Bioelectric signal processing method and device, storage medium and program product

Abstract

The invention discloses a bio-electricity signal processing method and device, a storage medium and a program product, and relates to the technical field of biological information.The bio-electricity signal processing method comprises the steps that a target working mode of a bio-electricity signal monitoring device is set, and the bio-electricity signal monitoring device comprises a plurality of electronic devices; each electronic device is deployed in at least one body area of the user; acquiring electrical parameters of each electronic device of the bio-electricity signal monitoring device in the target working mode; determining bio-electricity signal changes according to the electrical parameters; and determining the probability of disease attack according to the change of the bio-electricity signal. By monitoring the change of the bio-electricity signal, the health condition of the patient is monitored in real time, timely prediction is carried out before disease attack, and the risk of disease attack is reduced.

Description

Technical Field

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

[0002] In recent years, real-time analysis and prediction of bioelectric signals have shown great potential in medical diagnosis and treatment. Bioelectric signals such as brain wave signals, electrocardiogram signals and electromyography signals can reflect various physiological processes and disease states of the human body. By observing the dynamic changes of these signals, it can help detect diseases early, predict the development trend of the disease, and even assist doctors in formulating treatment plans.

[0003] Although the number of hardware studies on ECG and EEG signals is increasing, most studies still focus on signal analysis after the onset of disease, while there are relatively few studies on timely prediction before the onset of disease. Typical bioelectric signal processing hardware systems are based on silicon-based complementary metal oxide semiconductor technology. In the digital computing unit, large-scale integrated circuits are used to compress and process bioelectric signals in the digital field, or complex device combination architecture arrays are designed to directly process analog information at the onset of disease. This processing method based on existing signals makes it difficult to predict future signals, limiting the possibility of early disease detection and intervention, and the risk of disease onset is high. Summary of the invention

[0004] The main purpose of this application is to provide a bioelectric signal processing method, device, storage medium and program product, aiming to reduce the risk of disease onset.

[0005] Setting a target operating mode of a bioelectric signal monitoring device, wherein the bioelectric signal monitoring device comprises a plurality of electronic devices, each of which is deployed in at least one body region of a user;

[0006] Acquiring electrical parameters of each electronic component of the bioelectric signal monitoring device under the target working mode;

[0007] Determining bioelectric signal changes according to the electrical parameters;

[0008] The probability of disease onset is determined based on the changes in the bioelectric signal.

[0009] In one embodiment, the step of setting the target working mode of the bioelectric signal monitoring device includes:

[0010] Determining the layout of the electronic components in the bioelectric signal monitoring device;

[0011] When the layout mode is array distribution, setting the target working mode to a simultaneous operation mode;

[0012] When the layout mode is a scattered distribution, the target working mode is set to an asynchronous operation mode.

[0013] In one embodiment, the step of determining the layout of the electronic components in the bioelectric signal monitoring device includes:

[0014] The layout of the electronic components in the bioelectric signal monitoring device is determined according to the type of the bioelectric signal to be monitored.

[0015] In one embodiment, after the step of determining the layout of the electronic components in the bioelectric signal monitoring device, the method further includes:

[0016] In the case where the layout is array distribution, different array dimensions are set, wherein the array dimension is the dimension of the arrangement array of the electronic components of the bioelectric signal monitoring device.

[0017] In one embodiment, the step of determining the change of the bioelectric signal according to the electrical parameter comprises:

[0018] The bioelectric signal change is determined according to the association relationship between the electrical parameter and the preset electrical parameter and the bioelectric signal.

[0019] In one embodiment, before the step of determining the change of the bioelectric signal according to the electrical parameter and the preset correlation relationship, the method further includes:

[0020] Performing an electrical characteristic test on each of the electronic devices according to the acquired analog signal, wherein the analog signal is determined based on the bioelectric signal of the disease to be predicted;

[0021] The electrical parameter changes of each of the electronic devices are extracted, and the correlation between the electrical parameter changes and the bioelectrical signals is determined.

[0022] In one embodiment, after the step of determining the bioelectric signal change, the method further includes:

[0023] According to the changes of the bioelectric signal, drawing a spatiotemporal change map;

[0024] The initial location of the disease onset is determined based on the spatiotemporal variation map.

[0025] In addition, to achieve the above-mentioned purpose, the present application also proposes a bioelectric signal processing device, the bioelectric signal processing device comprising:

[0026] A mode setting module, used to form a bioelectric signal monitoring device by deploying various electronic devices in the body area;

[0027] A parameter acquisition module, used to coordinate the working relationship of the electronic components of the bioelectric signal monitoring device;

[0028] a change determination module, for determining a bioelectric signal change through a coordinated change in an electrical parameter of the bioelectric signal monitoring device;

[0029] The probability determination module is used to determine the probability of disease onset based on the changes in the bioelectric signal.

[0030] In addition, to achieve the above-mentioned purpose, the present application also proposes a bioelectric signal processing device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, and the computer program is configured to implement the steps of the bioelectric signal processing method described above.

[0031] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the bioelectric signal processing method described above are implemented.

[0032] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the bioelectric signal processing method described above are implemented.

[0033] One or more technical solutions proposed in the present application have at least the following technical effects: first, a target working mode of the biosignal monitoring device is set, wherein the bioelectric signal monitoring device includes multiple electronic devices, each of which is deployed in at least one body area of ​​the user to comprehensively and continuously capture weak electrical signals in the body. By setting the working mode, the consistency and accuracy of signal monitoring are ensured, and real-time monitoring of bioelectric signals in the body is realized; further, by obtaining the electrical parameters of each electronic device of the bioelectric signal monitoring device under the target working mode, a reliable data basis is provided for subsequent analysis of changes in bioelectric signals; then, based on the electrical parameters, the changes in bioelectric signals are determined. Since changes in bioelectric signals in the body will cause changes in the electrical parameters of each electronic device, observing the electrical parameters helps to promptly detect abnormal changes in bioelectric signals and increase the possibility of early detection of disease onset; finally, based on the changes in bioelectric signals, the probability of disease onset is determined, thereby achieving timely prediction before the onset of the disease, gaining valuable treatment time for patients and reducing the risk of disease onset. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0035] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0036] Figure 1 A schematic diagram of a process flow provided for Embodiment 1 of the bioelectric signal processing method of the present application;

[0037] Figure 2 A schematic diagram of the array distribution of electronic devices provided in Example 1 of the present application;

[0038] Figure 3 A schematic diagram of a brief process of the bioelectric signal processing method provided in Example 2 of the present application;

[0039] Figure 4 This is a schematic diagram of the module structure of the bioelectric signal processing device according to an embodiment of the present application;

[0040] Figure 5 Schematic diagram of the device structure of the hardware operating environment involved in the bioelectric signal processing method in the embodiment of the present application. DETAILED DESCRIPTION

[0041] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0042] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0043] In this embodiment, for the convenience of description, the following description is made with the terminal as the execution subject.

[0044] In conventional technology, the analysis and prediction of bioelectric signals mainly rely on advanced digital signal processing technology and deep learning algorithms. By statistically analyzing large-scale bioelectric signal data sets, characteristic waveforms related to the occurrence or impending occurrence of diseases are extracted, thereby providing strong support for early detection, diagnosis and formulation of treatment plans for diseases. However, this method cannot run independently without cloud computing, which limits its application in mobile scenarios. In terms of hardware, large-scale and complex integrated circuit technology makes it difficult to predict future signals, and must rely on a stable off-chip power supply, making the analysis and prediction system difficult to move and carry.

[0045] The present application provides a solution, according to the category of the bioelectric signal to be monitored, the layout of each electronic device in the bioelectric signal monitoring device is determined, and the electronic devices distributed in the array are set to operate simultaneously, so as to obtain the bioelectric signal more accurately and comprehensively; the electronic devices distributed at scattered points are set to operate asynchronously to reduce the invalid monitoring of some areas; then, according to the electrical parameters of each electronic device of the bioelectric signal monitoring device acquired in the target working mode, the bioelectric signal change is determined, and the electrical parameters and the bioelectric signal change can be mapped and determined through the preset association relationship, and then the bioelectric signal can be differentiated according to the electrical parameters, and the abnormal bioelectric signal changes can be distinguished, which provides the possibility for the early diagnosis of the disease; further, according to the bioelectric signal changes, the probability of the disease onset is determined, and timely prediction is achieved before the disease onset, reminding medical staff to take intervention measures, thereby reducing the risk of disease onset. In addition, according to the changes in the bioelectric signal, a spatiotemporal change map can be drawn, which helps to determine the starting position and starting time of the disease onset so that doctors can take targeted treatment measures.

[0046] It should be noted that the execution subject of this embodiment may be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, etc. The following takes the terminal as the execution subject as an example to illustrate this embodiment and the following embodiments.

[0047] Based on this, the present application embodiment provides a bioelectric signal processing method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the bioelectric signal processing method of the present application.

[0048] In this embodiment, the bioelectric signal processing method includes steps S10 to S40:

[0049] Step S10, setting a target working mode of the bioelectric signal monitoring device, wherein the bioelectric signal monitoring device includes a plurality of electronic devices, each of which is deployed in at least one body area of ​​the user;

[0050] It should be noted that electronic devices include single electronic components and combinations of multiple electronic components. The functions of electronic components are relatively simple. Common electronic components include: memristors, resistors, transistors, capacitors, synapses and inductors, etc. They interact through signals such as current and voltage and can be deployed anywhere in the body, including inside or outside the body. A combination of electronic components refers to a more complex circuit structure formed by connecting multiple single electronic components according to a certain circuit design, such as integrated circuits, amplifier circuits, filter circuits, etc., which can implement more sophisticated monitoring of bioelectric signals.

[0051] In addition, it should be noted that the bioelectric signal monitoring device is obtained by deploying multiple electronic devices on a biological body in a certain layout, and can be arranged inside or outside the biological body. Bioelectric signals refer to weak electrical signals generated inside a biological body (such as a human body), including brain wave signals, electrocardiogram signals, and electromyography signals, which can be collected, amplified, and processed by electronic devices to obtain information about the physiological activity state of the biological body.

[0052] For example, according to the characteristics of the patient's body area, the electronic devices are deployed in a suitable position and manner, and the electronic devices are connected through circuits to form a complete bioelectric signal monitoring device. Multiple bioelectric signal monitoring devices can be arranged in multiple areas of the patient's body.

[0053] Exemplarily, the electronic device is not limited to electronic components such as memristors, synapses, resistors, transistors, etc., nor is it limited to the combination of a first electronic component and a second electronic component. For example, in the monitoring of ECG signals, a single resistor and capacitor component and an amplifier circuit and a filter circuit composed of these components can be used to monitor changes in ECG signals. By using a combination, better performance can be provided, thereby accurately monitoring changes in bioelectric signals and improving the accuracy of disease onset prediction; by reasonably selecting and using single components and combinations, cost and performance can be balanced to achieve efficient and accurate bioelectric signal monitoring.

[0054] For example, before starting monitoring, the electronic components in the bioelectric signal monitoring device need to be calibrated and time-synchronized to ensure that each electronic component can accurately and consistently record the bioelectric signal to avoid data confusion or misleading.

[0055] For example, in order to ensure the accuracy of disease onset prediction, all electronic devices can be set to operate simultaneously to achieve real-time monitoring of all bioelectric signals, so as to avoid inaccurate predictions caused by untimely detection of some bioelectric signals.

[0056] It is understandable that the use of electronic devices to monitor bioelectric signals can replace conventional medical testing equipment and reduce medical costs; by setting the target working mode of the bioelectric signal monitoring device, it is ensured that the electronic devices cooperate with each other to complete the monitoring task together, thereby improving the accuracy of bioelectric signal monitoring.

[0057] Step S20, obtaining electrical parameters of each electronic component of the bioelectric signal monitoring device in the target working mode;

[0058] It should be noted that electrical parameters refer to physical quantities used to describe the working state of electronic devices, such as resistance, capacitance, voltage and current.

[0059] Exemplarily, a bioelectric signal monitoring device is used to monitor the user's electrocardiogram (ECG) signal. When the target working mode is simultaneous operation, all electronic devices in the monitoring device are started simultaneously to capture the ECG signal and record changes in electrical parameters of the electronic devices.

[0060] Step S30, determining the bioelectric signal change according to the electrical parameters;

[0061] In a feasible embodiment, based on the measurement and analysis of certain electrical parameters (such as voltage, current, resistance, etc.) of electronic devices, the change of electrical signals (such as electrocardiogram, electroencephalogram, electromyography, etc.) generated by the organism is determined. For example, the intensity change of the current bioelectric signal is determined by observing the probability that the current state and resistance state of the corresponding electronic device exceed the threshold within a preset time range.

[0062] Exemplarily, by analyzing the frequency response of the electronic device, a specific type of bioelectric signal, such as an electrocardiogram or an electroencephalogram signal, is identified; and then the amplitude change of the identified bioelectric signal is determined by the change in the resistance value of the resistor.

[0063] Exemplarily, suitable algorithms or models, such as support vector machines and neural networks, can be used to extract the changing characteristics of electrical parameters, including but not limited to time domain characteristics, frequency domain characteristics, etc., to further determine the changes in bioelectric signals, such as the periodic change frequency of bioelectric signals, changes in peak and valley values ​​of bioelectric signal intensity, etc.

[0064] It is understandable that through real-time monitoring of physiological activities in the body, abnormalities in the patient's bioelectric signals can be discovered in time, thereby making timely predictions before the onset of the disease and achieving early diagnosis.

[0065] Step S40, determining the probability of disease onset based on the change in the bioelectric signal.

[0066] Exemplarily, the possibility of the patient's illness is calculated by monitoring and analyzing the patient's bioelectric signals and applying a specific algorithm or model based on the patterns of their changes.

[0067] Exemplarily, when the probability of disease onset is detected to be greater than a preset threshold, warning information is output. For example, in monitoring heart disease patients, the risk of heart attack is assessed by analyzing changes in electrocardiogram signals. When the risk exceeds a preset threshold, warning information is output to remind medical staff or the patient's family to take timely action. With timely warning information, medical intervention can be carried out in advance to reduce the risk and severity of disease onset.

[0068] For example, since epileptic seizures are accompanied by abnormal synchronous discharges of neurons, the cerebral cortex is continuously excited in a regional area. In the pre-ictal period (i.e., when the epileptic seizure is about to begin) and the attack period, the EEG signal will show a gradually enhanced specific frequency waveform, such as spike waves, sharp waves, spike-slow waves, sharp-slow waves, multi-spike-slow waves, and high-frequency rhythm disorders. The EEG signals are monitored by deploying synaptic memristors in various areas of the cerebral cortex, and various types of synaptic memristors are set to operate simultaneously to obtain the resistance change of the electronic device, and according to the resistance change, the waveform change of the EEG signal is determined, and then the possibility of epileptic seizure is determined.

[0069] This embodiment provides a bioelectric signal processing method, which uses simple electronic devices to differentiate the bioelectric signals without the involvement of external circuits; at the same time, through real-time monitoring and analysis of bioelectric signals, the possibility of disease onset is predicted, thereby achieving timely prediction of disease onset, so that treatment measures can be taken in time to reduce the risk of disease onset.

[0070] In a feasible implementation manner, step S10 includes:

[0071] Step S11, determining the layout of each electronic device in the bioelectric signal monitoring device;

[0072] It should be noted that the layout method refers to the arrangement and connection method of electronic devices in the device, including but not limited to parameters such as the position, direction, and spacing of the devices.

[0073] Step S12, when the layout mode is array distribution, setting the target working mode to a simultaneous operation mode;

[0074] It should be noted that the simultaneous operation mode refers to operating the electronic devices in the bioelectric signal monitoring devices simultaneously under the same time conditions.

[0075] For example, synchronization signals can be used to ensure that all electronic devices operate simultaneously; distributed clock synchronization, master-slave control and other methods can also be used to achieve simultaneous operation of electronic devices.

[0076] For example, in cardiac monitoring, array-distributed electrodes are used to simultaneously collect electrocardiogram signals from multiple locations to obtain more comprehensive cardiac activity information.

[0077] It is understandable that by collecting data at multiple points simultaneously, the source and characteristics of bioelectric signals can be determined more accurately and comprehensively.

[0078] Step S13, when the layout mode is a scattered distribution, setting the target working mode to an asynchronous operation mode.

[0079] It should be noted that the asynchronous operation mode refers to asynchronously operating various electronic devices according to preset rules.

[0080] Exemplarily, for the monitoring of electromyographic signals, the electronic devices are distributed in a scattered manner. Considering that the spatial distribution of muscle activity on the body is uneven, the electronic devices distributed in specific areas can be activated according to the dynamic changes of muscle activity, thereby reducing the amount of data and improving the efficiency of monitoring.

[0081] It can be understood that by precisely controlling the operation of each electronic device, ineffective monitoring can be reduced.

[0082] In this implementation, different electronic device operation modes are adopted according to the layout mode, which can improve the performance of the bioelectric signal monitoring device and meet the needs of different application scenarios.

[0083] In a feasible implementation, step S11 includes:

[0084] Step S01, determining the layout of the electronic components in the bioelectric signal monitoring device according to the type of the bioelectric signal to be monitored.

[0085] For example, considering that the changes of ECG signals and EEG signals are subtle and need to cover multiple subtle areas of the heart or brain, the electronic devices in the relevant monitoring devices are arranged in an array to obtain comprehensive bioelectric signal information. For example, please refer to Figure 2 , Figure 2 A schematic diagram of an array distribution of electronic devices is provided. Figure 2 The central outline represents the human body. The array can be distributed in any area of ​​the human body. Figure 2 The array in is composed of horizontal lines (A1, A2, A3, ..., A m ) and vertical lines (B1, B2, B3, ..., B m ) to form multiple grid areas, each of which can be used to place simple electronic devices; and for electromyographic signals, they can be arranged in a scattered manner according to the position and shape of the muscles to obtain more accurate electromyographic signal information.

[0086] In this embodiment, by optimizing the layout of electronic devices for specific signal categories, noise interference can be reduced and the quality of monitored bioelectric signals can be improved, thereby improving the accuracy of prediction of bioelectric signal changes and disease onset.

[0087] In a feasible implementation manner, after step S11, the method further includes:

[0088] Step S02, when the layout mode is array distribution, setting different array dimensions, wherein the array dimension is the dimension of the arrangement array of the electronic components of the bioelectric signal monitoring device.

[0089] It should be noted that the array dimension refers to the dimension of the orderly arrangement of electronic devices in the bioelectric signal monitoring device, that is, the size of the array in different directions. For example, different numbers of rows and columns are set for a two-dimensional array.

[0090] For example, when using a two-dimensional array to monitor changes in EEG signals in the human cerebral cortex, the corresponding array dimensions can be set to 4 and 7, respectively. Compared with square or cube arrays, arrays of different dimensions can provide more current path options, thereby reducing the number or length of external circuit connections required when connecting external circuits, thereby reducing potential failure points and improving the reliability and stability of bioelectric signal monitoring devices; at the same time, arrays of different dimensions can reduce the crossing and interference of wires through more flexible wiring methods.

[0091] In this embodiment, by constructing an irregularly arranged array, it is more convenient to construct an external circuit for adjustment, which saves costs and improves the accuracy of bioelectric signal monitoring through the external circuit.

[0092] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. On this basis, step S30 includes step A31:

[0093] Step A31, determining a bioelectric signal change according to the electrical parameter and a correlation relationship between the preset electrical parameter and the bioelectric signal;

[0094] It should be noted that the preset association relationship refers to a predetermined correspondence rule between electrical parameters and changes in bioelectric signals, which is used to infer changes in bioelectric signals from electrical parameters.

[0095] In a feasible embodiment, since cell activities and tissue functional activities in a living body can cause changes in electric potential, resulting in changes in electrical parameters of electronic devices deployed in the body area, for example, changes in electric potential can cause changes in the response voltage or current of capacitors or transistors, etc. Therefore, the correlation between preset voltage and current signals and changes in ECG signals can be used to further determine changes in ECG signals based on changes in voltage and current signals.

[0096] Exemplarily, deep learning technology can also be introduced to automatically learn and optimize the association relationship to improve the accuracy of electrical parameter analysis, thereby improving the accuracy of prediction of the possibility of disease occurrence. For example, in ECG signal analysis, the mapping relationship between the characteristic waveforms such as the P wave and QRS of the ECG signal and the voltage signal can be learned through a deep learning model, and the weight of the model can be adjusted based on the newly added data and the back propagation algorithm to optimize the mapping relationship, thereby achieving accurate identification of changes in bioelectric signals, and also personalized monitoring of different patients.

[0097] In this embodiment, the change of the bioelectric signal can be accurately determined through correlation analysis, thereby improving the accuracy of medical monitoring and achieving timely monitoring of disease onset.

[0098] In a feasible implementation manner, before step A301, the method further includes:

[0099] Step A301, performing electrical characteristic test on each electronic device according to the acquired analog signal, wherein the analog signal is determined based on the bioelectric signal of the disease to be predicted;

[0100] It should be noted that the simulation signal mainly includes the simulation of bioelectric signals of different stages and types of diseases. According to the characteristics of the bioelectric signals of the disease to be predicted, the corresponding simulation signal can be generated. The simulation signal has the frequency, amplitude and waveform corresponding to the bioelectric signals in the organism to simulate the changes of bioelectric signals in the organism. The electrical characteristic test is used to measure the changes in the electrical parameters of electronic devices when the bioelectric signals change, so that the intrinsic connection and law between the changes in bioelectric signals and the changes in electrical parameters can be obtained by comparing and analyzing the data.

[0101] Exemplarily, the materials that can be used in electronic devices include inorganic materials, organic materials, metal materials, biological materials, etc. Electronic devices constructed of different materials can be used in different areas of the body to improve the accuracy of bioelectric signal monitoring. For example, inorganic and organic insulating materials can be combined, such as combining silicon dioxide, polylactic acid, polyvinyl alcohol, etc.; a flexible organic insulating substrate can be used in parts of the body with small curvature, and an inorganic insulating layer can be used in parts with large curvature. For example, for synaptic memristors, the above-mentioned inorganic or organic insulating materials can be used to optimize and improve the switching function layer, such as polyvinyl alcohol doped with gold nanoparticles, polyvinyl alcohol doped with sodium acetate, silicon dioxide doped with aluminum, etc.

[0102] Exemplarily, the electronic device may be an electronic component, or a combination of multiple electronic components, such as a combination of a synapse and a transistor, a combination of a resistor and a transistor, a combination of a capacitor and a resistor, etc.

[0103] Exemplarily, a similar analog signal is generated based on the bioelectric signal of the disease to be predicted, and the electrical characteristics of the electronic device are tested based on the analog signal, that is, the analog signal is input into the electronic device to observe the changes in the electrical parameters of the electronic device, including changes in current, resistance, capacitance, and capacitance release relaxation time, etc., to evaluate whether the changes in electrical parameters are significant enough to reflect the changes in the corresponding bioelectric signal. In the case where the changes in electrical parameters are not significant, it may be considered to replace the building materials of the electronic device, or to use the assembled electronic device to re-test the electrical characteristics.

[0104] Step A302: extract the electrical parameter changes of each electronic device and determine the correlation between the electrical parameter changes and the bioelectrical signal.

[0105] Exemplarily, the changes of key electrical parameters during and after the bioelectric signal test at different stages are extracted, and the correlation between the changes of electrical parameters and the changes of bioelectric signals is established by analyzing the data, so as to realize the recognition of bioelectric signals by electronic devices, so that in the subsequent monitoring process, the established correlation can be used to infer the changes of bioelectric signals in the body according to the changes of electrical parameters of electronic devices. Specifically, the following table content can be referred to:

[0106]

[0107]

[0108] The period can be set according to the periodic characteristics of the disease attack. From the above table, it can be seen that the probability of breaking through the threshold in the 15 sets of data between attacks is 1 / 15.

[0109]

[0110] It can be seen from the above table that the probability of breaking through the threshold in the 15 sets of data in the early stage of the attack is 9 / 15.

[0111]

[0112] It can be seen from the above table that the probability of breaking through the threshold in the 15 sets of data during the attack period is 1.

[0113] It is not difficult to find that the changes in bioelectric signals at different periods can correspond to different changes in electrical parameters. Therefore, the bioelectric signals at different stages can be differentiated and identified based on the electrical parameters.

[0114] For example, electronic components made of different materials or combinations of electronic components can be tested for their electrical properties, the changes in electrical parameters corresponding to each material or each combination can be extracted, the magnitude of the changes for different types of bioelectric signals can be compared, and the electronic devices with more obvious changes can be selected for practical applications.

[0115] In this embodiment, the electrical characteristics test enables the electronic device to differentially identify bioelectric signals of different stages and types.

[0116] In a feasible implementation manner, before step S30, the method further includes:

[0117] Step A301, drawing a spatiotemporal variation map according to the bioelectric signal changes;

[0118] It should be noted that the spatiotemporal variation map shows the dynamic data of bioelectric signals changing with time and space, that is, the changes of bioelectric signals at different times and in different parts of the body.

[0119] Exemplarily, the monitored bioelectric signal data is recorded, and the time when the corresponding data is monitored and the location information of the bioelectric signal monitoring device are recorded; the collected bioelectric signal data is preprocessed, including denoising, filtering, etc., and key features such as the peak and valley values ​​of the signal are extracted from the preprocessed data; the extracted features and spatiotemporal information are then used to construct a spatiotemporal change map.

[0120] It is understandable that by analyzing the spatiotemporal changes in bioelectric signals, signs of disease onset can be detected in advance so that preventive measures can be taken.

[0121] Step A302, determining the initial location of the disease onset based on the spatiotemporal variation map.

[0122] It should be noted that the initial location of the disease onset refers to the area or time point where the abnormality or change first occurs in the spatiotemporal change map.

[0123] In this embodiment, the spatiotemporal variation map can be used to more intuitively observe and analyze the patient's bioelectric signal changes, thereby improving the accuracy of disease diagnosis; after determining the initial location of the disease onset, intervention and treatment can be carried out on the lesion to optimize the treatment plan.

[0124] For example, to help understand the implementation process of the bioelectric signal prediction method obtained by combining this embodiment with the above-mentioned embodiment 1, please refer to Figure 3 , Figure 3 A brief flowchart of a bioelectric signal prediction method is provided, specifically:

[0125] Step B10, using the analog signal of the bioelectric signal, conduct an electrical characteristic test on the electronic device to determine the materials and combination of the electronic devices used to monitor various types of bioelectric signals (maintaining a single component or forming a combination of multiple components), and at the same time, clarify the corresponding relationship between the changes in the electrical parameters of each electronic device and the changes in the bioelectric signal.

[0126] Step B20, determine the layout of the electronic devices in the bioelectric signal monitoring device. Due to the different types of bioelectric signals to be monitored, the deployment method (layout method) of the electronic devices may be different. For scenes that require continuous and synchronous monitoring, such as ECG signal monitoring, an array layout can be used; for scenes where the signal changes little or the differences between regions are large, such as EMG signal monitoring, a scattered point layout can be used.

[0127] Step B30, setting the target working mode of the bioelectric signal monitoring device. Due to the different layouts of the electronic devices, the working relationships between the electronic devices may also need to be set differently to adapt to different scenarios and needs. When the layout is array distribution, the electronic devices are operated synchronously; when the layout is scattered distribution, the electronic devices are operated asynchronously. By selecting a suitable layout, each bioelectric signal can be accurately monitored as much as possible, thereby achieving accurate prediction of the onset of the disease.

[0128] Step B40, monitoring the changes in bioelectric signals. Through the above-mentioned electrical characteristic test, the electrical parameters of the electronic device achieve differentiated characterization of the bioelectric signals. Therefore, the changes in the bioelectric signals can be determined based on the changes in the electrical parameters of the electronic device.

[0129] Step B50, determine the probability of disease onset, based on the historical statistics of the changing patterns of bioelectric signals during the inter-attack period, pre-attack period and attack period of the disease, and based on the currently monitored changes in bioelectric signals, determine the probability of disease onset, and achieve timely prediction of disease onset.

[0130] Step B60, when the probability of disease onset exceeds a threshold, a warning message is issued to remind medical staff to take intervention measures.

[0131] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the bioelectric signal processing method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0132] The present application also provides a bioelectric signal processing device, please refer to Figure 4 , the bioelectric signal processing device comprises:

[0133] A mode setting module 10, for setting a target working mode of the bioelectric signal monitoring device, wherein the bioelectric signal monitoring device includes a plurality of electronic devices, each of which is deployed in at least one body region of the user;

[0134] The parameter acquisition module 20 is used to acquire the electrical parameters of each electronic device of the bioelectric signal monitoring device in the target working mode;

[0135] A change determination module 30, for determining a change in a bioelectric signal according to electrical parameters;

[0136] The probability determination module 40 is used to determine the probability of disease onset according to the changes in the bioelectric signal.

[0137] The bioelectric signal processing device provided in the embodiment of the present application adopts the bioelectric signal processing method in the above embodiment, which can reduce the risk of disease onset. Compared with the prior art, the beneficial effects of the bioelectric signal processing device provided in the present application are the same as the beneficial effects of the bioelectric signal processing method provided in the above embodiment, and the other technical features in the bioelectric signal processing device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0138] An embodiment of the present application provides a bioelectric signal processing device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the bioelectric signal processing method in the above-mentioned embodiment one.

[0139] Reference below Figure 5 , which shows a schematic diagram of the structure of a bioelectric signal processing device suitable for implementing the embodiment of the present application. The bioelectric signal processing device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The bioelectric signal processing device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0140] like Figure 5As shown, the bioelectric signal processing device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the bioelectric signal processing device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the bioelectric signal processing device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a bioelectric signal processing device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or provided instead.

[0141] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0142] The bioelectric signal processing device provided in the embodiment of the present application adopts the bioelectric signal processing method in the above embodiment to reduce the risk of disease onset. Compared with the prior art, the beneficial effects of the bioelectric signal processing device provided in the present application are the same as the beneficial effects of the bioelectric signal processing method provided in the above embodiment, and the other technical features in the bioelectric signal processing device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0143] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0144] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0145] An embodiment of the present application provides a computer-readable storage medium having computer-readable program instructions (ie, a computer program) stored thereon, wherein the computer-readable program instructions are used to execute the bioelectric signal processing method in the above-mentioned embodiment.

[0146] The computer-readable storage medium provided in the embodiment of the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0147] The computer-readable storage medium may be included in the bioelectric signal processing device; or may exist independently without being assembled into the bioelectric signal processing device.

[0148] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the bioelectric signal processing device, the bioelectric signal processing device: sets the target working mode of the bioelectric signal monitoring device, wherein the bioelectric signal monitoring device includes multiple electronic devices, and each electronic device is deployed in at least one body area of ​​the user; obtains the electrical parameters of each electronic device of the bioelectric signal monitoring device under the target working mode; determines the bioelectric signal changes based on the electrical parameters; and determines the probability of disease onset based on the bioelectric signal changes.

[0149] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0150] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0151] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0152] The readable storage medium provided in the embodiment of the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned bioelectric signal processing method, and can reduce the risk of disease onset. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the present application are the same as the beneficial effects of the bioelectric signal processing method provided in the above-mentioned embodiment, and will not be repeated here.

[0153] An embodiment of the present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned bioelectric signal processing method when executed by a processor.

[0154] The computer program product provided in the embodiment of the present application can reduce the risk of disease onset. Compared with the prior art, the beneficial effects of the computer program product provided in the present application are the same as the beneficial effects of the bioelectric signal processing method provided in the above embodiment, which will not be repeated here.

[0155] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A bioelectric signal processing method, characterized in that: The bioelectric signal processing method comprises: Setting a target operating mode of a bioelectric signal monitoring device, wherein the bioelectric signal monitoring device comprises a plurality of electronic devices, each of which is deployed in at least one body region of a user; Acquiring electrical parameters of each electronic component of the bioelectric signal monitoring device under the target working mode; Determining bioelectric signal changes according to the electrical parameters; The probability of disease onset is determined based on the changes in the bioelectric signal.

2. The bioelectric signal processing method according to claim 1, characterized in that: The step of setting the target working mode of the bioelectric signal monitoring device comprises: Determining the layout of the electronic components in the bioelectric signal monitoring device; When the layout mode is array distribution, setting the target working mode to a simultaneous operation mode; When the layout mode is a scattered distribution, the target working mode is set to an asynchronous operation mode.

3. The bioelectric signal processing method according to claim 2, characterized in that: The step of determining the layout of the electronic components in the bioelectric signal monitoring device includes: The layout of the electronic components in the bioelectric signal monitoring device is determined according to the type of the bioelectric signal to be monitored.

4. The bioelectric signal processing method according to claim 2, characterized in that: After the step of determining the layout of the electronic components in the bioelectric signal monitoring device, the method further includes: In the case where the layout is array distribution, different array dimensions are set, wherein the array dimension is the dimension of the arrangement array of the electronic components of the bioelectric signal monitoring device.

5. The bioelectric signal processing method according to claim 1, characterized in that: The step of determining the change of the bioelectric signal according to the electrical parameter comprises: The bioelectric signal change is determined according to the association relationship between the electrical parameter and the preset electrical parameter and the bioelectric signal.

6. The bioelectric signal processing method according to claim 5, characterized in that: Before the step of determining the change of the bioelectric signal according to the electrical parameter and the preset correlation relationship, the method further includes: Performing an electrical characteristic test on each of the electronic devices according to the acquired analog signal, wherein the analog signal is determined based on the bioelectric signal of the disease to be predicted; The electrical parameter changes of each of the electronic devices are extracted, and the correlation between the electrical parameter changes and the bioelectrical signals is determined.

7. The bioelectric signal processing method according to claim 1, characterized in that: After the step of determining the bioelectric signal change, the method further includes: According to the changes of the bioelectric signal, drawing a spatiotemporal change map; The initial location of the disease onset is determined based on the spatiotemporal variation map.

8. A bioelectric signal processing device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the bioelectric signal processing method according to any one of claims 1 to 7.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the bioelectric signal processing method according to any one of claims 1 to 7 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the bioelectric signal processing method according to any one of claims 1 to 7 are implemented.

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