Signal processing methods, devices, electronic equipment and storage media

Abstract

This disclosure relates to a signal processing method, apparatus, electronic device, and storage medium, and pertains to the field of signal processing. The method includes: acquiring a set of biological signals of a target object in real time; matching a set of corresponding weight coefficients to each biological signal; traversing various combinations of weight coefficients based on the set of weight coefficients matched for each biological signal to obtain a set of synthetic signals; determining a score for each synthetic signal in the set of synthetic signals according to a preset scoring method; selecting the synthetic signal with the highest score in the set of synthetic signals, using the weight coefficients corresponding to each biological signal in that synthetic signal as the optimal weight coefficients, and using the optimal weight coefficients to synthesize signals from the subsequently acquired set of biological signals in real time to determine the target biological signal. Applying this disclosure can provide an accurate data foundation for aortic counterpulsation.

Description

Technical Field

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

[0002] Current aortic counterpulsation techniques employ the RR interval prediction method. During the final stage of left ventricular pumping, air is inhaled to assist pumping and increase ejection volume. A certain time before the next left ventricular pumping phase, inspiration creates a negative pressure differential between the artery and the left ventricle, reducing the left ventricular pumping load. Therefore, accurate R-wave identification is a crucial prerequisite for this technique. Failure to accurately identify the R-wave can lead to counterpulsation errors, posing a life-threatening risk to the patient. Thus, accurate R-wave identification is the most important prerequisite for the application of this technique.

[0003] Electrocardiography (ECG), also known as electrocardiography, is a diagnostic technique that records the electrophysiological activity of the heart over time via the chest cavity and captures and records the data through electrodes on the skin. The R-point is the most prominent point on an ECG, and accurate detection of the R-point is fundamental to automated ECG analysis. R-point detection methods are a hot topic in ECG research. Existing R-point detection methods are primarily based on single-lead ECG waveforms or the main analysis lead in a multi-lead ECG. These methods struggle to detect R-points when leads are lost, signal peaks are low, or interference is significant. Furthermore, they are difficult to eliminate the influence of spikes, tall T waves, and drift interference, resulting in high computational complexity, poor real-time performance, and complex algorithms. Summary of the Invention

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

[0005] In a first aspect, embodiments of this disclosure provide a signal processing method, comprising: acquiring a set of biological signals of a target object in real time, wherein the set of biological signals includes at least two biological signals of the same type; matching a set of corresponding weight coefficients for each biological signal, wherein the set of weight coefficients is determined according to a set of weight coefficient ranges and weight coefficient deviations; traversing various combinations of weight coefficients to obtain a set of synthetic signals based on the set of weight coefficients matched for each biological signal; determining a score for each synthetic signal in the set of synthetic signals according to a preset scoring method; selecting the synthetic signal with the highest score in the set of synthetic signals, using the weight coefficients corresponding to each biological signal in the synthetic signal as the optimal weight coefficients, and using the optimal weight coefficients to synthesize signals from the subsequently acquired set of biological signals in real time to determine the target biological signal.

[0006] Secondly, embodiments of this disclosure provide a signal processing apparatus, comprising: a signal acquisition unit configured to acquire a set of biological signals of a target object in real time, wherein the set of biological signals includes at least two biological signals of the same type; a weight determination unit 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 of weight coefficient ranges and weight coefficient deviations; a set determination unit configured to traverse various combinations of weight coefficients according to the set of weight coefficients matched for each biological signal to obtain a set of synthetic signals; a scoring determination unit configured to determine the score of each synthetic signal in the set of synthetic signals according to a preset scoring determination method; and a signal synthesis unit configured to select the synthetic signal with the highest score in the set of synthetic signals, use the weight coefficients corresponding to each biological signal in the synthetic signal as the optimal weight coefficients, and use the optimal weight coefficients to synthesize signals from the subsequently acquired set of biological signals in real time to determine the target biological signal.

[0007] Thirdly, embodiments of this disclosure provide an electronic device including a memory, a processor, a bus, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the signal processing method as described in the first aspect.

[0008] Fourthly, embodiments of this disclosure provide a non-transitory computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the signal processing method as described in the first aspect.

[0009] By applying the technical solution disclosed herein, the biological signal set of the target object can be processed and synthesized to obtain the target biological signal, thereby making it easier to extract the R point and provide an accurate data basis for aortic counterpulsation.

[0010] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0011] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein:

[0012] Figure 1 An exemplary system architecture diagram in which an embodiment of the signal processing method of this disclosure can be applied;

[0013] Figure 2 This is a schematic flowchart of an embodiment of the signal processing method disclosed herein;

[0014] Figure 3This is a flowchart illustrating yet another embodiment of the signal processing method disclosed herein;

[0015] Figure 4 This is a schematic diagram of the central electrical signal in the signal processing method of this disclosure;

[0016] Figure 5 This is a schematic diagram of the preprocessed electrocardiogram signal and threshold in the signal processing method of this disclosure;

[0017] Figure 6 This is a schematic diagram of the structure of one embodiment of the signal processing apparatus of this disclosure;

[0018] Figure 7 This is a schematic diagram of the structure of an embodiment of the electronic device disclosed herein. Detailed Implementation

[0019] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of this disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains.

[0020] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this disclosure. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms “comprising” and / or “including” are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0021] Where there is no conflict, the embodiments and features described herein can be combined with each other.

[0022] To make the technical solutions and advantages of this disclosure clearer, the following description, in conjunction with the accompanying drawings and specific embodiments, will provide a more detailed account of this disclosure.

[0023] Figure 1 An exemplary system architecture 100 is shown that can be applied to embodiments of the signal processing methods or signal processing apparatus disclosed herein.

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

[0025] Users can use terminal device 101 to acquire biological signals collected by biological signal acquisition devices 103, 104, and 105 via network 102, and perform various processing on the biological signals. Simultaneously, the processed biological signals can be output or used for signal control.

[0026] Terminal device 101 can be hardware or software. When terminal device 101 is hardware, it can be various electronic devices, including but not limited to smartphones, tablets, in-vehicle computers, laptops, and desktop computers. When terminal device 101 is software, it can be installed in the electronic devices listed above. It can be implemented as multiple software programs or software modules (e.g., to provide distributed services) or as a single software program or software module. No specific limitations are made here.

[0027] Biosignal acquisition devices 103, 104, and 105 can be applied to a target object to collect its biosignals. The target object can be any type of living organism, such as a human or animal. The types of biosignals collected by biosignal acquisition devices 103, 104, and 105 can be the same or different. These biosignal acquisition devices 103, 104, and 105 may include, but are not limited to, electrocardiogram monitors, blood pressure monitors, and venous signal collectors. Correspondingly, the biosignals may include, but are not limited to, electrocardiogram signals, blood pressure signals, and venous signals. Biosignal acquisition devices 103, 104, and 105 can transmit the collected biosignals from the target object to a terminal device 101, enabling the terminal device 101 to process the biosignals.

[0028] It should be noted that the signal processing method provided in this embodiment 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 biosignal acquisition devices shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and biosignal acquisition devices can be included.

[0030] Figure 2 A flow 200 of one embodiment of the signal processing method of this disclosure is shown. For example... Figure 2 As shown, the signal processing method in this embodiment may include the following steps:

[0031] Step 201: Acquire the set of biological signals of the target object in real time.

[0032] In this embodiment, the execution subject of the signal processing method (e.g. Figure 1The terminal device 101 shown can acquire signals from various biosignal acquisition devices (e.g., Figure 1 The biosignal acquisition devices 103, 104, and 105 shown acquire a set of biosignals from the target object in real time. Here, the set of biosignals includes at least two biosignals of the same type. For example, they can all be electrocardiogram signals, or they can all be blood pressure signals, etc.

[0033] Step 202 involves matching each biological signal with a corresponding set of weight coefficients, wherein the set of weight coefficients is determined based on a set range of weight coefficients and a weight coefficient offset.

[0034] In this embodiment, the weighting coefficient range and offset distance corresponding to each biological signal can be determined. Here, the weighting coefficient range and offset distance corresponding to different biological signals can be the same or different. For example, the weighting coefficient range and offset distance can be determined by the acquisition location of the biological signal. Taking electrocardiogram (ECG) signals as an example, multiple electrodes can be used to acquire ECG signals, including twelve leads: I, II, III, aVL, aVF, aVR, v1, v2, v3, v4, v5, and v6. The electrodes corresponding to each lead are different. When determining the weighting coefficient range and offset distance corresponding to each lead, it can be determined based on the electrode location corresponding to each lead. Alternatively, the weighting coefficient range and offset distance corresponding to each biological signal can be preset. The weighting coefficient range represents the value between the minimum and maximum values ​​of the weight, and the offset distance represents the difference between two adjacent weight values.

[0035] After determining the weight coefficient range and offset step size for each biological signal, the set of weight coefficients for each biological signal can be determined. Taking a weight coefficient range of [-1, 1] and an offset step size of 0.5 as an example, the set of weight coefficients can include 5 weight values: -1, -0.5, 0, 0.5, and 1.

[0036] Step 203: Based on the set of weight coefficients matched for each biological signal, traverse various combinations of weight coefficients to obtain a set of synthetic signals.

[0037] After determining the set of weighted coefficients for each biological signal, various combinations of weighted coefficients can be iterated to obtain a set of synthetic signals. Specifically, each weighted coefficient in the set of weighted coefficients for a single biological signal can be matched with each weighted coefficient in the set of weighted coefficients for other biological signals to obtain multiple matched weighted coefficients. Then, each matched weighted coefficient is combined with each biological signal to obtain multiple synthetic signals, which is the set of synthetic signals.

[0038] For example, n lead signals are selected, and the weight coefficients matched for each lead signal are in the range of [-N, N]. The set of weight coefficients has a weight values ​​according to the discrete step length: M1, M2, ..., Ma.

[0039] The weighted coefficients that can be matched for the first lead signal are M1, M2, ..., Ma, the weighted coefficients that can be matched for the second lead signal are M1, M2, ..., Ma, and the weighted coefficients that can be matched for the nth lead signal are M1, M2, ..., Ma. Then, there are a total of a weighted coefficients that can be matched for the nth lead signal. n There are a number of numerical combinations, namely a n synthesized signal.

[0040] Let's take a 3-lead ECG signal as an example. The weighting coefficient for each lead is in the range [-1, 1], and the distance between the lead and the step size is 0.1. Therefore, the weighting coefficients for each lead can be: -1, -0.9, -0.8...0, 0.1, 0.2,...0.9, 1, a total of 21 weighting values. Combining the weighting coefficients of the 3 leads yields 21... 3 =9261 synthesized signals.

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

[0042] Taking electrocardiogram (ECG) signals as an example, traditional methods for calculating signal quality include signal-to-noise ratio (SNR) and spectral energy ratio (SGR). These methods aim to determine whether the R wave of the current signal is clear and significantly stronger than the P and T waves. A high-quality ECG signal should have a clear R wave with a higher amplitude than the P and T waves. When scoring a signal, factors such as SNR, SGR, and amplitude stability need to be considered comprehensively. For example, a signal with a large, clear, and stable amplitude 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 scoring method. Here, the preset scoring 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), spectral energy ratio, etc. The calculation formulas for the above scoring methods can be obtained in advance, and then parameter values ​​are extracted from each biological signal and substituted into the above calculation formulas to obtain the score of each biological signal.

[0044] The score for each synthetic signal is determined based on the selected scoring method and the weights 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, take the weight coefficients corresponding to each biological signal in the synthetic signal as the optimal weight coefficients, and use the optimal weight coefficients to synthesize signals from the subsequent real-time acquired biological signal set to determine the target biological signal.

[0046] After determining the scores of each synthesized signal, the synthesized signal with the highest score in the set can be selected as the optimal synthesized signal. The weight coefficients corresponding to each biological signal in this optimal synthesized signal are then used as optimal weight coefficients. These optimal weight coefficients are then used to synthesize signals from subsequent real-time acquired biological signal sets to determine the target biological signal. Specifically, the weights 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 ensures that the target biological signal has the highest possible score.

[0047] The signal processing method provided in the above embodiments of this disclosure can set a set of weight coefficients for a set of biological signals of a target object, and perform cross-matching on each biological signal according to the set of weight coefficients corresponding to each biological signal to obtain a set of synthetic signals. Then, the score of each synthetic signal in the set of synthetic signals is determined, and the weight coefficient combination corresponding to the synthetic signal with the highest score is used to synthesize the subsequent real-time acquired biological signals to obtain the target biological signal. This enables the extraction of the R-point using the target biological signal, providing an accurate data basis for aortic counterpulsation.

[0048] See also Figure 3 This illustrates flow 300 of another embodiment of the signal processing method according to this disclosure. Figure 3 As shown, the method in this embodiment may include the following steps:

[0049] Step 301: Acquire the set of biological signals of the target object in real time.

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

[0051] In this embodiment, after acquiring the set of biological signals, the set can be continuously analyzed and monitored to determine the characteristic values ​​of each biological signal in each cycle. Specifically, biological signals within a preset duration can be analyzed. These characteristic values ​​can include heart rate, amplitude, cycle length, etc. If the characteristic values ​​of each biological signal are stable within the preset duration, it indicates that the target object's current physical condition is stable, and subsequent processing can proceed.

[0052] Step 303 involves matching each biological signal with a corresponding set of weight coefficients, wherein the set of weight coefficients is determined based on a set range of weight coefficients and a weight coefficient offset.

[0053] Step 304: Preprocess each biological signal; based on the preprocessed biological signals and the set of weight coefficients matched by each biological signal, traverse various combinations of weight coefficients to obtain a synthetic signal set.

[0054] In this embodiment, each biological signal can first be preprocessed. This preprocessing can include filtering, differential processing, or direct processing using the Pan-Tompkins algorithm. After preprocessing each biological signal, the combined weight coefficients are used to obtain the synthesized signals. Specifically, when synthesizing each signal, each value in each biological signal can be multiplied by its corresponding weight coefficient, and then the products of each biological signal are summed.

[0055] Step 305: Calculate the dynamic threshold of each synthesized signal and identify all peaks below the dynamic threshold; determine the score of each synthesized signal based on the threshold and all peaks.

[0056] In this step, the synthesized signals are scored by calculating the signal quality of each synthesized signal. The ratio *r* of a dynamic threshold to each peak value below 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, local maximum peaks below the dynamic threshold have 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 electrocardiogram (ECG) signals, half the amplitude of the R wave in the preprocessed ECG signal can be used as the dynamic threshold. See [link to relevant documentation]. Figure 4 This shows the electrocardiogram signal before preprocessing. Figure 5 The synthesized electrocardiogram after preprocessing is shown. Figure 5 The diagram shows the dynamic threshold (the horizontal line) and all peak values ​​identified that are below the dynamic threshold. Figure 5 (Multiple bold black curves located below the horizontal line). Calculate the ratio r of the dynamic threshold to each peak value located below the horizontal line, and select the minimum value of r within a predetermined time range as the signal quality of the synthesized signal.

[0058] Based on the above method, the signal quality of each synthesized signal in all synthesized signal sets is calculated.

[0059] Existing methods typically employ spectral analysis or signal-to-noise ratio (SNR) to assess ECG signal quality. However, when using spectral analysis, the overlap in frequency between the T and R waves leads to inaccurate R-wave identification. Similarly, SNR, which also assesses signal quality based on frequency, suffers from the drawback of inaccurate R-wave identification.

[0060] In electrocardiogram (ECG) signals, those below a selected threshold but with localized peaks are often misidentified as R-waves, thus posing the greatest interference to detection. The signal quality definition method in this application effectively filters out these localized peaks below the threshold, making R-wave identification more accurate. The signal quality in this step is closely related to R-wave detection. The better the signal quality, the more accurate the R-wave identification.

[0061] It should be noted that selecting half of the R-wave amplitude as the threshold is merely an example, and the value can be adjusted according to actual needs or empirical values. This application does not specifically limit the range of values ​​to be selected.

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

[0063] Step 306: Select the synthetic signal with the highest score in the synthetic signal set, take the weight coefficients corresponding to each biological signal in the synthetic signal as the optimal weight coefficients, and use the optimal weight coefficients to synthesize signals from the subsequent real-time acquired biological signal set to determine the target biological signal.

[0064] After determining the score of each synthesized signal, the weight coefficient corresponding to each biological signal in the synthesized signal is taken as the optimal weight coefficient. This optimal weight coefficient is then used to synthesize signals from subsequent real-time acquired biological signal sets to determine the target biological signal. It is understood that the optimal weight coefficient can be unique for the target object. Having obtained the optimal weight coefficient, it can be directly used during biological signal synthesis to obtain the highest quality target biological signal.

[0065] In some optional implementations of this embodiment, before synthesizing the subsequently acquired biological signal set using the aforementioned optimal weighting coefficients, the synthetic biosignal value corresponding to the highest-scoring synthetic signal can be determined. This biosignal value can be obtained through feature recognition and other analytical processing of the synthetic signal. If the biological signal is an electrocardiogram (ECG) signal, the biosignal value can be the heart rate or the peak value of the R-wave. Simultaneously, the same analytical processing method can be used to process each biological signal in the biological signal set to obtain a set of biosignal values. Based on the synthetic biosignal values ​​and the set of biosignal values, the correctness of the optimal weighting coefficients is determined. Specifically, if the error between the synthetic biosignal values ​​and each biosignal value in the set of biosignal values ​​is less than a preset threshold, the optimal weighting coefficients can be considered correct.

[0066] If the optimal weighting coefficients are correct, it can be guaranteed that the subsequent target biological signals obtained are correct and of the best quality.

[0067] In some optional implementations of this embodiment, target signal identification can also be performed on the target biosignal, and signal control can be performed based on the time of occurrence of the target signal.

[0068] In this implementation, target biosignal identification can be further performed. Then, further signal control is implemented based on the timing of the target signal's appearance. For electrocardiogram signals, R-wave detection can be performed, and aortic counterpulsation can be initiated based on the timing of the R-wave's appearance.

[0069] The following explanation uses a three-lead ECG signal as an example. The weighting coefficients for each lead are in the range [-1, 1], with a step size of 0.1. Therefore, the weighting coefficients for each lead can be: -1, -0.9, -0.8...0, 0.1, 0.2,...0.9, 1. Combining the weighting coefficients of the three leads yields 9621 possible signal combinations.

[0070] If the weighting coefficient for the signal in lead 1 (denoted as ecg1) is 0.3, the weighting coefficient for the signal in lead 2 (denoted as ecg2) is 0.5, and the weighting coefficient for the signal in lead 3 (denoted as ecg3) is 0.8, then the synthesized signal is: ecg_new1 = ecg1 * 0.3 + ecg2 * 0.5 + ecg3 * 0.8. Calculate the score of this signal and denot it as Q1.

[0071] By changing the weighting coefficients, for example, the weighting coefficient for lead 1 (denoted as ecg2) is 0.2, the weighting coefficient for lead 2 (denoted as ecg2) is 0.6, and the weighting coefficient for lead 3 (denoted as ecg3) is 0.9, the synthesized signal is: ecg_new2 = ecg1*0.2 + ecg2*0.6 + ecg3*0.9. The score of this signal is calculated and denoted as Q2. By iterating through all coefficient combinations, 9621 signal combinations are obtained, and the quality of each signal is calculated. The signal with the highest quality is denoted as Qb, and the coefficient combination corresponding to Qb (denoted as s1, s2, s3) is the final coefficient combination. Using the final coefficient combination, the final signal is synthesized: ecg = ecg1*s1 + ecg2*s2 + ecg3*s3. This signal is then used for counterpulsation control.

[0072] The signal processing method provided in the above embodiments of this disclosure can set a set of weight coefficients for a set of biological signals of a target object, and perform cross-matching on each biological signal according to the set of weight coefficients corresponding to each biological signal to obtain a set of synthetic signals. Then, the score of each synthetic signal in the set of synthetic signals is determined, and the weight coefficient combination corresponding to the synthetic signal with the highest score is used to synthesize the subsequent real-time acquired biological signals to obtain the target biological signal. This enables the extraction of the R-point using the target biological signal, providing an accurate data basis for aortic counterpulsation.

[0073] Further reference Figure 6 As an implementation of the methods shown in the above figures, this disclosure provides an embodiment of a signal processing apparatus, which is similar to... Figure 2 Corresponding to the method embodiments shown, this 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 scoring determination unit 604, and a signal synthesis unit 605.

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

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

[0077] The set determination unit 603 is configured to traverse various combinations of weight coefficients to obtain a synthetic signal set based on a set of weight coefficients matched for each biological signal.

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

[0079] The signal synthesis unit 605 is configured to select the highest-scoring synthetic signal from the set of synthetic signals, use the weight coefficients corresponding to each biological signal in the synthetic signal as the optimal weight coefficients, and use the optimal weight coefficients to synthesize signals from the set of biological signals acquired in real time to determine the target biological signal.

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

[0081] Figure 7 A schematic diagram of the structure of an electronic device provided in one 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. The processor 701 and the memory 702 communicate with each other via the bus 703. When the processor 701 executes the computer program, it implements the steps of the above method, including, for example: acquiring a set of biological signals of the target object in real time, wherein the set of biological signals includes at least two biological signals of the same type; matching a corresponding set of weight coefficients for each biological signal, wherein the set of weight coefficients is determined according to a set of weight coefficient ranges and weight coefficient deviations; traversing various combinations of weight coefficients to obtain a set of synthetic signals based on the set of weight coefficients matched for each biological signal; determining the score of each synthetic signal in the set of synthetic signals according to a preset scoring method; selecting the synthetic signal with the highest score in the set of synthetic signals, using the weight coefficients corresponding to each biological signal in the synthetic signal as the optimal weight coefficients, and using the optimal weight coefficients to synthesize signals from the subsequently acquired set of biological signals in real time to determine the target biological signal.

[0083] In addition, one embodiment of this disclosure also provides a non-transitory computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements the steps of the above-described method, including, for example,: acquiring a set of biological signals of a target object in real time, wherein the set of biological signals includes at least two biological signals of the same type; matching a set of corresponding weight coefficients for each biological signal, wherein the set of weight coefficients is determined according to a set of weight coefficient ranges and weight coefficient deviations; traversing various combinations of weight coefficients to obtain a set of synthetic signals based on the set of weight coefficients matched for each biological signal; determining the score of each synthetic signal in the set of synthetic signals according to a preset scoring method; selecting the synthetic signal with the highest score in the set of synthetic signals, using the weight coefficients corresponding to each biological signal in the synthetic signal as the optimal weight coefficients, and using the optimal weight coefficients to synthesize signals from the subsequently acquired set of biological signals in real time to determine the target biological signal.

[0084] In summary, the technical solution disclosed herein can process and synthesize the biological signal set of the target object to obtain the target biological signal, thereby making it easier to extract the R point and provide an accurate data basis for aortic counterpulsation.

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

Claims

1. A signal processing method, comprising: Real-time acquisition of a set of biological signals of a target object, wherein the set of biological signals includes at least two biological signals of the same type; A set of weighted coefficients is matched to each biological signal, wherein the set of weighted coefficients is determined according to a set range of weighted coefficients and a weighted coefficient offset. Based on the set of weight coefficients matched for each biological signal, various combinations of weight coefficients are traversed to obtain a set of synthetic signals; The score of each synthetic signal in the synthetic signal set is determined according to the preset scoring method; The highest-scoring synthetic signal in the synthetic signal set is selected, and the weight coefficients corresponding to each biological signal in the synthetic signal are taken as the optimal weight coefficients. The optimal weight coefficients are then used to synthesize signals from the subsequent real-time acquired biological signal set to determine the target biological signal. The step of determining the score of each synthetic signal in the synthetic signal set according to a preset scoring method includes: calculating a dynamic threshold of the synthetic signal; identifying all peak values ​​less than the dynamic threshold; and determining the score of the synthetic signal based on the dynamic threshold and all peak values. The step of determining the score of the synthesized signal based on the dynamic threshold and all peak values ​​includes: Calculate the ratio r of the dynamic threshold to each peak value less than the dynamic threshold, and define the minimum value of r within a predetermined time range as the signal quality; determine the score of the synthesized signal based on the signal quality of the synthesized signal.

2. The method according to claim 1, wherein, The method further includes: Determine the synthetic biosignature value corresponding to the synthetic signal with the highest score; The biological feature values ​​corresponding to each biological signal are determined to obtain the set of biological feature values; Based on the synthetic biological feature value and the set of biological feature values, determine whether the optimal weight coefficient is correct.

3. The method according to claim 2, wherein, The method further includes: In response to determining that the optimal weighting coefficients are correct, signal control is performed using the target biological signal.

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

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

6. The method according to claim 1, wherein, The process involves iterating through various combinations of weight coefficients based on a set of weight coefficients matched for each biological signal to obtain a synthetic signal set, including: Preprocessing of various biological signals; Based on the preprocessed biological signals and the set of weight coefficients matched for each biological signal, various combinations of weight coefficients are traversed to obtain a set of synthetic signals.

7. A signal processing apparatus, comprising: The signal acquisition unit is configured to acquire a set of biological signals of a target object in real time, wherein the set of biological signals includes at least two biological signals of the same type; The weight determination unit is configured to match each biological signal with a set of weight coefficients, wherein the set of weight coefficients is determined according to a set weight coefficient range and a weight coefficient deviation. The set determination unit is configured to traverse various combinations of weight coefficients to obtain a synthetic signal set based on the set of weight coefficients matched for each biological signal; The scoring determination unit is configured to determine the score of each synthetic signal in the synthetic signal set according to a preset scoring determination method; wherein, determining the score of each synthetic signal in the synthetic signal set according to the preset scoring determination method includes: calculating a dynamic threshold of the synthetic signal; identifying all peak values ​​less than the dynamic threshold; and determining the score of the synthetic signal based on the dynamic threshold and all peak values. The step of determining the score of the synthesized signal based on the dynamic threshold and all peak values ​​includes: Calculate the ratio r of the dynamic threshold to each peak value less than the dynamic threshold, and define the minimum value of r within a predetermined time range as the signal quality; determine the score of the synthesized signal based on the signal quality of the synthesized signal. The signal synthesis unit is configured to select the highest-scoring synthetic signal from the set of synthetic signals, use the weight coefficients corresponding to each biological signal in the synthetic signal as the optimal weight coefficients, and use the optimal weight coefficients to synthesize signals from the set of biological signals acquired in real time to determine the target biological signal.

8. 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, it implements the signal processing method as described in any one of claims 1 to 6.

9. 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, it implements the signal processing method as described in any one of claims 1 to 6.

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