Electroencephalogram signal-based awakening method and apparatus for coma patient, electronic device, and storage medium

Abstract

An electroencephalogram signal-based awakening method and apparatus for a coma patient, an electronic device, and a storage medium. The method comprises: acquiring an electroencephalogram signal of a coma patient, wherein the electroencephalogram signal comprises the frequency, amplitude, rhythm, and the like of brain waves (S100); acquiring corresponding target feature data on the basis of the electroencephalogram signal, calling from a database a comparison data set corresponding to the target feature data, and determining the current coma degree of the coma patient on the basis of the comparison data set and the target feature data (S110); and on the basis of the current coma degree, adjusting a stimulation parameter used for stimulating the coma patient, and stimulating the trigeminal nerve of the coma patient on the basis of the stimulation parameter (S120).

Description

Method, device, electronic device and storage medium for promoting awakening of comatose patients based on electroencephalogram signals Technical Field

[0001] The present application relates to the field of neuromodulation. Specifically, the present application relates to a method, device, electronic device and storage medium for awakening a comatose patient based on electroencephalogram (EEG) signals. Background Art

[0002] Current technologies used to assess comatose patients, such as neurological monitoring, electroencephalography, and functional magnetic resonance imaging, require time to collect and interpret data, and cannot provide real-time feedback, which is crucial in the treatment of comatose patients.

[0003] Lack of real-time monitoring may result in an inability to respond promptly to rapid changes in a patient's condition. Clinical treatments for comatose patients include medication, acupuncture, hyperbaric oxygen therapy, and neuromodulation techniques. However, medications may be limited in effectiveness and may be associated with side effects depending on the cause of coma. There is insufficient scientific evidence to support the effectiveness of acupuncture in comatose patients. Furthermore, individual variability can be significant, and treatment efficacy can be inconsistent. The safety of acupuncture also requires careful consideration, particularly in the treatment of comatose patients, as there is a potential risk of infection. Hyperbaric oxygen therapy requires specialized equipment and a specific environment, and high oxygen concentrations may pose a risk of oxygen toxicity. Invasive neuromodulation techniques, including deep brain stimulation and spinal cord stimulation, are highly invasive and may be associated with surgical and infection risks. The long-term efficacy and safety of these treatments require further study.

[0004] Although existing technologies have played a certain role in the assessment and treatment of comatose patients, there is a lack of real-time monitoring and timely feedback to treatment. As can be seen from the above, how to improve the real-time monitoring of comatose patients during the awakening process remains to be solved. Summary of the Invention

[0005] This application provides a method, device, electronic device, and storage medium for awakening a comatose patient based on EEG signals, which can solve the problem of low real-time monitoring of comatose patients during awakening in related technologies. The technical solution is as follows:

[0006] According to one aspect of the present application, a method for promoting awakening of a comatose patient based on EEG signals comprises:

[0007] Obtaining EEG signals from comatose patients, where the EEG signals include the frequency, amplitude, and rhythm of brain waves;

[0008] Acquire corresponding target feature data based on the EEG signal, retrieve a comparison data group corresponding to the target feature data in a database, and determine the current coma degree of the comatose patient based on the comparison data group and the target feature data;

[0009] Stimulation parameters for stimulating the comatose patient are adjusted based on the current coma level, and the trigeminal nerve of the comatose patient is stimulated based on the stimulation parameters.

[0010] According to one aspect of the present application, a device for promoting awakening of a comatose patient based on EEG signals comprises:

[0011] An EEG signal acquisition module is used to acquire EEG signals of comatose patients, wherein the EEG signals include the frequency, amplitude, rhythm, etc. of brain waves;

[0012] a current coma degree determination module, which obtains corresponding target feature data based on the EEG signal, retrieves a comparison data group corresponding to the target feature data from a database, and determines the current coma degree of the comatose patient based on the comparison data group and the target feature data;

[0013] The stimulation module adjusts stimulation parameters for stimulating the comatose patient based on the current coma level, and stimulates the trigeminal nerve of the comatose patient based on the stimulation parameters.

[0014] In an exemplary embodiment, the device includes, but is not limited to:

[0015] A reaction data acquisition module is used to obtain the reaction data of the comatose patient after being stimulated, wherein the reaction data includes the frequency, amplitude, rhythm, etc. of the brain wave;

[0016] A real-time adjustment module is used to adjust stimulation parameters for stimulating the comatose patient in real time based on the response data.

[0017] In an exemplary embodiment, the device includes, but is not limited to:

[0018] A target feature data acquisition module, used to acquire the corresponding target feature data based on time changes;

[0019] a coma degree set acquisition module, which records the current coma degree of the coma patient in different time periods based on the plurality of target feature data, and is used to acquire a corresponding coma degree set;

[0020] The optimal time period determination module is used to determine the optimal time period for performing stimulation treatment on the comatose patient based on the coma level set.

[0021] In an exemplary embodiment, the device includes, but is not limited to:

[0022] a feature data set acquisition module, configured to pre-process the EEG signal and perform Fourier transform to acquire a corresponding feature data set, wherein the feature data set includes a plurality of feature data;

[0023] A multi-set acquisition module is used to acquire multiple sets of feature data sets based on time changes;

[0024] The feature data group acquisition module classifies each feature data in the plurality of feature data sets to obtain a feature data group for each feature data, wherein the feature data group includes feature data in different time periods.

[0025] In an exemplary embodiment, the device includes, but is not limited to:

[0026] A retrieving module, used to retrieve the coma degree corresponding to the comatose patient and stimulation parameters for stimulating the comatose patient;

[0027] A stress response acquisition module is used to obtain the corresponding stress response of a comatose patient after being stimulated;

[0028] The recording module is used to record the coma degree, the stimulation parameters and the corresponding stress response of the comatose patient.

[0029] In an exemplary embodiment, the device includes, but is not limited to:

[0030] Identity information acquisition module, which obtains the identity information of the comatose patient;

[0031] The encryption module stores the identity information and the corresponding EEG signal and uses them to set access encryption.

[0032] According to one aspect of the present application, an electronic device includes at least one processor and at least one memory, wherein the memory stores computer-readable instructions; the computer-readable instructions are executed by one or more of the processors, so that the electronic device implements the method for awakening a comatose patient based on EEG signals as described above.

[0033] According to one aspect of the present application, a storage medium stores computer-readable instructions thereon, and the computer-readable instructions are executed by one or more processors to implement the above-mentioned method for promoting awakening of a comatose patient based on EEG signals.

[0034] According to one aspect of the present application, a computer program product includes computer-readable instructions, which are stored in a storage medium. One or more processors of an electronic device read the computer-readable instructions from the storage medium, load and execute the computer-readable instructions, so that the electronic device implements the method for awakening a comatose patient based on EEG signals as described above.

[0035] The beneficial effects brought about by the technical solution provided by the present application are: by collecting EEG signals, target feature data is obtained based on the EEG signal conversion, and by comparing the target feature data with similar data in an existing database, the coma degree of the current comatose patient can be determined through the comparison of feature data. For different coma degrees, the stimulation parameters for stimulating the trigeminal nerve of the comatose patient can be adjusted; through the above process, the coma degree can be determined by comparing with the data in the database in real time and the stimulation parameters can be adjusted, thereby realizing real-time monitoring of comatose patients during the awakening process.

[0036] During the stimulation of a comatose patient, the real-time coma degree of the comatose patient can be determined by the acquired reaction data based on the patient's real-time reaction, and the stimulation parameters can be adjusted in real time, thereby realizing dynamic adjustment of the stimulation parameters; while providing individualized treatment for comatose patients, by collecting the coma degree of the comatose patient during the treatment process and recording the stress response of the comatose patient during the treatment process, a more accurate treatment time period can be provided as a reference for subsequent comatose patients of the same type.

[0037] In the above technical solution, the current coma level of the coma patient can be determined by acquiring the EEG signal of the coma patient and then comparing the corresponding target feature data with the comparison data group in the database. Then, the stimulation parameters used to stimulate the coma patient are adjusted according to the current coma level of the coma patient. Then, the trigeminal nerve of the coma patient is stimulated based on the adjusted stimulation parameters, thereby achieving the effect of awakening the coma patient. The coma level can be determined by comparing with the data in the database in real time to adjust the stimulation parameters, thereby realizing real-time monitoring of the coma patient during the awakening process, thereby effectively solving the problem of low real-time monitoring of the coma patient during the awakening process in the related technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for describing the embodiments of the present application. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without inventive efforts.

[0039] FIG1 is a schematic diagram of an implementation environment according to the present application;

[0040] FIG2 is a flow chart showing a method for promoting awakening of a comatose patient based on EEG signals according to an exemplary embodiment;

[0041] FIG3 is a flowchart of steps S111 to S113 of another method for promoting awakening of a comatose patient based on EEG signals according to an exemplary embodiment;

[0042] FIG4 is a flowchart showing steps S114 to S116 of another method for promoting awakening of a comatose patient based on EEG signals according to an exemplary embodiment;

[0043] FIG5 is a flowchart showing steps S121 to S122 of another method for promoting awakening of a comatose patient based on EEG signals according to an exemplary embodiment;

[0044] FIG6 is a flowchart of steps S123 to S125 in another method for promoting awakening of a comatose patient based on EEG signals according to an exemplary embodiment;

[0045] FIG7 is a structural block diagram of a device for promoting awakening of a comatose patient based on EEG signals according to an exemplary embodiment;

[0046] Fig. 8 is a structural block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0047] The following describes in detail embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and are not to be construed as limiting the present application.

[0048] It will be understood by those skilled in the art that, unless expressly stated otherwise, the singular forms "a", "an", "said" and "the" used herein may also include the plural forms. It should be further understood that the term "comprising" used in the specification of the present disclosure refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. In addition, "connected" or "coupled" as used herein may include wireless connections or wireless couplings. The term "and / or" used herein includes all or any units and all combinations of one or more associated listed items.

[0049] As mentioned above, although existing technologies have played a certain role in the assessment and treatment of comatose patients, there is a lack of real-time monitoring and timely feedback to the treatment. From the above, it can be seen that how to improve the real-time monitoring of comatose patients during the awakening process remains to be solved.

[0050] To this end, the method for awakening a comatose patient based on EEG signals provided in this application can effectively improve the real-time monitoring of the awakening process of a comatose patient. Accordingly, the method for awakening a comatose patient based on EEG signals is suitable for an awakening device for a comatose patient based on EEG signals, and the awakening device for a comatose patient based on EEG signals can be deployed in an electronic device.

[0051] In order to make the objectives, technical solutions and advantages of this application clearer, the implementation methods of this application will be further described in detail below with reference to the accompanying drawings.

[0052] Figure 1 is a schematic diagram of an implementation environment involved in a method for promoting awakening of a comatose patient based on EEG signals. The implementation environment includes a terminal and a server.

[0053] The client provides a function for collecting EEG information, for example, an EEG collection device contacts the patient's scalp through an electrode array or a scalp sensor.

[0054] The server can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. This server is an electronic device used to provide background services. For example, in this implementation, the server provides cloud storage services for the EEG encoding database to the terminal.

[0055] Please refer to Figure 2. An embodiment of the present application provides a method for waking up a comatose patient based on EEG signals. The method is applicable to an electronic device, which may be a server in the implementation environment shown in Figure 1.

[0056] In the following method embodiments, for ease of description, the execution subject of each step of the method is taken as an electronic device as an example for illustration, but this does not constitute a specific limitation.

[0057] As shown in FIG2 , the method may include the following steps:

[0058] S100, obtaining EEG signals from comatose patients;

[0059] Among them, in an embodiment of the present application, when obtaining the EEG signal of a comatose patient, the EEG acquisition equipment can contact the patient's scalp through an electrode array or a scalp sensor to record the electrical activity of the patient's brain, thereby obtaining the EEG signal of the comatose patient; the EEG signal includes the frequency, amplitude, rhythm, etc. of the brain waves.

[0060] It should be pointed out here that when a person is in a coma, there are abnormal signals in the brain waves. Changes in the frequency of brain waves: a coma is often accompanied by changes in the frequency of brain waves, such as an increase in delta waves, which are usually associated with deep sleep or impaired consciousness; changes in the amplitude of brain waves: a coma may manifest as a decrease in the amplitude of brain waves, which reflects a decrease in brain activity; abnormal rhythms: in a coma, the EEG may show abnormal rhythms, such as slow fluctuations or specific patterns of waves, which may be associated with specific types of brain damage or dysfunction.

[0061] S110, obtaining an EEG signal of the comatose patient, obtaining corresponding target feature data based on the EEG signal, retrieving a comparison data set corresponding to the target feature data in a database, and determining the current coma level of the comatose patient based on the comparison data set and the target feature data;

[0062] After obtaining the EEG signal of the comatose patient, it is necessary to convert the corresponding EEG signal into feature data that can be compared, as shown in FIG3 . Therefore, the method further includes:

[0063] S111, preprocessing the EEG signal and performing Fourier transform to obtain a corresponding feature data set;

[0064] The feature data set includes multiple feature data, and the feature data may be one or more of frequency, amplitude, and rhythm.

[0065] S112, acquiring multiple sets of feature data sets based on time changes;

[0066] Among them, in the present application scheme, it is necessary to monitor the comatose patients in real time, so it is necessary to obtain corresponding special data sets for the comatose patients at different time points, so there will be corresponding feature data sets at different time points.

[0067] S113, classifying each feature data in the plurality of feature data sets to obtain a feature data group for each feature data;

[0068] Since there are corresponding feature data sets at different time points, by classifying each feature data in the feature data set, similar feature data in different time periods can be obtained.

[0069] By executing steps S111 to S113, in the process of awakening a comatose patient, the EEG characteristics of the comatose patient at different times can be recorded, and the changing characteristics of different types of EEG characteristics can be understood in the process of continuous time changes, which is more conducive to targeted awakening treatment for comatose patients.

[0070] In the process of determining the coma degree of a comatose patient by comparing target feature data, the specific process is as follows:

[0071] As mentioned above, the EEG characteristics associated with coma primarily include changes in EEG frequency, amplitude, and rhythm. These characteristics have a significant impact on subsequent control because they directly reflect the brain's activity and function. Changes in EEG frequency are particularly important, as different EEG frequencies are associated with different brain activity states.

[0072] During the analysis process, the accuracy of feature data comparison is affected by many factors, including the quality of the model and database used. Currently, EEG signal analysis generally adopts deep learning technologies, such as convolutional neural networks (CNN) and recurrent neural networks (RNN). These technologies can automatically extract data features for classification and improve the performance of the model as the amount of data increases. The process of comparing feature data usually involves comparing the individual's EEG signal with the standard signal stored in the database. These databases may include EEG signal datasets of different sleep stages, such as Sleep-EDF or SHHS.

[0073] Coma assessment: The most commonly used method for coma assessment is the Glasgow Coma Scale (GCS). This is a tool used to assess a patient's level of consciousness and is widely used in clinical practice. The GCS scale includes three assessment items: eye opening response, verbal response, and motor response on the non-hemiplegic side. The maximum total score is 15 points, with higher scores indicating milder conditions and lower scores indicating more severe conditions. A GCS score of 3-8 indicates severe head trauma requiring drainage; a GCS score of 9-12 indicates moderate head trauma, and a GCS score of 13-15 indicates mild head trauma.

[0074] The Glasgow Coma Scale (GCS) is primarily used in patients with varying degrees of coma after stroke, craniocerebral surgery, and other causes. Limitations of this scoring system include not incorporating brainstem reflexes, being unsuitable for alcoholics, and being inappropriate for patients taking sleeping pills. The goal is to analyze the correlation between EEG features and the GCS to determine the patient's coma status.

[0075] In the method for determining the current coma degree of a comatose patient based on comparing the data set with the target feature data, as shown in FIG4 , the method further includes:

[0076] S114, acquiring corresponding target feature data based on time changes.

[0077] S115, recording the current coma degree of the comatose patient in different time periods based on the multiple target feature data, and obtaining a corresponding coma degree set;

[0078] Among them, during the awakening treatment process for comatose patients, the coma degree of coma patients in different time periods is different.

[0079] S116: Determine an optimal time period for stimulation treatment of the comatose patient based on the coma level set.

[0080] By executing steps S114 to S116, during the awakening treatment of a comatose patient, the treatment effects of the comatose patient after treatment at different time points can be collected. For example, after one awakening treatment, it can be recorded whether the coma level of the comatose patient during this awakening treatment is alleviated or aggravated. By recording the treatment time of each awakening treatment and classifying the treatment effects after the awakening treatment in different time periods, the optimal time period for the comatose patient during the awakening treatment process can be determined.

[0081] In addition, in the process of determining the best time period for awakening treatment, the characteristic data of rhythm can also be used for selection. For example, the circadian rhythm of the brain waves of comatose patients may be different from that of ordinary people. During the coma period, the rhythm of the brain waves of comatose patients can be collected, and then the time period with more active rhythm can be found in all time periods for targeted stimulation, and other frequency bands can be stimulated during inactive time periods. By stimulating the time period with more active rhythm, the best time period can be determined.

[0082] S120 , obtaining an electroencephalogram signal of the comatose patient, adjusting stimulation parameters for stimulating the comatose patient based on the current coma level, and stimulating the trigeminal nerve of the comatose patient based on the stimulation parameters.

[0083] Among them, common stimulation methods include the following: non-invasive brain stimulation: such as transcranial direct current stimulation (regulating the excitability of the cerebral cortex through weak current) and repetitive transcranial magnetic stimulation (influencing brain remodeling and cortical reorganization through electromagnetic pulses); invasive brain stimulation: such as deep brain stimulation and vagus nerve stimulation, these methods can stimulate the patient's thalamic reticular nucleus; sensory stimulation programs: including exercise therapy, audio therapy, music therapy and multi-sensory training therapy.

[0084] Compared with other stimulation methods, the biggest difference of trigeminal nerve stimulation lies in its stimulation target and mechanism. Trigeminal nerve stimulation mainly targets the trigeminal nerve in the head, which is a local nerve stimulation method, while other methods such as transcranial direct current stimulation and repetitive transcranial magnetic stimulation are more focused on regulating the excitability of the cerebral cortex or affecting brain remodeling. In addition, invasive brain stimulation such as deep brain stimulation and vagus nerve stimulation usually require surgical intervention, while trigeminal nerve stimulation is generally non-invasive. Sensory stimulation projects involve stimulation of multiple sensory systems and are not limited to specific neural pathways or brain regions.

[0085] In the case of stimulating the comatose patient to awaken, as shown in FIG5 , the method further includes:

[0086] S121, obtaining response data of a comatose patient after being stimulated;

[0087] Among them, the response data includes the frequency, amplitude, rhythm, etc. of brain waves.

[0088] S122, adjusting stimulation parameters for stimulating the comatose patient in real time based on the response data.

[0089] During the awakening treatment of comatose patients, each stimulation of the comatose patient will have a corresponding stress response, and the corresponding EEG signal of the comatose patient will change. At this time, the acquired response data can be used to achieve subtle real-time adjustments to the stimulation parameters.

[0090] At the same time, during each stimulation treatment of the comatose patient, as shown in FIG6 , the method further includes:

[0091] S123, retrieving the coma degree corresponding to the comatose patient and stimulation parameters for stimulating the comatose patient;

[0092] As mentioned above, the stimulation parameters will be adjusted according to the coma level of the comatose patient, which will not be elaborated here. Different levels of coma will correspond to stimulation parameters for awakening treatment.

[0093] S124, obtaining the corresponding stress response of the comatose patient after being stimulated.

[0094] S125, recording the coma degree, stimulation parameters, and corresponding stress response of the comatose patient.

[0095] By executing steps S123 to S125, during each stimulation process for awakening a comatose patient, the stimulation parameters are continuously fine-tuned in real time. The coma level, stimulation parameters, and corresponding stress response of the comatose patient are recorded at each time, achieving personalized treatment for the comatose patient. This allows for more accurate assessment of the patient's condition and treatment effectiveness. The recorded data can also be used as a reference for treating other comatose patients. For example, if patient A1 observes that stimulation x1 is more effective at coma level n1, if patient B also detects the characteristics of coma level n1, stimulation x1 will be used. However, since patients A1 and A2 respond differently at coma level n1, the database is used to determine which stimulation x1-xn is most likely to produce a benefit. Then, based on patient B's response, the expectations for x1, x2, etc. in the database are adjusted.

[0096] During the stimulation process of awakening treatment for comatose patients, the information of the comatose patients needs to be stored, and the security and privacy protection of the stored information need to be guaranteed. Therefore, in the data acquisition and data storage stage of each treatment process, after obtaining the EEG signal of the comatose patient, the method also includes: obtaining the identity information of the comatose patient, and then storing the identity information and the corresponding EEG signal and setting access encryption. It should be pointed out here that the EEG signal includes various data in the entire awakening treatment process. After encryption, access rights are set for the stored information; when it is necessary to understand the relevant data of the above-mentioned comatose patients during the treatment process, access rights and corresponding passwords are required to access, thereby achieving safety and privacy protection for comatose patients.

[0097] The following are embodiments of the device of the present application, which can be used to implement the method for promoting awakening a comatose patient based on EEG signals involved in the present application. For details not disclosed in the device embodiments of the present application, please refer to the method embodiments of the method for promoting awakening a comatose patient based on EEG signals involved in the present application.

[0098] Please refer to FIG7 . In an embodiment of the present application, a device for promoting awakening of a comatose patient based on EEG signals is provided, including but not limited to:

[0099] The EEG signal acquisition module 200 is used to acquire EEG signals of comatose patients, wherein the EEG signals include the frequency, amplitude, rhythm, etc. of brain waves;

[0100] The current coma degree determination module 210 obtains corresponding target feature data based on the EEG signal, retrieves a comparison data set corresponding to the target feature data from a database, and determines the current coma degree of the comatose patient based on the comparison data set and the target feature data;

[0101] The stimulation module 220 adjusts stimulation parameters for stimulating the comatose patient based on the current coma level, and stimulates the trigeminal nerve of the comatose patient based on the stimulation parameters.

[0102] In an exemplary embodiment, the apparatus includes, but is not limited to:

[0103] The reaction data acquisition module 300 acquires the reaction data of the comatose patient after being stimulated, wherein the reaction data includes the frequency, amplitude, rhythm, etc. of the brain wave;

[0104] The real-time adjustment module 310 is used to adjust stimulation parameters for stimulating the comatose patient in real time based on the response data.

[0105] In an exemplary embodiment, the apparatus includes, but is not limited to:

[0106] Target feature data acquisition module 400, used to acquire corresponding target feature data based on time changes;

[0107] A coma level set acquisition module 410 records the current coma level of the comatose patient at different time periods based on multiple target feature data, and is used to acquire a corresponding coma level set;

[0108] The optimal time period determination module 420 is configured to determine the optimal time period for performing stimulation treatment on the comatose patient based on the coma level set.

[0109] In an exemplary embodiment, the apparatus includes, but is not limited to:

[0110] The feature data set acquisition module 500 is used to pre-process the EEG signal and perform Fourier transform to obtain a corresponding feature data set, wherein the feature data set includes multiple feature data;

[0111] A multiple set acquisition module 510 is used to acquire multiple sets of feature data sets based on time changes;

[0112] The feature data group acquisition module 520 classifies each feature data in the plurality of feature data sets to obtain a feature data group for each feature data, wherein the feature data group includes feature data in different time periods.

[0113] In an exemplary embodiment, the apparatus includes, but is not limited to:

[0114] A retrieving module 600 is used to retrieve the coma degree corresponding to the comatose patient and stimulation parameters for stimulating the comatose patient;

[0115] The stress response acquisition module 610 is used to acquire the stress response of the comatose patient after being stimulated;

[0116] The recording module 620 is used to record the coma degree, stimulation parameters and the corresponding stress response of the comatose patient.

[0117] In an exemplary embodiment, the apparatus includes, but is not limited to:

[0118] Identity information acquisition module 700, obtains identity information of the comatose patient;

[0119] The encryption module 710 stores the identity information and the corresponding EEG signal and uses them to set access encryption.

[0120] It should be noted that the above-mentioned embodiment provides an apparatus for awakening a comatose patient based on EEG signals, and only uses the division of the above-mentioned functional modules as an example to illustrate the awakening of a comatose patient based on EEG signals. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the apparatus for awakening a comatose patient based on EEG signals will be divided into different functional modules to complete all or part of the functions described above.

[0121] In addition, the device for awakening a comatose patient based on EEG signals and the method for awakening a comatose patient based on EEG signals provided in the above embodiments belong to the same concept, and the specific manner in which each module performs operations has been described in detail in the method embodiments and will not be repeated here.

[0122] Please refer to FIG8 . An embodiment of the present application provides an electronic device 4000 . The electronic device 400 may include a desktop computer, a laptop computer, a server, etc.

[0123] In FIG. 8 , the electronic device 4000 includes at least one processor 4001 and at least one memory 4003 .

[0124] Data exchange between the processor 4001 and the memory 4003 can be achieved through at least one communication bus 4002. The communication bus 4002 may include a path for transmitting data between the processor 4001 and the memory 4003. The communication bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The communication bus 4002 can be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is used in FIG8 , but this does not mean that there is only one bus or one type of bus.

[0125] Optionally, the electronic device 4000 may further include a transceiver 4004, which may be used for data exchange between the electronic device and other electronic devices, such as data transmission and / or data reception. It should be noted that in actual applications, the number of transceivers 4004 is not limited to one, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of the present application.

[0126] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 4001 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.

[0127] The memory 4003 can be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program instructions or codes in the form of instructions or data structures and can be accessed by the electronic device 400, but is not limited to these.

[0128] Computer-readable instructions are stored in the memory 4003 , and the processor 4001 can read the computer-readable instructions stored in the memory 4003 through the communication bus 4002 .

[0129] The computer-readable instructions are executed by one or more processors 4001 to implement the method for promoting awakening of a comatose patient based on EEG signals in the above-mentioned embodiments.

[0130] In addition, an embodiment of the present application provides a storage medium on which computer-readable instructions are stored. The computer-readable instructions are executed by one or more processors to implement the above-mentioned method for awakening a comatose patient based on EEG signals.

[0131] In an embodiment of the present application, a computer program product is provided. The computer program product includes computer-readable instructions, which are stored in a storage medium. One or more processors of an electronic device read the computer-readable instructions from the storage medium, load and execute the computer-readable instructions, so that the electronic device implements the method for awakening a comatose patient based on EEG signals as described above.

[0132] Compared with related technologies, the method collects EEG signals and converts them into target feature data. By comparing the target feature data with similar data in an existing database, the coma level of the current comatose patient can be determined through feature data comparison. For different coma levels, the stimulation parameters for stimulating the trigeminal nerve of the comatose patient can be adjusted. Through the above process, the coma level can be determined by comparing with the data in the database in real time to adjust the stimulation parameters, thereby realizing real-time monitoring of comatose patients during the awakening process.

[0133] During the stimulation of a comatose patient, the real-time coma degree of the comatose patient can be determined by the acquired reaction data based on the patient's real-time reaction, and the stimulation parameters can be adjusted in real time, thereby realizing dynamic adjustment of the stimulation parameters; while providing individualized treatment for comatose patients, by collecting the coma degree of the comatose patient during the treatment process and recording the stress response of the comatose patient during the treatment process, a more accurate treatment time period can be provided as a reference for subsequent comatose patients of the same type.

[0134] It should be understood that although the steps in the flowcharts of the accompanying drawings are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the flowcharts of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0135] The above description is only part of the implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for promoting awakening of a comatose patient based on electroencephalogram signals, characterized in that: include: Obtaining EEG signals from comatose patients, where the EEG signals include the frequency, amplitude, and rhythm of brain waves; Acquire corresponding target feature data based on the EEG signal, retrieve a comparison data group corresponding to the target feature data in a database, and determine the current coma degree of the comatose patient based on the comparison data group and the target feature data; Stimulation parameters for stimulating the comatose patient are adjusted based on the current coma level, and the trigeminal nerve of the comatose patient is stimulated based on the stimulation parameters.

2. The method according to claim 1, wherein In the method for adjusting stimulation parameters for stimulating a comatose patient based on the current coma level, the method further includes: Obtaining response data of comatose patients after being stimulated, including the frequency, amplitude, and rhythm of brain waves; Stimulation parameters for stimulating the comatose patient are adjusted in real time based on the response data.

3. The method according to claim 1, wherein In the method for determining the current coma degree of the comatose patient based on the comparison data set and the target characteristic data, the method further includes: Acquire the corresponding target feature data based on time changes; Recording the current coma degree of the comatose patient in different time periods based on the plurality of target feature data, and obtaining a corresponding coma degree set; An optimal time period for performing stimulation therapy on the comatose patient is determined based on the coma level set.

4. The method according to claim 1, wherein In the method for obtaining corresponding target feature data based on the EEG signal, the method further includes: Preprocessing and Fourier transforming the EEG signal to obtain a corresponding feature data set, wherein the feature data set includes multiple feature data; Acquire multiple sets of feature data sets based on time changes; Each feature data in the plurality of feature data sets is classified to obtain a feature data group for each feature data, wherein the feature data group includes feature data in different time periods.

5. The method according to claim 1, wherein During the process of stimulating the trigeminal nerve of the comatose patient based on the stimulation parameters, the method further includes: Retrieving the coma degree corresponding to the comatose patient and the stimulation parameters for stimulating the comatose patient; Obtain the corresponding stress response of comatose patients after being stimulated; The coma degree, the stimulation parameters and the corresponding stress response of the comatose patient are recorded.

6. The method according to claim 1, wherein After obtaining the EEG signal of the comatose patient, the method further includes: Obtaining identity information of comatose patients; The identity information and the corresponding EEG signal are stored and access encryption is set.

7. A device for promoting awakening of comatose patients based on EEG signals, characterized in that: include: An EEG signal acquisition module is used to acquire EEG signals of comatose patients, wherein the EEG signals include the frequency, amplitude, rhythm, etc. of brain waves; a current coma degree determination module, which obtains corresponding target feature data based on the EEG signal, retrieves a comparison data group corresponding to the target feature data from a database, and determines the current coma degree of the comatose patient based on the comparison data group and the target feature data; The stimulation module adjusts stimulation parameters for stimulating the comatose patient based on the current coma level, and stimulates the trigeminal nerve of the comatose patient based on the stimulation parameters.

8. The device according to claim 7, wherein The device further comprises: A reaction data acquisition module is used to obtain the reaction data of the comatose patient after being stimulated, wherein the reaction data includes the frequency, amplitude, rhythm, etc. of the brain wave; A real-time adjustment module is used to adjust stimulation parameters for stimulating the comatose patient in real time based on the response data.

9. An electronic device, characterized in that: include: at least one processor and at least one memory, wherein: The memory has computer-readable instructions stored thereon; The computer-readable instructions are executed by one or more of the processors, so that the electronic device implements the method for promoting awakening of a comatose patient based on EEG signals as claimed in any one of claims 1 to 6.

10. A storage medium having computer-readable instructions stored thereon, characterized in that: The computer-readable instructions are executed by one or more processors to implement the method for promoting awakening of a comatose patient based on EEG signals according to any one of claims 1 to 6.

Images