • physiological characteristics such as heart rate, blood pressure and so on.
  • the heart rate value is obtained by conversion based on the number of peak points within a certain period of time. For example, if the number of peak points of the 5s physiological characteristic optimization signal is N, then the heart rate is N*12.
  • signal quality evaluation may be performed on the second peak point set and the physiological characteristic optimization signal. If the evaluation result is poor signal quality, this segment of signal will not be used for subsequent physical measurement, and the signal will be terminated directly. deal with. If the evaluation result is that the signal quality is good, this segment of the signal will be used for physical sign measurement, and the existing sign analysis method can be used to analyze the second peak point set and the physiological characteristic optimization signal to obtain the physiological characteristics of the target individual.
  • the above physiological characteristic signal processing method first uses the existing filtering and peak-lifting technology to obtain the initial peak point set, and then uses the secondary peak-lifting mechanism to optimize the initial peak point set to obtain the final peak point set, which can be realized when the user is in the Collecting data in an active state eliminates the need to deliberately keep the user in a stationary state for a long time, increases the applicable scenarios for physical sign measurement, improves the user experience, and truly realizes real-time monitoring of the user's physiological characteristics, which can improve application scenarios such as wearable devices.
  • FIG. 6 a schematic flowchart of an electronic device 100 implementing physiological feature measurement for a target individual provided by an embodiment of the present application.
  • the target individual wears the electronic device 100, and the electronic device 100 can collect the original physiological characteristic signal.
  • An existing peak-lifting algorithm may be used to perform a peak point extraction operation on the obtained physiological characteristic optimized signal obtained by filtering, so as to obtain a first peak point set.
  • the way to lift the peaks can be to extract only the peak points or only the trough points.
  • Secondary peak-lifting treatment Model the original physiological characteristic signal, and divide the waveform obtained by modeling into an up-slope waveform segment and a down-slope waveform segment, and then arbitrarily select a waveform segment as the target waveform for subsequent analysis, and then collect the first peak points.
  • the included peak points are marked on the target waveform and the interference level is calculated.
  • the interference degree is minimized by trying to add peak points, delete peak points, and update peak points on the target waveform, and then construct a second peak point set based on the peak points in the state of minimum interference degree.
  • Signal quality detection Use the existing signal quality detection method to evaluate the second peak point set and the optimized signal of physiological characteristics. If the evaluation result is that the signal quality is poor, the signal will not be used for the subsequent physical measurement, and the signal processing will be ended directly. .
  • the atrial fibrillation detection function generally requires the user to remain still for about 1 minute before Perform atrial fibrillation testing.
  • Sample 1 9899 segments of PPG signals without atrial fibrillation were collected in static and active states, and each segment lasted about 1 minute; sample 2: 6740 segments of PPG signals with atrial fibrillation were collected in static and active states, each segment The signal lasts about 1 minute.
  • the sample 1 and sample 2 are processed by the existing physiological characteristic signal processing method.
  • 6891 signals have passed the existing signal quality inspection, and it can be considered that most of them are in a static state.
  • the remaining 3008 segments of the signal did not pass the existing signal quality check, and it can be considered that most of them were collected in the active state; among the 6740 segments of atrial fibrillation-free PPG signals collected, 3031 segments passed the existing signals.
  • For quality inspection it can be considered that most of the signals were collected in a static state, and the remaining 3709 segments of signals did not pass the existing signal quality inspection, and it can be considered that most of them were collected in an active state. That is, when using the existing physiological characteristic signal processing methods, about 6717 (3008+3709) segments of PPG signal data cannot pass the signal quality verification. In the data that passes the signal quality verification, the accuracy of atrial fibrillation measurement is above 95%. .
  • the current atrial fibrillation warning function generally cannot be used when the user is in the vehicle.
  • the PPG signal data in the vehicle scene was collected.
  • the vehicle-mounted data of the atrial fibrillation patient was not collected, so the actual samples were negative Samples to test the false alarm rate of atrial fibrillation (the bumps in the vehicle state will cause the signal to fluctuate, making it easy to produce false alarms in the results measured by the current electronic equipment).
  • Sample 1 343-segment PPGs are collected from the driver's left hand in a vehicle-mounted scenario
  • Sample 2 343-segment PPGs are collected from the driver's right hand in an on-board scenario
  • Sample 3 49-segment PPGs are collected from the driver in a stationary scenario. That is, a total of 735 (343+343+49) segments of PPG are collected, and each segment of the signal lasts about 1 minute.
  • the electronic device 100 may include a processor 1001 , a memory 1002 , and a communication bus 1003 .
  • 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 can be used to implement the above-described physiological characteristic signal processing method in the electronic device 100 .
  • the structure illustrated in this embodiment does not constitute a specific limitation on the electronic device 100 .
  • the electronic device 100 may include more or fewer components than shown, or some components may be combined, or some components may be split, or a different arrangement of components.
  • the processor 1001 may include one or more processing units, for example, the processor 1001 may include an application processor (application processor, AP), a graphics processor (graphics processing unit, GPU), an image signal processor (image signal processor, ISP) ), controller, video codec, DSP, CPU, baseband processor, and/or neural-network processing unit (NPU), etc. Wherein, different processing units may be independent devices, or may be integrated in one or more processors.
  • application processor application processor, AP
  • graphics processor graphics processor
  • image signal processor image signal processor
  • ISP image signal processor
  • controller video codec
  • DSP digital signal processor
  • CPU central processing unit
  • baseband processor baseband processor
  • NPU neural-network processing unit
  • the processor 1001 may also be provided with a memory for storing instructions and data.
  • the memory in processor 1001 is cache memory. This memory may hold instructions or data that have just been used or recycled by the processor 1001 . If the processor 1001 needs to use the instruction or data again, it can be called directly from this memory. Repeated access is avoided, and the waiting time of the processor 1001 is reduced, thereby improving the efficiency of the system.
  • the processor 1001 may include one or more interfaces.
  • the interface may include an integrated circuit (inter-integrated circuit, I2C) interface, an integrated circuit built-in audio (inter-integrated circuit sound, I2S) interface, a pulse code modulation (pulse code modulation, PCM) interface, a universal asynchronous transceiver (universal asynchronous transmitter) receiver/transmitter, UART) interface, mobile industry processor interface (MIPI), general-purpose input/output (GPIO) interface, SIM interface, and/or USB interface, etc.
  • I2C integrated circuit
  • I2S integrated circuit built-in audio
  • PCM pulse code modulation
  • PCM pulse code modulation
  • UART universal asynchronous transceiver
  • MIPI mobile industry processor interface
  • GPIO general-purpose input/output
  • memory 1002 may include high-speed random access memory, and may also include non-volatile memory such as hard disk, internal memory, plug-in hard disk, Smart Media Card (SMC), Secure Digital (Secure) Digital, SD) card, flash card (Flash Card), at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device.
  • non-volatile memory such as hard disk, internal memory, plug-in hard disk, Smart Media Card (SMC), Secure Digital (Secure) Digital, SD) card, flash card (Flash Card), at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device.
  • This embodiment also provides a computer storage medium, where computer instructions are stored in the computer storage medium, and when the computer instructions are executed on the electronic device, the electronic device executes the above-mentioned related method steps to realize the physiological characteristic signal processing in the above-mentioned embodiment. method.
  • This embodiment also provides a computer program product, when the computer program product runs on the computer, the computer executes the above-mentioned relevant steps, so as to realize the physiological characteristic signal processing method in the above-mentioned embodiment.
  • the embodiments of the present application also provide an apparatus, which may specifically be a chip, a component or a module, and the apparatus may include a connected processor and a memory; wherein, the memory is used for storing computer execution instructions, and when the apparatus is running, The processor can execute the computer-executed instructions stored in the memory, so that the chip executes the physiological characteristic signal processing methods in the foregoing method embodiments.
  • the first electronic device, computer storage medium, computer program product or chip provided in this embodiment are all used to execute the corresponding method provided above. Therefore, for the beneficial effects that can be achieved, reference may be made to the provided above. The beneficial effects in the corresponding method will not be repeated here.
  • the disclosed apparatus and method may be implemented in other manners.
  • the device embodiments described above are only illustrative.
  • the division of the modules or units is only a logical function division. In actual implementation, there may be other division methods.
  • multiple units or components may be combined. Or it may be integrated into another device, or some features may be omitted, or not implemented.
  • the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in electrical, mechanical or other forms.
  • the units described as separate components may or may not be physically separated, and components shown as units may be one physical unit or multiple physical units, that is, may be located in one place, or may be distributed to multiple different places. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution in this embodiment.
  • each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit.
  • the above-mentioned integrated units may be implemented in the form of hardware, or may be implemented in the form of software functional units.
  • the integrated unit if implemented in the form of a software functional unit and sold or used as an independent product, may be stored in a readable storage medium.
  • a readable storage medium including several instructions to make a device (may be a single chip microcomputer, a chip, etc.) or a processor (processor) to execute all or part of the steps of the methods described in the various embodiments of the present application.
  • the aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disk or optical disk and other media that can store program codes .

Landscapes

  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Data Mining & Analysis (AREA)
  • Public Health (AREA)
  • Theoretical Computer Science (AREA)
  • Medical Informatics (AREA)
  • Physics & Mathematics (AREA)
  • Pathology (AREA)
  • General Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • General Physics & Mathematics (AREA)
  • Evolutionary Biology (AREA)
  • Evolutionary Computation (AREA)
  • General Engineering & Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Artificial Intelligence (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Molecular Biology (AREA)
  • Veterinary Medicine (AREA)
  • Animal Behavior & Ethology (AREA)
  • Surgery (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Biophysics (AREA)
  • Primary Health Care (AREA)
  • Epidemiology (AREA)
  • Databases & Information Systems (AREA)
  • Physiology (AREA)
  • Cardiology (AREA)
  • Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)

Abstract

Les modes de réalisation de la présente demande se rapportent au domaine des dispositifs électroniques et concernent un procédé de traitement de signal de caractéristique physiologique. Un signal d'optimisation de caractéristique physiologique est obtenu par réalisation d'un traitement de filtrage sur un signal de caractéristique physiologique d'origine acquis ; des points de crête du signal d'optimisation de caractéristique physiologique sont extraits à l'aide d'un algorithme d'extraction de crête prédéfini pour obtenir un premier ensemble de points de crête ; puis, un traitement d'extraction de crête secondaire et d'optimisation est effectué sur le premier ensemble de points de crête sur la base du signal de caractéristique physiologique d'origine pour obtenir un second ensemble de points de crête, de façon à obtenir des caractéristiques physiologiques d'un individu cible par réalisation d'une analyse sur la base du second ensemble de points de crête et du signal d'optimisation de caractéristique physiologique. Les modes de réalisation de la présente demande concernent en outre un dispositif électronique, une puce et un support de stockage lisible par ordinateur. Selon la présente demande, un mécanisme d'extraction de crête secondaire est introduit, de telle sorte que des données de caractéristique physiologique puissent être acquises lorsqu'un utilisateur est actif et ainsi l'expérience d'utilisation de l'utilisateur est améliorée et la surveillance en temps réel des caractéristiques physiologiques de l'utilisateur est mise en œuvre.
PCT/CN2021/127382 2020-11-05 2021-10-29 Procédé de traitement de signal de caractéristique physiologique, dispositif électronique, puce et support de stockage lisible WO2022095796A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202011225485.6A CN114530246A (zh) 2020-11-05 2020-11-05 生理特征信号处理方法、电子设备、芯片及可读存储介质
CN202011225485.6 2020-11-05

Publications (1)

Publication Number Publication Date
WO2022095796A1 true WO2022095796A1 (fr) 2022-05-12

Family

ID=81457515

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2021/127382 WO2022095796A1 (fr) 2020-11-05 2021-10-29 Procédé de traitement de signal de caractéristique physiologique, dispositif électronique, puce et support de stockage lisible

Country Status (2)

Country Link
CN (1) CN114530246A (fr)
WO (1) WO2022095796A1 (fr)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115778337B (zh) * 2022-10-28 2023-06-16 北京津发科技股份有限公司 人体生物信号数据处理方法、装置、设备及存储介质

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105997054A (zh) * 2016-06-22 2016-10-12 天津理工大学 一种心电信号预分析的方法
US10117598B1 (en) * 2015-11-08 2018-11-06 Sensogram Technologies, Inc. Non-invasive wearable respiration rate monitoring system
CN109363655A (zh) * 2018-12-17 2019-02-22 上海理工大学 基于示波法和累积分布函数估算的血压测量装置及方法
US10327649B1 (en) * 2014-03-31 2019-06-25 Sensogram Technologies, Inc. Non-invasive wearable blood pressure monitoring system
CN111588367A (zh) * 2020-06-05 2020-08-28 青岛歌尔智能传感器有限公司 心率检测方法、装置及计算机可读存储介质

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10327649B1 (en) * 2014-03-31 2019-06-25 Sensogram Technologies, Inc. Non-invasive wearable blood pressure monitoring system
US10117598B1 (en) * 2015-11-08 2018-11-06 Sensogram Technologies, Inc. Non-invasive wearable respiration rate monitoring system
CN105997054A (zh) * 2016-06-22 2016-10-12 天津理工大学 一种心电信号预分析的方法
CN109363655A (zh) * 2018-12-17 2019-02-22 上海理工大学 基于示波法和累积分布函数估算的血压测量装置及方法
CN111588367A (zh) * 2020-06-05 2020-08-28 青岛歌尔智能传感器有限公司 心率检测方法、装置及计算机可读存储介质

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115778337B (zh) * 2022-10-28 2023-06-16 北京津发科技股份有限公司 人体生物信号数据处理方法、装置、设备及存储介质

Also Published As

Publication number Publication date
CN114530246A (zh) 2022-05-24

Similar Documents

Publication Publication Date Title
US20220125322A1 (en) Methods and Systems for Determining Abnormal Cardiac Activity
Limam et al. Atrial fibrillation detection and ECG classification based on convolutional recurrent neural network
CN111345816B (zh) 多导联qrs波群检测方法、装置、设备及存储介质
WO2021253709A1 (fr) Procédé et appareil de classification de battements cardiaques d'électrocardiogramme, et dispositif électronique et support
WO2022095796A1 (fr) Procédé de traitement de signal de caractéristique physiologique, dispositif électronique, puce et support de stockage lisible
CN112057066A (zh) 心率的检测方法、可穿戴设备及计算机存储介质
CN111035395B (zh) 血氧饱和度信号分析方法、装置、计算机设备和存储介质
US12059259B2 (en) Method and apparatus for processing an electrocardiogram signal and electronic device
CN114283494A (zh) 一种用户跌倒预警方法、装置、设备和存储介质
US20240156361A1 (en) Deep learning-based heart rate detection method and wearable device
CN112930139A (zh) 分析睡眠呼吸障碍事件的系统和方法
KR20200125256A (ko) 심전도 분석 장치 및 방법
WO2020000523A1 (fr) Procédé et appareil de traitement de signal
Ding et al. Cascaded convolutional neural network with attention mechanism for mobile EEG-based driver drowsiness detection system
WO2023123864A1 (fr) Appareil de détermination d'état musculaire basé sur un signal électromyographique et une saturation en oxygène du sang musculaire
US11564613B2 (en) Non-invasive continuous heart rhythm monitoring based on wearable sensors
WO2023168834A1 (fr) Procédé et appareil d'évaluation de qualité de signal de signe physique, dispositif électronique et support de stockage
CN106491118A (zh) 基于安卓系统的心电图机的实时心率计算系统及其方法
CN115954019A (zh) 一种融合自注意力和卷积操作的环境噪声识别方法及系统
CN115563470A (zh) 一种面向在线wifi感知的低延迟活动信号分割方法及装置
WO2021058007A1 (fr) Procédé et appareil de traitement de données de série chronologique, et support de stockage lisible par ordinateur
CN113688802B (zh) 基于肌电信号的手势识别方法、装置、设备及存储介质
CN113057611B (zh) 用于输出信息的方法、装置、设备以及存储介质
CN115082907A (zh) 确定对象状态的方法、装置、电子设备和存储介质
Qananwah et al. Cardiac arrhythmias classification using photoplethysmography database

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 21888493

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 21888493

Country of ref document: EP

Kind code of ref document: A1