Biological electrical signal processing method, device, storage medium and program product

Abstract

The application discloses a biological electrical signal processing method and device, a storage medium and a program product, relates to the technical field of biological information, and the biological electrical signal processing method comprises the steps of setting a target working mode of a biological electrical signal monitoring device, wherein the biological electrical signal monitoring device comprises a plurality of electronic devices, and each electronic device is arranged on at least one body region of a user; acquiring electrical parameters of each electronic device of the biological electrical signal monitoring device in the target working mode; determining a biological electrical signal change according to the electrical parameters; and determining a probability of disease onset according to the biological electrical signal change. The application realizes real-time monitoring of the health condition of a patient by monitoring the biological electrical signal change, timely prediction before disease onset, and reduction of the risk of disease onset.

Description

Technical Field

[0001] This application relates to the field of bioinformatics, and in particular to bioelectric signal processing methods, devices, storage media, and program products. Background Technology

[0002] In recent years, real-time analysis and prediction of bioelectrical signals have shown great potential in medical diagnosis and treatment. Bioelectrical signals such as brainwave signals, electrocardiogram signals, and electromyogram signals can reflect a variety of physiological processes and disease states in the human body. By observing the dynamic changes of these signals, it is possible to help detect diseases early, predict the development trend of diseases, and even assist doctors in formulating treatment plans.

[0003] While the number of hardware studies on electrocardiogram (ECG) and electroencephalogram (EEG) signals is increasing, most research still focuses on signal analysis after the onset of disease, with relatively little attention paid to timely prediction before the onset of disease. Typical bioelectrical signal processing hardware systems are based on silicon-based complementary metal-oxide-semiconductor (CMOS) technology. These systems use large-scale integrated circuits in the digital computing unit to compress bioelectrical signals in the digital domain, or design complex arrays of devices to directly process analog information during a disease onset. However, this approach, based on processing existing signals, makes it difficult to predict future signals, limiting the possibility of early disease detection and intervention, and resulting in a higher risk of disease onset. Summary of the Invention

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

[0005] The target operating mode of the bioelectric signal 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;

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

[0007] Based on the electrical parameters, the changes in bioelectrical signals are determined;

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

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

[0010] Determine the layout of each electronic component in the bioelectric signal monitoring device;

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

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

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

[0014] The layout of each electronic component in the bioelectric signal monitoring device is determined based on the category of the bioelectric signal to be monitored.

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

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

[0017] In one embodiment, the step of determining the change in bioelectrical signal based on the electrical parameters includes:

[0018] Based on the correlation between the electrical parameters and preset electrical parameters and bioelectrical signals, the changes in bioelectrical signals are determined.

[0019] In one embodiment, prior to the step of determining the bioelectrical signal change based on the electrical parameters and a preset correlation, the method further includes:

[0020] Based on the acquired analog signals, the electrical characteristics of each electronic device are tested, wherein the analog signals are determined based on the bioelectrical signals of the disease to be predicted.

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

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

[0023] Based on the changes in the bioelectrical signals, a spatiotemporal variation map was drawn;

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

[0025] Furthermore, to achieve the above objectives, this application also proposes a bioelectric signal processing device, which includes:

[0026] The mode setting module is used to form a bioelectrical signal monitoring device through various electronic devices deployed in the body area;

[0027] The parameter acquisition module is used to coordinate the working relationship of each electronic component of the bioelectric signal monitoring device;

[0028] The change determination module is used to determine the changes in bioelectrical signals by measuring the changes in the electrical parameters of the coordinated bioelectrical signal monitoring device.

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

[0030] In addition, to achieve the above objectives, this application also proposes a bioelectric signal processing device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the bioelectric signal processing method as described above.

[0031] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the bioelectric signal processing method described above.

[0032] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the bioelectric signal processing method described above.

[0033] The one or more technical solutions proposed in this application have at least the following technical effects: First, a target working mode for the biosignal monitoring device is set, wherein the bioelectric signal monitoring device includes multiple electronic devices, each deployed in at least one body area of ​​the user, to comprehensively and continuously capture weak electrical signals within the organism. By setting the working mode, the consistency and accuracy of signal monitoring are ensured, realizing real-time monitoring of bioelectric signals within the organism. Furthermore, by acquiring the electrical parameters of each electronic device in the target working mode, reliable data is provided for subsequent analysis of changes in bioelectric signals. Next, based on the electrical parameters, changes in bioelectric signals are determined. Since changes in bioelectric signals within the body lead to changes in the electrical parameters of each electronic device, observing these parameters helps to promptly detect abnormal changes in bioelectric signals, increasing the likelihood of early disease detection. Finally, based on changes in bioelectric signals, the probability of disease onset is determined, enabling timely prediction before disease onset, buying valuable treatment time for patients, and reducing the risk of disease onset. Attached Figure Description

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

[0035] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 This is a flowchart illustrating an embodiment of the bioelectric signal processing method of this application.

[0037] Figure 2 This is a schematic diagram of the array distribution of electronic devices provided in Embodiment 1 of this application;

[0038] Figure 3 This is a simplified flowchart of the bioelectric signal processing method provided in Embodiment 2 of this application;

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

[0040] Figure 5 This is a schematic diagram of the device structure of the hardware operating environment involved in the bioelectric signal processing method in the embodiments of this application. Detailed Implementation

[0041] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0042] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0043] In this embodiment, for ease of description, the following description uses the terminal as the execution subject.

[0044] In conventional technologies, the analysis and prediction of bioelectrical signals primarily rely on advanced digital signal processing techniques and deep learning algorithms. By statistically analyzing large-scale bioelectrical signal datasets, characteristic waveforms associated with the occurrence or impending onset of diseases are extracted, providing strong support for early detection, diagnosis, and treatment planning. However, this method cannot operate independently without cloud computing, limiting its application in mobile scenarios. On the hardware side, the technical means of large-scale, complex integrated circuits make it difficult to predict future signals, and the system must rely on a stable external power supply, making the analysis and prediction system difficult to move and carry.

[0045] This application provides a solution that determines the layout of electronic devices in a bioelectric signal monitoring device based on the category of the bioelectric signal to be monitored. Arrayed electronic devices are configured to operate simultaneously for more accurate and comprehensive acquisition of bioelectric signals; scattered electronic devices are configured to operate asynchronously to reduce invalid monitoring in certain areas. Then, based on the acquired electrical parameters of each electronic device in the target operating mode, changes in the bioelectric signal are determined. The electrical parameters and changes in the bioelectric signal can be mapped using a preset correlation, enabling differentiated identification of bioelectric signals based on the electrical parameters, distinguishing abnormal changes, and providing the possibility for early disease diagnosis. Furthermore, the probability of disease onset can be determined based on changes in the bioelectric signal, enabling timely prediction before disease onset and alerting medical personnel to take intervention measures, thereby reducing the risk of disease onset. In addition, spatiotemporal variation maps can be drawn based on changes in the bioelectric signal, helping to determine the starting location and time of disease onset, so that doctors can take targeted treatment measures.

[0046] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the above functions. The following description uses a terminal as the executing entity to illustrate this embodiment and the subsequent embodiments.

[0047] Based on this, embodiments of this application provide a bioelectric signal processing method, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the bioelectric signal processing method of this application.

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

[0049] Step S10: Set the target working mode of the bioelectric signal monitoring device, wherein the bioelectric signal monitoring device includes multiple 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. Electronic components generally have a single function; common electronic components include memristors, resistors, transistors, capacitors, synapses, and inductors. They interact through signals such as current and voltage and can be deployed anywhere in the body, both internally and externally. Combinations of electronic components refer to more complex circuit structures formed by connecting multiple individual electronic components according to a specific circuit design, such as integrated circuits, amplifier circuits, and filter circuits. These structures enable more precise monitoring of bioelectrical signals.

[0051] Additionally, it should be noted that bioelectric signal monitoring devices are obtained by deploying multiple electronic devices on an organism in a certain layout. These devices can be placed inside or outside the organism. Bioelectric signals refer to weak electrical signals generated within an organism (such as the human body), including brainwave signals, electrocardiogram (ECG) signals, and electromyographic (EMG) signals. These signals can be collected, amplified, and processed by electronic devices to obtain information about the organism's physiological state.

[0052] For example, based on the characteristics of different areas of the patient's body, electronic devices are deployed in appropriate locations and methods, and these devices are connected via circuits to form a complete bioelectrical signal monitoring device. Multiple bioelectrical signal monitoring devices can be deployed in multiple areas of the patient's body.

[0053] For example, electronic devices are not limited to memristors, synapses, resistors, transistors, or other electronic components, nor are they limited to combinations of first and second electronic components. For instance, in monitoring electrocardiogram (ECG) signals, individual resistive and capacitive components, along with amplifier and filter circuits composed of these components, can be used to monitor changes in ECG signals. By using combinations, superior performance can be provided, thereby achieving accurate monitoring of changes in bioelectrical signals and improving the accuracy of disease onset prediction. By rationally selecting and using individual components and combinations, cost and performance can be balanced, achieving efficient and accurate bioelectrical signal monitoring.

[0054] For example, before monitoring begins, the electronic devices in the bioelectric signal monitoring device need to be calibrated and time-synchronized to ensure that each electronic device can accurately and consistently record bioelectric signals and 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 bioelectrical signals, so as to avoid inaccurate predictions caused by untimely detection of some bioelectrical signals.

[0056] It is understandable that using 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 various electronic devices cooperate with each other to complete the monitoring task, thereby improving the accuracy of bioelectric signal monitoring.

[0057] Step S20: Obtain the electrical parameters of each electronic component of the bioelectric signal monitoring device under the target working mode;

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

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

[0060] Step S30: Determine the changes in bioelectrical signals based on electrical parameters;

[0061] In one feasible embodiment, changes in electrical signals (such as electrocardiograms, electroencephalograms, and electromyograms) generated by a living organism are determined based on the measurement and analysis of certain electrical parameters (such as voltage, current, and resistance) of electronic devices. For example, the intensity change of the current bioelectrical signal is determined by observing the probability that the current state and resistance state of the corresponding electronic device exceed a threshold within a preset time range.

[0062] For example, by analyzing the frequency response of electronic devices, specific types of bioelectric signals, such as electrocardiogram (ECG) or electroencephalogram (EEG) signals, can be identified; and by measuring the change in the resistance of a resistor, the amplitude change of the identified bioelectric signals can be determined.

[0063] For example, suitable algorithms or models, such as support vector machines and neural networks, can be used to extract the variation characteristics of electrical parameters, including but not limited to time domain features and frequency domain features, in order to determine the variation of bioelectric signals, such as the periodic variation frequency of bioelectric signals, and the changes in the peak and trough values ​​of bioelectric signal intensity.

[0064] Understandably, by monitoring physiological activities in a living organism in real time, abnormal bioelectrical signals in patients can be detected in a timely manner, thus enabling early prediction and diagnosis before the onset of the disease.

[0065] Step S40: Determine the probability of disease onset based on changes in bioelectrical signals.

[0066] For example, by monitoring and analyzing the patient's bioelectrical signals, and based on their changing patterns, a specific algorithm or model can be used to calculate the patient's likelihood of developing the disease.

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

[0068] For example, because epileptic seizures are accompanied by abnormal synchronous discharge of neurons and sustained regional excitation of the cerebral cortex, the electroencephalogram (EEG) signals exhibit gradually increasing specific frequency waveforms, such as spikes, sharp waves, spike-and-slow-wave complexes, sharp-and-slow-wave complexes, polyspike-and-slow-wave complexes, and high-level arrhythmias, during the pre-seizure phase (i.e., before the seizure begins) and the ictal phase. By monitoring the EEG signals using synaptic memristors deployed in various regions of the cerebral cortex, and by setting various synaptic memristors to operate simultaneously, the changes in the resistance values ​​of the electronic devices are acquired. Based on these resistance changes, the waveform changes of the EEG signals are determined, thereby determining the likelihood of an epileptic seizure.

[0069] This embodiment provides a bioelectric signal processing method that uses simple electronic devices to differentiate bioelectric signals without the need for external circuits. Simultaneously, by real-time monitoring and analysis of bioelectric signals, the likelihood of disease onset can be predicted, enabling timely prediction of disease onset so that timely treatment measures can be taken to reduce the risk of disease onset.

[0070] In one feasible implementation, step S10 includes:

[0071] Step S11: Determine the layout of each electronic component in the bioelectric signal monitoring device;

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

[0073] Step S12: When the layout is an array distribution, set the target working mode to simultaneous operation mode.

[0074] It should be noted that the simultaneous operation mode refers to the simultaneous operation of various electronic components in various bioelectric signal monitoring devices under the condition of consistent timing.

[0075] For example, all electronic devices can be ensured to operate simultaneously through synchronization signals; or the simultaneous operation of electronic devices can be achieved through distributed clock synchronization, master-slave control, or other methods.

[0076] For example, in cardiac monitoring, an array of electrodes is used to simultaneously acquire electrocardiogram signals from multiple locations to obtain more comprehensive information about cardiac activity.

[0077] Understandably, simultaneous acquisition from multiple points allows for a more accurate and comprehensive determination of the source and characteristics of bioelectrical signals.

[0078] Step S13: When the layout is a scattered distribution, set the target working mode to asynchronous operation mode.

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

[0080] For example, in 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] Understandably, precise control of the operation of each electronic component can reduce ineffective monitoring.

[0082] In this embodiment, different operating modes of electronic devices can be adopted according to the layout, which can improve the performance of the bioelectric signal monitoring device and meet the needs of different application scenarios.

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

[0084] Step S01: Determine the layout of each electronic device in the bioelectric signal monitoring device according to the type of bioelectric signal to be monitored.

[0085] For example, considering the subtle changes in electrocardiogram (ECG) and electroencephalogram (EEG) signals, and the need to cover multiple minute areas of the heart or brain, the electronic components in the relevant monitoring device are arranged in an array to acquire comprehensive bioelectrical signal information. For example, please refer to... Figure 2 , Figure 2 A schematic diagram of the array distribution of electronic devices is provided. Figure 2 The central outline represents the human body, and this array can be distributed across any area of ​​the human body. Figure 2 The array in the image consists of horizontal lines (A1, A2, A3, ..., A...). m ) and vertical lines (B1, B2, B3, ..., B m The area is divided into multiple grid regions, each of which can hold simple electronic devices; for electromyographic signals, they can be distributed in a scattered manner according to the location and shape of the muscle 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 enhancing the accuracy of predicting changes in bioelectric signals and disease onset.

[0087] In one possible implementation, after step S11, the method further includes:

[0088] Step S02: In the case of an array distribution layout, different array dimensions are set, where the array dimension is the dimension of the array of electronic devices of the bioelectric signal monitoring device.

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

[0090] For example, when using a two-dimensional array to monitor changes in electroencephalogram (EEG) signals in the human cerebral cortex, the corresponding array dimensions can be set to 4 and 7, respectively. Compared to square or cubic arrays, using arrays of different dimensions provides more current path options, thereby reducing the number or length of external circuit connections required when connecting external circuits, thus reducing potential failure points and improving the reliability and stability of the bioelectrical signal monitoring device. At the same time, arrays of different dimensions can reduce wire crossings and interference through more flexible wiring methods.

[0091] In this embodiment, by constructing an irregular array, it is easier to build and adjust the external circuit. While saving costs, the accuracy of bioelectric signal monitoring is improved by using the external circuit.

[0092] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description and will not be repeated hereafter. Based on this, step S30 includes step A31:

[0093] Step A31: Determine the changes in bioelectrical signals based on the electrical parameters and the correlation between preset electrical parameters and bioelectrical signals;

[0094] It should be noted that the preset correlation refers to the pre-determined correspondence rules between electrical parameters and changes in bioelectrical signals, which are used to infer changes in bioelectrical signals from electrical parameters.

[0095] In one feasible embodiment, since cell activity and tissue function in a living organism can cause changes in electrical potential, which in turn cause changes in the electrical parameters of electronic devices deployed on the body, for example, changes in electrical potential can cause changes in the response voltage or current of capacitors or transistors, the changes in the electrocardiogram (ECG) signal can be further determined based on the changes in the voltage and current signals by utilizing the pre-defined correlation between voltage and current signals and ECG signal changes.

[0096] For example, deep learning technology can also be introduced to automatically learn and optimize correlations, thereby improving the accuracy of electrical parameter analysis and thus enhancing the accuracy of predicting the likelihood of disease occurrence. For instance, in electrocardiogram (ECG) signal analysis, a deep learning model can learn the mapping relationship between characteristic waveforms such as the P wave and QRS complex of ECG signals and voltage signals. Based on newly added data and a backpropagation algorithm, the model weights can be adjusted to optimize this mapping relationship, thereby achieving accurate identification of changes in bioelectrical signals and enabling personalized monitoring of different patients.

[0097] In this embodiment, by analyzing the correlation, the changes in bioelectrical signals can be accurately determined, thereby improving the accuracy of medical monitoring and enabling timely monitoring of disease onset.

[0098] In one possible implementation, prior to step A301, the method further includes:

[0099] Step A301: Based on the acquired analog signals, perform electrical characteristic tests on each electronic device, wherein the analog signals are determined based on the bioelectrical signals of the disease to be predicted.

[0100] It should be noted that the simulated signals mainly include simulations of bioelectrical signals at different stages and for different types of diseases. Based on the bioelectrical signal characteristics of the disease to be predicted, corresponding simulated signals can be generated. These simulated signals possess frequencies, amplitudes, and waveforms corresponding to those of bioelectrical signals within the organism, thus simulating changes in bioelectrical signals within the organism. Electrical characteristic testing is used to examine the changes in the electrical parameters of electronic devices under conditions of bioelectrical signal variations. By comparing and analyzing the data, the intrinsic relationship and patterns between changes in bioelectrical signals and changes in electrical parameters can be obtained.

[0101] For example, electronic devices can utilize materials including inorganic, organic, metallic, and biomaterials. Different materials can be used to construct electronic devices in different areas of the body, thereby improving the accuracy of bioelectrical signal monitoring. For instance, inorganic and organic insulating materials can be combined, such as silica, polylactic acid, and polyvinyl alcohol. Flexible organic insulating substrates can be used in areas of low curvature within the body, while inorganic insulating layers can be used in areas of high curvature. For example, for synaptic memristors, the aforementioned inorganic or organic insulating materials can be optimized and modified to construct switching functional layers, such as polyvinyl alcohol-doped gold nanoparticles, polyvinyl alcohol-doped sodium acetate, and silica-doped aluminum.

[0102] For example, an electronic device can be an electronic component or a combination of multiple electronic components, such as a combination of a neural synapse and a transistor, a combination of a resistor and a transistor, a combination of a capacitor and a resistor, etc.

[0103] For example, based on the bioelectrical signals of the disease to be predicted, a similar simulated signal is generated. This simulated signal is then used to test the electrical characteristics of an electronic device. Specifically, the simulated signal is input into the electronic device, and changes in its electrical parameters are observed, including changes in current magnitude, resistance, capacitance, and capacitance relaxation time. The extent to which these changes reflect the corresponding changes in the bioelectrical signal is assessed. If the changes in electrical parameters are not significant, it may be necessary to consider replacing the building materials of the electronic device or using a composite electronic device to retest its electrical characteristics.

[0104] Step A302: Extract the changes in electrical parameters of each electronic device and determine the correlation between the changes in electrical parameters and bioelectrical signals.

[0105] For example, changes in key electrical parameters during and after bioelectrical signal testing at different stages are extracted. By analyzing the data, a correlation is established between changes in electrical parameters and changes in bioelectrical signals. This enables the identification of bioelectrical signals using electronic devices, allowing for the inference of changes in bioelectrical signals within the organism based on changes in the electrical parameters of the electronic devices during subsequent monitoring. Specifically, please refer to the following table:

[0106]

[0107]

[0108] The cycle can be set according to the cyclical characteristics of the disease. As can be seen from the table above, the probability of breaking the threshold in the 15 sets of data during the interictal period is 1 / 15.

[0109]

[0110] As can be seen from the table above, the probability of breaking the threshold in the 15 sets of data in the pre-seizure stage is 9 / 15.

[0111]

[0112] As can be seen from the table above, the probability of breaking the threshold in the 15 data sets during the seizure period is 1.

[0113] It is not difficult to find that changes in bioelectrical signals at different times can correspond to changes in different electrical parameters. Therefore, bioelectrical signals at different stages can be differentiated based on electrical parameters.

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

[0115] In this embodiment, by testing electrical characteristics, electronic devices can differentiate and identify bioelectrical signals of different stages and types.

[0116] In one possible implementation, prior to step S30, the method further includes:

[0117] Step A301: Draw a spatiotemporal variation map based on the changes in bioelectrical signals;

[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 body parts.

[0119] For example, the monitored bioelectric signal data is recorded, along with the time of the monitored data and the location information of the bioelectric signal monitoring device. The collected bioelectric signal data is preprocessed, including noise reduction and filtering. Key features, such as signal peaks and valleys, are extracted from the preprocessed data. Then, the extracted features and spatiotemporal information are used to construct a spatiotemporal variation map.

[0120] Understandably, by analyzing the spatiotemporal changes in bioelectrical signals, signs of disease onset can be detected in advance, allowing for preventative measures to be taken.

[0121] Step A302: Determine the initial location of the disease onset based on the spatiotemporal change map.

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

[0123] In this embodiment, the spatiotemporal variation map allows for a more intuitive observation and analysis of changes in the patient's bioelectrical signals, thereby improving the accuracy of disease diagnosis. After determining the initial location of the disease onset, intervention and treatment can be targeted at the lesion, optimizing 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 embodiment one, please refer to... Figure 3 , Figure 3 A simplified flowchart of a bioelectrical signal prediction method is provided, specifically:

[0125] Step B10 involves using simulated bioelectric signals to test the electrical characteristics of electronic devices, determining the materials and combination methods of the electronic devices used to monitor various bioelectric signals (keeping a single element or forming a combination of multiple elements), and clarifying the correspondence between changes in the electrical parameters of each electronic device and changes in bioelectric signals.

[0126] Step B20: Determine the layout of the electronic devices in the bioelectric signal monitoring device. Since the types of bioelectric signals to be monitored differ, the deployment (layout) of the electronic devices may vary. For scenarios requiring continuous and synchronous monitoring, such as ECG signal monitoring, an array layout can be used; for scenarios where signal changes are small or differences between regions are large, such as electromyography signal monitoring, a scattered layout can be used.

[0127] Step B30 involves setting the target operating mode of the bioelectric signal monitoring device. Due to different layouts of the electronic components, the working relationships between them may also need to be configured differently to adapt to different scenarios and requirements. In the case of an array-distributed layout, the electronic components operate synchronously; in the case of a scattered layout, the electronic components operate asynchronously. By selecting an appropriate layout, the device aims to accurately monitor each type of bioelectric signal, thereby achieving accurate prediction of disease onset.

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

[0129] Step B50: Determine the probability of disease onset. Based on the historical statistical patterns of changes in bioelectrical signals during the inter-onset, pre-onset, and onset periods, and based on the currently monitored changes in bioelectrical signals, determine the probability of disease onset to achieve timely prediction of disease onset.

[0130] Step B60: When the probability of disease onset exceeds the threshold, issue an early warning message and remind medical staff to take intervention measures.

[0131] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the bioelectric signal processing method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0132] This application also provides a bioelectric signal processing device; please refer to... Figure 4 The bioelectric signal processing device includes:

[0133] The mode setting module 10 is used to set the target working mode of the bioelectric signal monitoring device, wherein the bioelectric signal monitoring device includes multiple electronic devices, each of which is deployed in at least one body area of ​​the user.

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

[0135] The change determination module 30 is used to determine the changes in bioelectrical signals based on electrical parameters;

[0136] The probability determination module 40 is used to determine the probability of disease onset based on changes in bioelectrical signals.

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

[0138] This 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 to enable the at least one processor to perform the bioelectric signal processing method in the above embodiment 1.

[0139] The following is for reference. Figure 5 The diagram illustrates a structural schematic of a bioelectric signal processing device suitable for implementing embodiments of this application. The bioelectric signal processing device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5 The bioelectric signal processing device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0140] like Figure 5As shown, the bioelectric signal processing device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.) that can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the bioelectric signal processing device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the bioelectric signal processing device to communicate wirelessly or wiredly with other devices 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 possess all of the systems shown. More or fewer systems may be implemented alternatively.

[0141] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

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

[0143] It should be understood that the various parts disclosed in this application can be implemented using 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 suitable manner in one or more embodiments or examples.

[0144] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0145] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, which are used to execute the bioelectric signal processing method in the above embodiments.

[0146] The computer-readable storage medium provided in this application embodiment 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 thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. 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 conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0147] The aforementioned computer-readable storage medium may be included in the bioelectric signal processing device; or it may exist independently and not assembled into the bioelectric signal processing device.

[0148] The aforementioned computer-readable storage medium carries one or more programs that, when executed by the bioelectric signal processing device, cause the bioelectric signal processing device to: set a target operating mode for the bioelectric signal monitoring device, wherein the bioelectric signal monitoring device includes multiple electronic devices, each electronic device being deployed in at least one body region of the user; acquire electrical parameters of each electronic device of the bioelectric signal monitoring device under the target operating mode; determine changes in bioelectric signals based on the electrical parameters; and determine the probability of disease onset based on the changes in bioelectric signals.

[0149] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0150] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0151] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

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

[0153] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the bioelectric signal processing method described above.

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

[0155] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A bioelectric signal processing method, characterized in that, The bioelectric signal processing method includes: Based on the category of the bioelectric signal to be monitored, the layout of each electronic device in the bioelectric signal monitoring device is determined. The bioelectric signal monitoring device includes multiple electronic devices, each of which is deployed in at least one body area of ​​the user. In the case where the layout is an array distribution, the target operating mode of the bioelectric signal monitoring device is set to simultaneous operation mode; When the layout is a scattered distribution, the target working mode is set to asynchronous operation mode; Acquire the electrical parameters of each electronic component of the bioelectric signal monitoring device under the target operating mode; The changes in bioelectric signals are determined based on the correlation between the electrical parameters and preset electrical parameters and bioelectric signals. The correlation is determined based on the changes in the electrical parameters of each electronic device after the electrical characteristics of each electronic device are tested using the obtained analog signals. The analog signals are determined based on the bioelectric signals of the disease to be predicted. The probability of disease onset is determined based on the changes in the bioelectrical signals.

2. The bioelectric signal processing method as described in claim 1, characterized in that, Following the step of determining the layout of each electronic component in the bioelectric signal monitoring device, the method further includes: In the case where the layout is an array distribution, different array dimensions are set, wherein the array dimension is the dimension of the arrangement array of each electronic device of the bioelectric signal monitoring device.

3. The bioelectric signal processing method as described in claim 1, characterized in that, Following the step of determining the changes in bioelectrical signals, the method further includes: Based on the changes in the bioelectrical signals, a spatiotemporal variation map was drawn; Based on the spatiotemporal variation map, the initial location of the disease onset is determined.

4. A bioelectric signal processing device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the bioelectric signal processing method as described in any one of claims 1 to 3.

5. 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, it implements the steps of the bioelectric signal processing method as described in any one of claims 1 to 3.

6. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the bioelectric signal processing method as described in any one of claims 1 to 3.

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