Signal processing method and device, electronic equipment and storage medium

Abstract

The invention relates to a signal processing method and device, electronic equipment and a storage medium, and relates to the field of signal processing. The method comprises the following steps: acquiring a biological signal set of a target object in real time; matching a corresponding weight coefficient set for each biological signal; according to the weight coefficient set matched with each biological signal, traversing various weight coefficient combinations to obtain a composite signal set; determining the score of each composite signal in the composite signal set according to a preset score determination mode; and selecting the composite signal with the highest score in the composite signal set, taking the weight coefficient corresponding to each biological signal in the composite signal as the optimal weight coefficient, and performing signal synthesis on the biological signal set acquired subsequently in real time by using the optimal weight coefficient so as to determine a target biological signal. By applying the method and the device, an accurate data basis can be provided for aorta counter-blogging.

Description

Technical Field

[0001] The present application relates to the field of signal processing technology, and in particular to a signal processing method, device, electronic device and storage medium. Background Art

[0002] In the existing aortic counterpulsation technology, the RR interval prediction method is used to inflate and assist the left ventricular pumping at the end of the left ventricular pumping period to increase the ejection volume; in the next period of left ventricular pumping, the inhalation causes a negative pressure difference between the artery and the left ventricle to reduce the left ventricular pumping load; therefore, accurate identification of the R wave is an important prerequisite for the application of this technology. If the R wave cannot be accurately identified, counterpulsation errors will occur, which will endanger the patient's life. Therefore, accurate identification of the R wave is the most important prerequisite for the application of this technology.

[0003] Electrocardiogram, also known as electrocardiography (ECG), is a diagnostic technology that records the electrophysiological activity of the heart in time units through the chest cavity, and captures and records it through electrodes on the skin. The R point is the most obvious point in the electrocardiogram. Correctly detecting the R point is the basis for the automatic analysis of the entire electrocardiogram. The R point detection method is a hot topic in electrocardiogram-related research. Existing R point detection methods are basically based on single-lead ECG waveforms or on the main analysis lead waveform in multi-lead waveforms. It is difficult to make a detection when the lead is off, the signal peak is small, or the interference is large, and it is difficult to rule out the influence of spike pulses, tall T waves, and drift interference. The calculation is large, the real-time performance is not strong, and the algorithm is complex. Summary of the invention

[0004] Embodiments of the present disclosure provide a signal processing method, device, electronic device, and storage medium.

[0005] In a first aspect, an embodiment of the present disclosure provides a signal processing method, comprising: acquiring a set of biological signals of a target object in real time, wherein the biological signal set includes at least two biological signals of the same type; matching a corresponding weight coefficient set for each biological signal, wherein the weight coefficient set is determined according to a set weight coefficient range and a discrete step length of the weight coefficient; according to the weight coefficient set matched for each biological signal, traversing various weight coefficient combinations to obtain a synthetic signal set; determining a score for each synthetic signal in the synthetic signal set according to a preset score determination method; selecting the synthetic signal with the highest score in the synthetic signal set, using the weight coefficient corresponding to each biological signal in the synthetic signal as the optimal weight coefficient, and performing signal synthesis on the subsequent biological signal set acquired in real time using the optimal weight coefficient to determine the target biological signal.

[0006] In a second aspect, an embodiment of the present disclosure provides a signal processing device, comprising: a signal acquisition unit, configured to acquire a set of biological signals of a target object in real time, wherein the biological signal set includes at least two biological signals of the same type; a weight determination unit, configured to match a corresponding weight coefficient set to each biological signal, wherein the weight coefficient set is determined according to a set weight coefficient range and a discrete step length of the weight coefficient; a set determination unit, configured to traverse various weight coefficient combinations to obtain a synthetic signal set according to the weight coefficient set matched to each biological signal; a score determination unit, configured to determine a score for each synthetic signal in the synthetic signal set according to a preset score determination method; a signal synthesis unit, configured to select the synthetic signal with the highest score in the synthetic signal set, use the weight coefficient corresponding to each biological signal in the synthetic signal as the optimal weight coefficient, and use the optimal weight coefficient to perform signal synthesis on the subsequent biological signal set acquired in real time to determine the target biological signal.

[0007] In a third aspect, an embodiment of the present disclosure provides an electronic device, comprising a memory, a processor, a bus, and a computer program stored in the memory and executable on the processor, wherein the processor implements the signal processing method described in the first aspect when executing the computer program.

[0008] In a fourth aspect, an embodiment of the present disclosure provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the signal processing method described in the first aspect.

[0009] By applying the technical solution disclosed in the present invention, the biological signal set of the target object can be processed and synthesized to obtain the target biological signal, so that the R point can be extracted more conveniently, providing an accurate data basis for the aortic counterpulsation.

[0010] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0012] Figure 1 is an exemplary system architecture diagram to which an embodiment of the signal processing method of the present disclosure may be applied;

[0013] Figure 2 A flowchart of an embodiment of a signal processing method disclosed herein;

[0014] Figure 3A flowchart of another embodiment of the signal processing method disclosed herein;

[0015] Figure 4 A schematic diagram of a central electrostatic signal in the signal processing method disclosed in the present invention;

[0016] Figure 5 A schematic diagram of a preprocessed ECG signal and a threshold value in the signal processing method disclosed in the present invention;

[0017] Figure 6 is a structural schematic diagram of an embodiment of a signal processing device disclosed in the present invention;

[0018] Figure 7 A schematic diagram of the structure of an embodiment of an electronic device disclosed herein. DETAILED DESCRIPTION

[0019] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present disclosure belongs.

[0020] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0021] In the absence of conflict, the embodiments in the present disclosure and the features in the embodiments may be combined with each other.

[0022] In order to make the technical solutions and advantages of the present disclosure more clearly understood, the present disclosure is further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0023] Figure 1 An exemplary system architecture 100 is shown to which embodiments of the signal processing method or signal processing apparatus of the present disclosure can be applied.

[0024] like Figure 1 As shown, the system architecture 100 may include a terminal device 101, a network 102, and biological signal acquisition devices 103, 104, 105. The network 102 is used to provide a medium for a communication link between the terminal device 101 and the biological signal acquisition devices 103, 104, 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0025] The user can use the terminal device 101 to obtain the collected biosignals from the biosignal collection devices 103, 104, and 105 through the network 104, and perform various processes on the biosignals. At the same time, the processed biosignals can be output, or the processed biosignals can be used for signal control.

[0026] The terminal device 101 can be hardware or software. When the terminal device 101 is hardware, it can be various electronic devices, including but not limited to smart phones, tablet computers, car computers, laptop computers, desktop computers, etc. When the terminal device 101 is software, it can be installed in the electronic devices listed above. It can be implemented as multiple software or software modules (for example, to provide distributed services), or it can be implemented as a single software or software module. No specific limitation is made here.

[0027] The biosignal acquisition devices 103, 104, and 105 can act on the target object to collect the biosignal of the target object. The target object can be any type of living body, such as a human, an animal, etc. The types of biosignals collected by the biosignal acquisition devices 103, 104, and 105 can be the same or different. The above-mentioned biosignal acquisition devices 103, 104, and 105 can include but are not limited to electrocardiogram monitors, blood pressure monitors, venous signal collectors, etc. Correspondingly, the biosignal can include but are not limited to electrocardiogram signals, blood pressure signals, venous signals, etc. The biosignal acquisition devices 103, 104, and 105 can send the collected biosignals of the target object to the terminal device 101 so that the terminal device 101 can process the above-mentioned biosignals.

[0028] It should be noted that the signal processing method provided in the embodiment of the present disclosure is generally executed by the terminal device 101. Accordingly, the signal processing device is generally disposed in the terminal device 101.

[0029] It should be understood that Figure 1 The number of terminal devices, networks and biological signal collection devices in the embodiment is only for illustration. Any number of terminal devices, networks and biological signal collection devices may be provided according to the implementation requirements.

[0030] Figure 2 FIG. 2 shows a flow chart 200 of an embodiment of a signal processing method of the present disclosure. Figure 2 As shown, the signal processing method of this embodiment may include the following steps:

[0031] Step 201, acquiring a biological signal set of a target object in real time.

[0032] In this embodiment, the execution subject of the signal processing method (for example Figure 1The terminal device 101 shown in the figure can collect the biological signal from various biological signal collection devices (such as Figure 1 The biological signal collection devices 103, 104, and 105 shown in the figure acquire the biological signal set of the target object in real time. Here, the biological signal set includes at least two biological signals of the same type. For example, both can be electrocardiogram signals, or both can be blood pressure signals.

[0033] Step 202, matching each biological signal with a corresponding set of weight coefficients, wherein the set of weight coefficients is determined according to a set weight coefficient range and a discrete step length of the weight coefficients.

[0034] In this embodiment, the weight coefficient range and discrete step length corresponding to each biological signal can be determined. Here, the weight coefficient range and discrete step length corresponding to different biological signals can be the same or different. For example, the weight coefficient range and discrete step length can be determined by the collection position of the biological signal. Taking the electrocardiogram signal as an example, multiple electrodes can be used to collect the electrocardiogram signal, including twelve leads, namely I, II, III, avL, avF, avR, v1, v2, v3, v4, v5, and v6. The electrodes corresponding to each lead are different. When determining the weight coefficient range and discrete step length corresponding to each lead, it can be determined according to the electrode position corresponding to each lead. Alternatively, the weight coefficient range and discrete step length corresponding to each biological signal can be pre-set. The weight coefficient range indicates that the weight can take a value between the minimum and maximum values, and the discrete step length indicates the difference between two adjacent weights.

[0035] After determining the weight coefficient range and discrete step length corresponding to each biological signal, the weight coefficient set corresponding to each biological signal can be determined. Taking the weight coefficient range as [-1, 1] and the discrete step length as 0.5 as an example, the weight coefficient set can include 5 weight values ​​of -1, -0.5, 0, 0.5, and 1.

[0036] Step 203: According to the weight coefficient set matched for each biological signal, various weight coefficient combinations are traversed to obtain a synthetic signal set.

[0037] After determining the weight coefficient set for each biosignal match, various weight coefficient combinations can be traversed to obtain a synthetic signal set. Specifically, each weight coefficient in the weight coefficient set for a single biosignal match can be matched with each weight coefficient in the weight coefficient set for other biosignal matches to obtain multiple matching weight coefficients. Then, each matching weight coefficient is combined with each biosignal to obtain multiple synthetic signals, which is a synthetic signal set.

[0038] Exemplarily, n lead signals are selected, and the weight coefficient range matched for each lead signal is [-N, N], and the weight coefficient set has a total of a weight values ​​according to the discrete step length: M1, M2...Ma.

[0039] The weight coefficients that can be matched for the first lead signal are M1, M2, ..., Ma, respectively; the weight coefficients that can be matched for the second lead signal are M1, M2, ..., Ma, respectively; and the weight coefficients that can be matched for the nth lead signal are M1, M2, ..., Ma, respectively. Then the n lead signals have a total of n There are a n synthesized signal.

[0040] Take 3-lead ECG signals as an example. The weight coefficient range of each lead ECG signal is [-1, 1], and the discrete step length is 0.1. The weight coefficient of each lead ECG signal can be: -1, -0.9, -0.8...0, 0.1, 0.2,...0.9, 1, a total of 21 weight values. The weight coefficients of the 3-lead ECG signals are combined to get 21 3 =9261 synthesized signals.

[0041] Step 204: Determine the score of each synthetic signal in the synthetic signal set according to a preset score determination method.

[0042] Taking ECG signals as an example, traditional methods for calculating signal quality include signal-to-noise ratio, spectrum energy ratio, etc. The purpose is to determine whether the R wave of the current signal is clear and whether it is significantly stronger than the P wave and T wave. A good quality ECG signal should have a clear R wave and a higher amplitude than the P wave and T wave. When scoring the signal, it is necessary to comprehensively consider factors such as signal-to-noise ratio, spectrum ratio, and amplitude stability. For example, a signal with a large amplitude, clearness, and stability is better than a signal with a small amplitude, high noise, and unstable amplitude.

[0043] In this embodiment, the score of each biological signal in the biological signal set can be determined according to a preset score determination method. Here, the preset score determination method may include but is not limited to: signal-to-noise ratio, RT amplitude ratio (i.e., the ratio of R wave to T wave), spectrum energy ratio, etc. The calculation formula of the above score determination method can be obtained in advance, and then the parameter value is extracted from each biological signal and substituted into the above calculation formula to obtain the score of each biological signal.

[0044] The score of each synthetic signal is determined according to the selected scoring method and in combination with the weight of each synthetic signal in the synthetic signal set.

[0045] Step 205, select the synthetic signal with the highest score in the synthetic signal set, use the weight coefficient corresponding to each biological signal in the synthetic signal as the optimal weight coefficient, and use the optimal weight coefficient to perform signal synthesis on the subsequent real-time acquired biological signal set to determine the target biological signal.

[0046] After determining the score of each synthetic signal, the synthetic signal with the highest score in the synthetic signal set can be selected and used as the optimal synthetic signal. The weight coefficient corresponding to each biological signal in the optimal synthetic signal is further used as the optimal weight coefficient. The optimal weight coefficient is used to perform signal synthesis on the subsequent biological signal set acquired in real time to determine the target biological signal. Specifically, the weight corresponding to each biological signal can be used to weight each in-phase value in each biological signal to obtain the target biological signal. This can ensure that the target biological signal has the highest score.

[0047] The signal processing method provided by the above-mentioned embodiment of the present disclosure can set a weight coefficient set for the biological signal set of the target object, and cross-match each biological signal according to the weight coefficient set corresponding to each biological signal to obtain a synthetic signal set. Then, the score of each synthetic signal in the synthetic signal set is determined, and the weight coefficient combination corresponding to the synthetic signal with the highest score is used to synthesize each biological signal acquired in real time to obtain the target biological signal, so that the target biological signal can be used to extract the R point, providing an accurate data basis for the aortic counterpulsation.

[0048] Continue to see Figure 3 , which shows a process 300 of another embodiment of the signal processing method according to the present disclosure. Figure 3 As shown, the method in this embodiment may include the following steps:

[0049] Step 301, acquiring a biological signal set of a target object in real time.

[0050] Step 302: Analyze each biometric signal in the biometric signal set within a preset time period to determine the characteristic value of each biometric signal in each period.

[0051] In this embodiment, after obtaining the biological signal set, the biological signal set can be continuously analyzed and monitored to determine the characteristic values ​​of each biological signal in each cycle. Specifically, the biological signals within a preset time length can be analyzed. The above characteristic values ​​can be heart rate, amplitude, cycle length, etc. If the characteristic values ​​of each biological signal are stable within the preset time length, it means that the current physical condition of the target object is stable and subsequent processing can be performed.

[0052] Step 303, matching each biological signal with a corresponding set of weight coefficients, wherein the set of weight coefficients is determined according to a set weight coefficient range and a discrete step length of the weight coefficients.

[0053] Step 304, preprocessing each biological signal; according to each preprocessed biological signal and the weight coefficient set matched with each biological signal, traverse various weight coefficient combinations to obtain a synthetic signal set.

[0054] In this embodiment, each biological signal may be preprocessed first. The preprocessing here may include filtering, differentiation, or direct processing using the Pan-Tompkins algorithm. After preprocessing each biological signal, each synthetic signal is obtained by combining a set of weight coefficients. Specifically, when synthesizing each synthetic signal, each value in each biological signal may be multiplied by the corresponding weight coefficient, and then the product values ​​corresponding to each biological signal are added together to obtain the result.

[0055] Step 305, calculating a dynamic threshold of each synthetic signal, and identifying all peaks smaller than the dynamic threshold; determining a score of each synthetic signal according to the threshold and all peaks.

[0056] In this step, scoring the composite signal is achieved by calculating the signal quality of each composite signal. The ratio r of the dynamic threshold to each peak value less than the dynamic threshold is calculated, and the minimum value of r within a predetermined time range (including but not limited to 1 minute) is defined as the signal quality. According to the definition of signal quality, the local maximum peak less than the dynamic threshold has the greatest impact on detection and the worst signal quality. The smaller the local maximum peak is compared to the dynamic threshold, the better the signal quality.

[0057] For example, for an ECG signal, half of the amplitude of the R wave in the preprocessed ECG signal can be used as the dynamic threshold. Figure 4 , shows the ECG signal before preprocessing, Figure 5 shows the synthesized ECG signal after preprocessing, Figure 5 The figure shows the dynamic threshold (the horizontal line) and all the peaks that are smaller than the dynamic threshold ( Figure 5 The ratio r of the dynamic threshold to each peak value below the horizontal line is calculated, and the minimum value of r within a predetermined time range is selected as the signal quality of the synthetic signal.

[0058] According to the above method, the signal quality of each composite signal in the set of all composite signals is calculated.

[0059] In existing methods, the spectrum method or the signal-to-noise ratio is usually used to evaluate the signal quality of ECG signals. When the spectrum method is used, the T wave and the R wave overlap in frequency, resulting in inaccurate identification of the R wave. The signal-to-noise ratio also uses frequency to evaluate the signal quality, and it also has the disadvantage of inaccurate R wave identification.

[0060] In the ECG signal, signals with a local peak value that is smaller than the selected threshold are usually mistakenly identified as R waves, so this type of signal interferes with the detection the most. The signal quality definition method in this application can effectively filter out the local maximum peak that is smaller than the threshold, making the identification of R waves more accurate. The signal quality performed in this step is closely related to R wave detection. The better the signal quality, the more accurate the identified R wave.

[0061] It should be noted that the selection of the threshold value as half of the R wave amplitude is only exemplary, and the selection of the value can be adjusted according to actual needs or empirical values. The present application does not specifically limit the selection range of the value.

[0062] In this step, the synthesized signal with the best signal quality is the synthesized signal with the highest score in the set of synthesized signals.

[0063] Step 306, select the synthetic signal with the highest score in the synthetic signal set, use the weight coefficient corresponding to each biological signal in the synthetic signal as the optimal weight coefficient, and use the optimal weight coefficient to perform signal synthesis on the subsequent real-time acquired biological signal set to determine the target biological signal.

[0064] After determining the score of each synthetic signal, the weight coefficient corresponding to each biological signal in the synthetic signal is used as the optimal weight coefficient. The above-mentioned optimal weight coefficient is used to synthesize the biological signal set subsequently acquired in real time to determine the target biological signal. It can be understood that the optimal weight coefficient can be unique to the target object. After obtaining the above-mentioned optimal weight coefficient, the above-mentioned optimal weight coefficient can be directly used in the synthesis of biological signals to obtain the target biological signal with the highest quality.

[0065] In some optional implementations of the present embodiment, before using the above-mentioned optimal weight coefficient to perform signal synthesis on the subsequent real-time acquired biosignal set, the synthetic biometric characteristic value corresponding to the synthetic signal with the highest score can also be determined. The biometric characteristic value can be obtained by performing feature recognition and other analysis and processing on the synthetic signal. If the biosignal is an electrocardiogram signal, the biometric characteristic value can be the heart rate or the peak value of the R wave. At the same time, the same analysis and processing method can be used to process each biosignal in the biosignal set to obtain a biometric characteristic value set. Based on the synthetic biometric characteristic value and the biometric characteristic value set, determine whether the optimal weight coefficient is correct. Specifically, if the error between the synthetic biometric characteristic value and each biometric characteristic value in the biometric characteristic value set is less than a preset threshold, it can be determined that the optimal weight coefficient is correct.

[0066] If the optimal weight coefficient is correct, it can ensure that the target biological signal obtained subsequently is correct and of the best quality.

[0067] In some optional implementations of this embodiment, target signal recognition may be performed on the target biological signal, and signal control may be performed according to the occurrence time of the target signal.

[0068] In this implementation, the target biological signal can be further identified, and then further signal control can be performed according to the appearance time of the target signal. For the electrocardiogram signal, R wave detection can be performed, and then aortic counterpulsation can be performed according to the appearance time of the R wave.

[0069] The following is an example of a 3-lead ECG signal. The weight coefficient range coefficient of each lead ECG signal is [-1, 1], and the discrete step length is 0.1, so the weight coefficient of each lead ECG signal can be: -1, -0.9, -0.8...0, 0.1, 0.2,...0.9, 1. The weight coefficients of the 3-lead ECG signals are combined to obtain 9621 combination signals.

[0070] If the weight coefficient of the first lead signal (recorded as ecg1) is 0.3, the weight coefficient of the second lead signal (recorded as ecg2) is 0.5, and the weight coefficient of the third lead signal (recorded as ecg3) is 0.8, the synthesized signal is: ecg_new1 = ecg1*0.3+ecg2*0.5+ecg3*0.8. Calculate the score of the signal, recorded as Q1.

[0071] Change a set of weight coefficients, such as the weight coefficient of the first lead signal is 0.2, the weight coefficient of the second lead signal (recorded as ecg2) is 0.6, and the weight coefficient of the third lead signal (recorded as ecg3) is 0.9, then the synthesized signal is: ecg_new2 = ecg1*0.2+ecg2*0.6+ecg3*0.9. Calculate the score of the signal, recorded as Q2. Traverse all coefficient combinations to obtain 9621 groups of signals, and calculate 9621 signal qualities. The signal with the highest quality is recorded as Qb, and the coefficient combination corresponding to Qb (recorded as s1, s2, s3) is the final coefficient combination. Using the final coefficient combination, synthesize the final signal: ecg = ecg1*s1+ecg2*s2+ecg3*s3. Use this signal for counterpulsation control.

[0072] The signal processing method provided by the above-mentioned embodiment of the present disclosure can set a weight coefficient set for the biological signal set of the target object, and cross-match each biological signal according to the weight coefficient set corresponding to each biological signal to obtain a synthetic signal set. Then, the score of each synthetic signal in the synthetic signal set is determined, and the weight coefficient combination corresponding to the synthetic signal with the highest score is used to synthesize each biological signal acquired in real time to obtain the target biological signal, so that the target biological signal can be used to extract the R point, providing an accurate data basis for the aortic counterpulsation.

[0073] Further references Figure 6 As an implementation of the methods shown in the above figures, the present disclosure provides an embodiment of a signal processing device, and the device embodiment is Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0074] like Figure 6 As shown, the signal processing device 600 of this embodiment includes: a signal acquisition unit 601, a weight determination unit 602, a set determination unit 603, a score determination unit 604 and a signal synthesis unit 605.

[0075] The signal acquisition unit 601 is configured to acquire a biological signal set of the target object in real time, wherein the biological signal set includes at least two biological signals of the same type.

[0076] The weight determination unit 602 is configured to match each biological signal with a corresponding set of weight coefficients, wherein the set of weight coefficients is determined according to a set weight coefficient range and a discrete step length of the weight coefficients.

[0077] The set determination unit 603 is configured to traverse various weight coefficient combinations to obtain a synthetic signal set according to the weight coefficient set matched for each biological signal.

[0078] The score determination unit 604 is configured to determine the score of each synthetic signal in the synthetic signal set according to a preset score determination method.

[0079] The signal synthesis unit 605 is configured to select the synthetic signal with the highest score in the synthetic signal set, use the weight coefficient corresponding to each biological signal in the synthetic signal as the optimal weight coefficient, and use the optimal weight coefficient to perform signal synthesis on the subsequent real-time acquired biological signal set to determine the target biological signal.

[0080] In addition, in the technical solution of the present application, an electronic device is also proposed.

[0081] Figure 7 A schematic structural diagram of an electronic device provided by an embodiment of the present disclosure is shown.

[0082] like Figure 7 As shown, the electronic device may include a processor 701, a memory 702, a bus 703, and a computer program stored in the memory 702 and executable on the processor 701, wherein the processor 701 and the memory 702 communicate with each other through the bus 703. When the processor 701 executes the computer program, the steps of implementing the above method include: acquiring a biological signal set of the target object in real time, wherein the biological signal set includes at least two biological signals of the same type; matching a corresponding weight coefficient set for each biological signal, wherein the weight coefficient set is determined according to a set weight coefficient range and a discrete step length of the weight coefficient; traversing various weight coefficient combinations to obtain a synthetic signal set according to the weight coefficient set matched for each biological signal; determining the score of each synthetic signal in the synthetic signal set according to a preset score determination method; selecting the synthetic signal with the highest score in the synthetic signal set, using the weight coefficient corresponding to each biological signal in the synthetic signal as the optimal weight coefficient, and synthesizing the subsequent biological signal set acquired in real time using the optimal weight coefficient to determine the target biological signal.

[0083] In addition, in one embodiment of the present disclosure, a non-transitory computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented, for example, including: acquiring a set of biological signals of the target object in real time, wherein the biological signal set includes at least two biological signals of the same type; matching a corresponding weight coefficient set for each biological signal, wherein the weight coefficient set is determined according to a set weight coefficient range and a discrete step length of the weight coefficient; according to the weight coefficient set matched for each biological signal, traversing various weight coefficient combinations to obtain a synthetic signal set; determining the score of each synthetic signal in the synthetic signal set according to a preset score determination method; selecting the synthetic signal with the highest score in the synthetic signal set, and using the weight coefficient corresponding to each biological signal in the synthetic signal as the optimal weight coefficient, and using the optimal weight coefficient to perform signal synthesis on the subsequent biological signal set acquired in real time to determine the target biological signal.

[0084] In summary, in the technical solution of the present invention, the biological signal set of the target object can be processed and synthesized to obtain the target biological signal, so that the R point can be extracted more conveniently, providing an accurate data basis for the aortic counterpulsation.

[0085] The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present disclosure shall be included in the scope of protection of the present disclosure.

Claims

1. A signal processing method, comprising: Acquire a biological signal set of a target object in real time, wherein the biological signal set includes at least two biological signals of the same type; Matching a corresponding weight coefficient set for each biological signal, wherein the weight coefficient set is determined according to a set weight coefficient range and a discrete step length of the weight coefficient; According to the weight coefficient set matched for each biological signal, various weight coefficient combinations are traversed to obtain a synthetic signal set; Determining a score for each synthetic signal in the synthetic signal set according to a preset score determination method; The synthetic signal with the highest score in the synthetic signal set is selected, and the weight coefficient corresponding to each biological signal in the synthetic signal is used as the optimal weight coefficient. The optimal weight coefficient is used to perform signal synthesis on the subsequent biological signal set acquired in real time to determine the target biological signal.

2. The method according to claim 1, wherein: The method further comprises: Determine a synthetic biometric feature value corresponding to the synthetic signal with the highest score; Determine the biometric value corresponding to each biometric signal to obtain a set of biometric values; Whether the optimal weight coefficient is correct is determined according to the synthetic biometric feature value and the biometric feature value set.

3. The method according to claim 2, wherein: The method further comprises: In response to determining that the optimal weight coefficient is correct, signal control is performed using the target biosignal.

4. The method according to claim 3, wherein: The using the target biological signal to perform signal control includes: The target biological signal is identified as a target signal, and signal control is performed according to the occurrence time of the target signal.

5. The method according to claim 1, wherein: Before obtaining the target biological signal, the method further includes: Each biological signal in the biological characteristic signal set within a preset time period is analyzed to determine whether the characteristic value of each biological characteristic signal in each period is stable.

6. The method according to claim 1, wherein: The method is based on a weight coefficient set matched to each biological signal, traversing various weight coefficient combinations to obtain a synthetic signal set, including: Preprocessing each biological signal; According to the preprocessed biological signals and the weight coefficient sets matched to the biological signals, various weight coefficient combinations are traversed to obtain a synthetic signal set.

7. The method according to claim 6, wherein: The step of determining the score of each synthetic signal in the synthetic signal set according to a preset score determination method includes: Calculate the dynamic threshold of the synthetic signal; Identify all peaks that are smaller than the dynamic threshold; The score of the synthetic signal is determined according to the dynamic threshold and all peak values.

8. A signal processing device, comprising: a signal acquisition unit, configured to acquire a biological signal set of the target object in real time, wherein the biological signal set includes at least two biological signals of the same type; A weight determination unit, configured to match each biological signal with a corresponding weight coefficient set, wherein the weight coefficient set is determined according to a set weight coefficient range and a discrete step length of the weight coefficient; a set determination unit configured to traverse various weight coefficient combinations to obtain a synthetic signal set according to a weight coefficient set matched for each biological signal; a score determination unit, configured to determine a score of each synthetic signal in the synthetic signal set according to a preset score determination method; The signal synthesis unit is configured to select the synthetic signal with the highest score in the synthetic signal set, use the weight coefficient corresponding to each biological signal in the synthetic signal as the optimal weight coefficient, and use the optimal weight coefficient to perform signal synthesis on the subsequent real-time acquired biological signal set to determine the target biological signal.

9. An electronic device comprising a memory, a processor, a bus, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the signal processing method according to any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the signal processing method according to any one of claims 1 to 7 is implemented.

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