Postoperative limb movement function recovery method and system based on electrophysiological monitoring

Abstract

The invention relates to the technical field of movement limb recovery, in particular to a postoperative limb movement function recovery method and system based on electrophysiological monitoring, and the method comprises the steps: carrying out electrophysiological monitoring on a to-be-recovered part to obtain an original recovery data set, carrying out questionnaire survey on a to-be-recovered patient to obtain a data set of a patient before recovery, and confirming a complete recovery item. And according to the original recovery data set, the pre-recovery patient data set and the neural network model, determining an original recovery item in the complete recovery items, performing training effect evaluation on the patient to be recovered in a historical recovery period to obtain a stage effect value, and obtaining a target recovery item based on the stage effect value and the original recovery item. According to the method, personalized formulation of postoperative recovery plans of different patients can be realized, and the efficiency of postoperative limb movement function recovery is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of motor limb recovery, and in particular to a method and system for recovering limb motor function after surgery based on electrophysiological monitoring. Background Art

[0002] Postoperative recovery of limb motor function is an important topic in the field of modern medical rehabilitation. The recovery of limb motor function not only directly affects the patient's quality of life, but also is related to their mental health, social adaptability and long-term survival rate. Postoperative patients may face problems such as limited mobility and decreased daily living ability due to damaged nerve or muscle function. Therefore, how to help patients recover limb function as soon as possible through precise, personalized and dynamically adjusted recovery plans has become an important need in the field of postoperative rehabilitation.

[0003] Traditional techniques typically rely on the experience of physical therapists to design recovery training plans, mainly providing rehabilitation guidance through simple repetitive movements and subjective assessments. However, this method lacks accurate quantitative assessment of the patient's nerve and muscle function status, resulting in insufficient personalization of the recovery plan. In addition, this method cannot dynamically adjust training intensity and task difficulty, making it difficult to adapt to individual differences and changing needs during the patient's recovery process. Summary of the Invention

[0004] The present invention provides a postoperative limb motor function recovery method and system based on electrophysiological monitoring, the main purpose of which is to achieve personalized formulation of postoperative recovery plans for different patients and improve the efficiency of postoperative limb motor function recovery.

[0005] To achieve the above objectives, the present invention provides a method for postoperative limb motor function recovery based on electrophysiological monitoring, comprising: receiving a limb recovery instruction, determining a patient to be recovered based on the limb recovery instruction, and confirming a part of the patient to be recovered; Performing electrophysiological monitoring on the part to be recovered to obtain an original recovery data set, wherein the electrophysiological monitoring includes: neuroelectrophysiological monitoring and electromyography monitoring; Conducting a questionnaire survey on the patients to be recovered to obtain a pre-recovery patient data group, wherein the pre-recovery patient data group includes: patient psychological data, patient limb data, and quality of life data; Identify a complete recovery project, wherein the complete recovery project includes multiple complete recovery tasks, and each complete recovery task is set with a task completion count and a task difficulty index; Determining an original recovery project in a complete recovery project based on the original recovery data set, the pre-recovery patient data set, and the pre-trained neural network model, wherein the original recovery project includes a plurality of original recovery tasks, and the neural network model can perform a suitability score on the complete recovery tasks in the complete recovery project; Determining a current recovery period of the patient to be recovered, and confirming a historical recovery period based on the current recovery period, and evaluating the training effect of the patient to be recovered within the historical recovery period to obtain a stage effect value; Based on the stage effect value, the completion times of each original recovery task in the original recovery project are adjusted to obtain the target recovery project; Postoperative limb motor function recovery based on electrophysiological monitoring was completed based on the target recovery project.

[0006] Optionally, performing electrophysiological monitoring on the part to be recovered to obtain an original recovery data set includes: Connecting a pre-built nerve conduction velocity monitor to the area to be restored, wherein the nerve conduction velocity monitor includes: a stimulating electrode and a recording electrode, wherein the stimulating electrode is connected to the nerve path corresponding to the area to be restored, and the recording electrode is connected to the muscle corresponding to the area to be restored; Using the connected nerve conduction velocity monitor to perform nerve testing on the area to be restored, obtaining a raw nerve potential data set, wherein the raw nerve potential data set includes: nerve conduction velocity and compound muscle action potential. The nerve testing is performed by continuously applying electrical stimulation using the stimulating electrode until the recording electrode records a muscle response; Connecting a pre-built electromyograph to the part to be restored, wherein the electromyograph includes needle electrodes, and the needle electrodes are inserted into the muscles corresponding to the part to be restored; Performing muscle testing on the part to be recovered using the connected electromyograph to obtain a motor electromyogram, wherein the muscle testing is performed by the patient to be recovered exercising the part to be recovered and recording the electrical activity generated during the exercise; Based on the original nerve potential data group and the motor electromyogram, the original recovery data group is identified.

[0007] Optionally, identifying the original recovery data group based on the original nerve potential data group and the motor electromyogram includes: Acquire a standard nerve potential data set, compare the standard nerve potential data set with the original nerve potential data set, and obtain a restored nerve potential data set, wherein the restored nerve potential data set includes: a conduction velocity deviation value and a composite potential deviation value; Identify the motion muscle feature group in the motion electromyogram, compare the motion muscle feature group with a preset standard muscle feature group, and obtain a recovery muscle motion data group, wherein the motion muscle feature group includes: maximum amplitude and duration, and the recovery muscle motion data group includes: maximum amplitude deviation value and duration deviation value; The recovered nerve potential data set and the recovered muscle movement data set are combined to obtain an original recovered data set.

[0008] Optionally, the questionnaire survey is conducted on the patients to be recovered to obtain a pre-recovery patient data set, including: Constructing a patient recovery questionnaire, wherein the patient recovery questionnaire includes: a psychological status questionnaire, a limb function questionnaire, and a quality of life questionnaire, and each questionnaire item in the patient recovery questionnaire has a corresponding score; Obtaining a patient recovery score group set based on a patient recovery questionnaire, wherein the patient recovery score group set includes multiple patient recovery score groups, and the patient recovery score groups include: a psychological state score group, a limb function score group, and a quality of life score group; All the patient recovery groups in the patient recovery group set are merged to obtain the pre-recovery patient data group, wherein the psychological state score group, limb function score group and quality of life score group correspond to the patient psychological data, patient limb data and quality of life data respectively.

[0009] Optionally, determining the original recovery items in the complete recovery items based on the original recovery data set, the pre-recovery patient data set, and the pre-trained neural network model includes: Extract the full recovery tasks in the full recovery project one by one; Based on the original recovery data set and the pre-recovery patient data set, the complete recovery task is scored using a neural network model to obtain a task suitability score, wherein the neural network model includes: an input layer, a hidden layer, and an output layer, wherein the output layer has one node, and the output value of the output layer is the task suitability score; Aggregating the task suitability scores to obtain a task suitability score set, and identifying an original suitability score set in the task suitability score set using a preset suitability score threshold; The original recovery task set corresponding to the original fitness score set is identified in the complete recovery project, and the original recovery task set is recorded as the original recovery project.

[0010] Optionally, the use of a neural network model to score the complete recovery task to obtain a task suitability score includes: Identify a standard task feature group for a complete recovery task, wherein the standard task feature group includes: a standard task number and a standard difficulty index; Based on the standard task feature group, the original recovery data group and the pre-recovery patient data group, a patient recovery feature vector is constructed, wherein the patient recovery feature vector is expressed as: in, represents the patient recovery feature vector, represents the standard task feature group, Indicates the number of standard tasks in the standard task feature group, represents the standard difficulty index in the standard task feature group, Represents the original recovery data group, and Respectively represent the first and second original recovery data in the original recovery data group. Original recovery data, Indicates the number of original recovery data in the original recovery data group, represents the patient data set before recovery, and Respectively represent the first and second patient data in the patient data group before recovery. Patient data before recovery, represents the number of pre-reconstruction patient data in the pre-reconstruction patient data group; The patient recovery feature vector is input into the neural network model to obtain a task suitability score, where the number of nodes in the input layer of the neural network model is the same as the vector dimension of the patient recovery feature vector.

[0011] Optionally, the training effect evaluation of the patient to be recovered within the historical recovery period to obtain a stage effect value includes: Identify a task completion cycle group in a historical recovery cycle and an actual recovery project corresponding to the historical recovery cycle, wherein the task completion cycle group includes multiple task completion cycles, the actual recovery project includes multiple actual recovery tasks, and within one task completion cycle, the patient to be recovered only needs to complete one corresponding actual recovery task; Extract task completion cycles in the task completion cycle group in sequence, identify the actual recovery task corresponding to the task completion cycle in the actual recovery project, and obtain the theoretical completion times of the actual recovery task; Performing a motion standard evaluation on the actual recovery task to obtain a motion standard value, wherein the motion standard evaluation is performed by a trained physician, and the value of the motion standard value is between 0 and 1; Counting the actual number of times the patient to be recovered completes the actual recovery task within the task completion period, and calculating the task completion ratio based on the actual number of completions and the theoretical number of completions, wherein the task completion ratio is the ratio of the actual number of completions to the theoretical number of completions; The action standard values and task completion ratios are summarized respectively to obtain an action standard value group and a task completion ratio group, and the stage effect value is calculated based on the action standard value group and the task completion ratio group.

[0012] Optionally, calculating the stage effect value according to the action standard value group and the task completion ratio group includes: The stage effect value is calculated using the following formula: in, Indicates the stage effect value, Indicates the number of action specification values in the action specification value group or the number of task completion ratios in the task completion ratio group. Indicates the first Action specification value, Indicates the number of tasks in the completion ratio group. The percentage of tasks completed, Indicates taking the absolute value.

[0013] Optionally, adjusting the number of completions of each original restoration task in the original restoration project based on the stage effect value to obtain the target restoration project includes: Extracting original recovery tasks in the original recovery project in sequence, and determining the original completion times, original difficulty indexes, and original suitability scores of the original recovery tasks; Identify the maximum difficulty index of the original restoration project, and calculate the adjusted completion number using the following formula based on the original completion number, maximum difficulty index, original difficulty index, stage effect value, and original suitability score: in, Indicates the number of times adjustments are completed. Indicates the raw number of completions, represents the maximum difficulty index, represents the raw difficulty index, represents a power function with a natural constant as its base, represents the raw fitness score, Indicates the preset scoring coefficient; The original recovery task is updated based on the adjustment completion times to obtain a target recovery task, and the target recovery tasks are summarized to obtain a target recovery project.

[0014] To achieve the above objectives, the present invention further provides a postoperative limb motor function recovery system based on electrophysiological monitoring, comprising: A recovery data monitoring module is configured to receive a limb recovery instruction, determine a patient to be recovered based on the limb recovery instruction, identify the part of the patient to be recovered, perform electrophysiological monitoring on the part to be recovered, and obtain an original recovery data set, wherein the electrophysiological monitoring includes: neuroelectrophysiological monitoring and electromyography monitoring; A pre-recovery data collection module is used to conduct a questionnaire survey on patients to be recovered to obtain a pre-recovery patient data group, wherein the pre-recovery patient data group includes: patient psychological data, patient limb data and quality of life data, and to identify a complete recovery project, wherein the complete recovery project includes multiple complete recovery tasks, and each complete recovery task is set with a task completion number and a task difficulty index; an original project determination module, configured to determine an original recovery project in a complete recovery project based on the original recovery data set, the pre-recovery patient data set, and the pre-trained neural network model, wherein the original recovery project includes a plurality of original recovery tasks, and the neural network model can perform a suitability score on the complete recovery tasks in the complete recovery project; The task frequency adjustment module is used to determine the current recovery cycle of the patient to be recovered, and confirm the historical recovery cycle based on the current recovery cycle, evaluate the training effect of the patient to be recovered within the historical recovery cycle, and obtain the stage effect value. Based on the stage effect value, the completion frequency of each original recovery task in the original recovery project is adjusted to obtain the target recovery project.

[0015] In order to solve the above problem, the present invention further provides an electronic device, comprising: a memory storing at least one instruction; The processor executes the instructions stored in the memory to implement the above-mentioned postoperative limb motor function recovery method based on electrophysiological monitoring.

[0016] In order to solve the above problems, the present invention also provides a computer-readable storage medium, which stores at least one instruction, and the at least one instruction is executed by a processor in an electronic device to implement the above-mentioned postoperative limb motor function recovery method based on electrophysiological monitoring.

[0017] In order to solve the problems described in the background technology, the present invention first determines the patient to be recovered and the part to be recovered. This step provides accurate positioning and direction for subsequent electrophysiological monitoring and recovery plan formulation by clarifying the target patient and target part. Then, electrophysiological monitoring is performed on the part to be recovered to obtain an original recovery data group. This step quantitatively evaluates the nerve and muscle function status through electrophysiological monitoring. Then, a questionnaire survey is conducted on the patient to be recovered to obtain a pre-recovery patient data group. This step comprehensively quantifies the patient's psychological state, limb function and quality of life through the questionnaire survey, and provides multi-dimensional patient initial state data for the formulation of a recovery plan, thereby realizing the formulation of a personalized recovery plan. By confirming the complete recovery project, an optional framework and task template are provided for the formulation of subsequent personalized recovery plans, ensuring that the recovery plan can dynamically change with different patients and different recovery cycles. Furthermore, according to the original recovery data group and the number of patients before recovery, According to the group and pre-trained neural network model, the original recovery project is determined in the complete recovery project. This step intelligently selects the recovery task suitable for the patient through the neural network model, and combines the patient's specific data to ensure the scientificity and personalization of the recovery plan, thereby improving the recovery efficiency and maximizing the recovery effect. Finally, the current recovery cycle of the patient to be recovered is determined, and the historical recovery cycle is confirmed based on the current recovery cycle. The training effect of the patient to be recovered is evaluated within the historical recovery cycle to obtain a stage effect value, and based on the stage effect value, the number of completions of each original recovery task in the original recovery project is adjusted to obtain a target recovery project. This step introduces the stage effect value to quantify the recovery situation of the patient to be recovered within the historical recovery cycle, and dynamically adjusts the number of completions of the recovery task in combination with the stage effect value, thereby optimizing the difficulty and intensity of the recovery plan, which ensures the adaptability of the recovery process to the patient and improves the recovery effect of different patients. Therefore, the present invention can realize the personalized formulation of postoperative recovery plans for different patients and improve the efficiency of postoperative limb motor function recovery. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic flow chart of a method for postoperative limb motor function recovery based on electrophysiological monitoring provided by one embodiment of the present invention; Figure 2 This is a functional module diagram of a postoperative limb motor function recovery system based on electrophysiological monitoring provided by one embodiment of the present invention; Figure 3 A schematic structural diagram of an electronic device for implementing the postoperative limb motor function recovery method based on electrophysiological monitoring provided in one embodiment of the present invention.

[0019] Description of reference numerals: 1. Electronic device; 10. Processor; 11. Memory; 12. Bus.

[0020] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0021] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0022] The embodiment of the present application provides a method for recovering limb motor function after surgery based on electrophysiological monitoring. The execution subject of the method for recovering limb motor function after surgery based on electrophysiological monitoring includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the method for recovering limb motor function after surgery based on electrophysiological monitoring can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0023] Reference Figure 1 FIG2 is a flow chart of a method for recovering limb motor function after surgery based on electrophysiological monitoring according to an embodiment of the present invention. In this embodiment, the method for recovering limb motor function after surgery based on electrophysiological monitoring includes: S1. Receive a limb recovery instruction, determine a patient to be recovered based on the limb recovery instruction, and confirm the part to be recovered of the patient to be recovered.

[0024] It can be understood that the limb recovery instruction refers to an instruction initiated manually to perform limb recovery on a specific patient, the patient to be recovered refers to the specific patient indicated in the limb recovery instruction, and the part to be recovered refers to the limb part of the patient to be recovered that needs limb movement recovery, such as the left calf, etc.

[0025] S2. Perform electrophysiological monitoring on the part to be recovered to obtain an original recovery data set, wherein the electrophysiological monitoring includes: neuroelectrophysiological monitoring and electromyography monitoring.

[0026] It is clear that the electrophysiological monitoring includes neuroelectrophysiological monitoring and electromyographic monitoring, wherein electrophysiological monitoring can detect the nerve conduction velocity and compound muscle action potential of the part to be recovered, thereby realizing the quantitative evaluation of the nerve function state, and electromyographic monitoring can dynamically record the muscle electrical activity during the movement of the part to be recovered, thereby realizing the accurate analysis of muscle activity ability and coordination. Therefore, the combination of neuroelectrophysiological monitoring and electromyographic monitoring can realize the comprehensive monitoring and optimization adjustment effect of the coordinated recovery of nerve and muscle function. The original recovery data group refers to the data combination obtained after electrophysiological monitoring, which includes the recovery nerve potential data group obtained by neuroelectrophysiological monitoring, and the recovery muscle movement data group obtained by electromyographic monitoring. The detailed acquisition method of the recovery nerve potential data group and the recovery muscle movement data group will be given later.

[0027] Specifically, the electrophysiological monitoring of the part to be recovered is performed to obtain an original recovery data set, including: Connecting a pre-built nerve conduction velocity monitor to the area to be restored, wherein the nerve conduction velocity monitor includes: a stimulating electrode and a recording electrode, wherein the stimulating electrode is connected to the nerve path corresponding to the area to be restored, and the recording electrode is connected to the muscle corresponding to the area to be restored; Using the connected nerve conduction velocity monitor to perform nerve testing on the area to be restored, obtaining a raw nerve potential data set, wherein the raw nerve potential data set includes: nerve conduction velocity and compound muscle action potential. The nerve testing is performed by continuously applying electrical stimulation using the stimulating electrode until the recording electrode records a muscle response; Connecting a pre-built electromyograph to the part to be restored, wherein the electromyograph includes needle electrodes, and the needle electrodes are inserted into the muscles corresponding to the part to be restored; Performing muscle testing on the part to be recovered using the connected electromyograph to obtain a motor electromyogram, wherein the muscle testing is performed by the patient to be recovered exercising the part to be recovered and recording the electrical activity generated during the exercise; Based on the original nerve potential data group and the motor electromyogram, the original recovery data group is identified.

[0028] It is understood that the nerve conduction velocity monitor refers to a device used to measure nerve signal conduction velocity and muscle response potential, such as the Neuropack series of neuroelectrophysiological detectors. The stimulating electrode refers to an electrode device attached to the nerve pathway at the site to be restored, and its function is to apply controllable electrical stimulation to activate nerve fibers. The recording electrode refers to an electrode device attached to the corresponding muscle at the site to be restored, and its function is to capture and record the action potential signals generated by the muscle. The detailed process of the nerve detection is as follows: the stimulating electrode applies electrical stimulation (such as a square wave pulse) to trigger the nerve impulse, and the recording electrode synchronously collects the muscle response signal. The nerve conduction velocity refers to the speed at which the nerve impulse is conducted along the nerve fiber during the nerve detection process, for example, 50m / s. The compound muscle action potential refers to the total potential amplitude generated by the synchronous discharge of multiple muscle fibers during the nerve detection process, for example, the action potential amplitude of the normal abductor pollicis brevis muscle is 5mV. Furthermore, the electromyograph refers to a device used to record muscle electrical activity, such as the Dantec Keypoint electromyograph. The needle electrode refers to a thin needle-shaped electrode inserted into the muscle to be detected, and its function is to directly collect the electrical signals generated by muscle movement. The detailed process of the muscle testing is as follows: the patient actively moves the part to be recovered, and the needle electrode records the electrical activity waveform during muscle contraction in real time. The movement electromyogram refers to the dynamic electrical signal spectrum during the muscle testing process, for example: the explosive high-amplitude wave group (amplitude 500μV, duration 200 ms) presented during normal biceps brachii contraction.

[0029] In detail, the identifying of the original recovery data group based on the original nerve potential data group and the motor electromyogram includes: Acquire a standard nerve potential data set, compare the standard nerve potential data set with the original nerve potential data set, and obtain a restored nerve potential data set, wherein the restored nerve potential data set includes: a conduction velocity deviation value and a composite potential deviation value; Identify the motion muscle feature group in the motion electromyogram, compare the motion muscle feature group with a preset standard muscle feature group, and obtain a recovery muscle motion data group, wherein the motion muscle feature group includes: maximum amplitude and duration, and the recovery muscle motion data group includes: maximum amplitude deviation value and duration deviation value; The recovered nerve potential data set and the recovered muscle movement data set are combined to obtain an original recovered data set.

[0030] It should be explained that the standard nerve potential data set refers to a numerical group of artificially set nerve potential data. The standard nerve potential data set includes: standard nerve conduction velocity and standard compound muscle action potential, wherein the standard nerve conduction velocity and standard compound muscle action potential refer to: the nerve conduction velocity and compound muscle action potential of healthy people of the same age as the patient to be recovered and with normal limb motor function, respectively. The standard nerve potential data set can be obtained through a pre-test or through historical data. The recovery nerve potential data set includes: conduction velocity deviation value and compound potential deviation value, wherein the conduction velocity deviation value refers to a numerical value indicating the degree of deviation between the nerve conduction velocity and the standard nerve conduction velocity in the standard nerve potential data set, and its calculation method is: ,in, Denotes the conduction velocity deviation value, represents the standard nerve conduction velocity, Represents nerve conduction velocity, the composite potential deviation value refers to the numerical value of the degree of deviation between the composite muscle action potential and the standard composite muscle action potential in the standard nerve potential data group. The calculation logic of the composite potential deviation value is the same as the calculation logic of the conduction velocity deviation value, which will not be repeated here. The motor muscle feature group refers to a numerical combination used to represent the characteristics of muscle activity in the motor muscle electrogram, wherein the maximum amplitude refers to the maximum peak amplitude of the electrical signal during muscle contraction, which can represent the intensity of muscle contraction, and the duration refers to the time span of the muscle electrical signal from the start to the end, which can represent the persistence of muscle contraction. The standard muscle feature group refers to an artificially set numerical group of muscle characteristics, and the standard muscle feature group includes: standard maximum amplitude and standard duration. The acquisition method of the standard muscle feature group is the same as that of the standard nerve potential data group, which will not be repeated here. The recovery muscle motion data group includes a maximum amplitude deviation value and a duration deviation value, wherein the maximum amplitude deviation value refers to a numerical value representing the degree of deviation between the maximum amplitude and the standard maximum amplitude in the standard muscle feature group, which is calculated as follows: ,in, Indicates the maximum amplitude deviation value, and Representing the standard maximum amplitude and maximum amplitude, respectively. The duration deviation value refers to the numerical value of the degree of deviation between the duration and the standard duration in the standard muscle feature group. Its calculation logic is the same as that of the maximum amplitude deviation value. Furthermore, the reason for using the recovery neural potential data group and the recovery muscle movement data group as the original recovery data group instead of directly using the original neural potential data group and the movement muscle feature group as the original recovery data group is that the deviation value can intuitively quantify the difference between the patient and the healthy standard, making it easier for the subsequent neural network model to evaluate recovery needs and dynamically adjust the training plan, while reducing the interference of individual baseline differences on the analysis.

[0031] S3. Conduct a questionnaire survey on the patients to be recovered to obtain a pre-recovery patient data group, wherein the pre-recovery patient data group includes: patient psychological data, patient limb data, and quality of life data.

[0032] It can be understood that the pre-recovery patient data group includes: patient psychological data, patient limb data and quality of life data, among which patient psychological data refers to data obtained through a psychological status questionnaire for evaluating the psychological state of patients to be recovered. Since the psychological state directly affects rehabilitation compliance and training effect, it is necessary to collect the patient's psychological data. Patient limb data refers to data obtained through a limb function questionnaire for evaluating the limb motor ability of patients to be recovered. Since the limb function level determines the difficulty design of the recovery task, it is necessary to collect the patient's limb data. Quality of life data refers to data obtained through a quality of life questionnaire for evaluating the current quality of life of patients to be recovered. Since the quality of life reflects the actual needs of the rehabilitation goals, for example: the current quality of life of a patient to be recovered is low, and the patient urgently hopes to quickly improve the quality of life through limb recovery. At this time, the difficulty or number of subsequent recovery tasks can be increased.

[0033] Specifically, a questionnaire survey is conducted on the patients to be recovered to obtain a pre-recovery patient data set, including: Constructing a patient recovery questionnaire, wherein the patient recovery questionnaire includes: a psychological status questionnaire, a limb function questionnaire, and a quality of life questionnaire, and each questionnaire item in the patient recovery questionnaire has a corresponding score; Obtaining a patient recovery score group set based on a patient recovery questionnaire, wherein the patient recovery score group set includes multiple patient recovery score groups, and the patient recovery score groups include: a psychological state score group, a limb function score group, and a quality of life score group; All the patient recovery groups in the patient recovery group set are merged to obtain the pre-recovery patient data group, wherein the psychological state score group, limb function score group and quality of life score group correspond to the patient psychological data, patient limb data and quality of life data respectively.

[0034] It should be explained that the psychological state questionnaire refers to the State-Trait Anxiety Questionnaire, wherein the State-Trait Anxiety Questionnaire is a psychological assessment tool, which includes the Trait Anxiety Questionnaire and the State Anxiety Questionnaire, which are used to distinguish an individual's temporary anxiety state (state anxiety) from a long-term anxiety tendency (trait anxiety), and the Trait Anxiety Questionnaire and the State Anxiety Questionnaire both include 20 items, and each item is scored from 0 to 3 points. The limb function questionnaire refers to the limb motor function scoring questionnaire, which is a standardized motor ability assessment tool. The questionnaire is obtained by designing it based on the internationally accepted Fugl-Meyer assessment scale or a similar standard tool. The quality of life questionnaire refers to the Barthel Index Scale, which is a daily living activity assessment tool widely used in rehabilitation medicine, covering the scoring of 10 basic activity abilities such as eating, dressing, and walking.

[0035] Furthermore, all questions involved in the above-mentioned patient recovery questionnaire have corresponding scores, so the pre-recovery patient data group can be obtained through the patient recovery questionnaire.

[0036] S4. Confirm a complete recovery project, wherein the complete recovery project includes multiple complete recovery tasks, and each complete recovery task is set with a task completion count and a task difficulty index.

[0037] It is understood that the complete recovery program refers to a combination of multiple tasks designed to guide recovery patients. The complete recovery program includes multiple complete recovery tasks, each of which refers to specific exercises that the patient needs to perform and is designed by a training physician. The number of task completions and task difficulty index refer to the number of times a recovery task is required and the difficulty of completing the recovery task, respectively.

[0038] S5. Determine an original recovery project in a complete recovery project based on the original recovery data group, the pre-recovery patient data group, and the pre-trained neural network model, wherein the original recovery project includes multiple original recovery tasks, and the neural network model can perform suitability scoring on the complete recovery tasks in the complete recovery project.

[0039] It should be explained that in order to carry out comprehensive limb recovery for the recovering patient, a complete recovery project, namely the above-mentioned complete recovery project, is first developed. This complete project includes many tasks. This complete project is developed by professional medical staff and can refer to similar rehabilitation plans for other patients in the past. However, the recovering patient does not have to perform all the tasks in this complete recovery project. For example, the patient's limbs have recovered to a certain extent, and some tasks in the complete recovery project do not need to be performed again, or in the early stage of recovery, the patient's mental state is poor and not suitable for completing certain tasks. According to the above description, it can be seen that the original recovery project suitable for the recovering patient to be executed in the current recovery cycle is identified from the complete recovery project. In addition, the tasks that appear in the complete recovery project do not necessarily have to be completed according to the number of task completions set in the complete project. For example, for a certain task, after analysis, it is found that the patient does not need to complete it according to the rated number of times, but only needs to complete it a smaller number of times. Therefore, the number of completions of each task in the original recovery project also needs to be adjusted to obtain the target recovery project. In order to achieve the above-mentioned purpose of identifying the original recovery project, this solution introduces a neural network model, which can output a suitability score through the input original recovery data, pre-recovery patient data and feature data of the complete recovery task (i.e., the subsequent standard task feature group). The suitability score reflects whether the input complete recovery task is suitable for the patient to be recovered in the current recovery cycle.

[0040] Furthermore, the neural network model is trained as follows: first, historical recovery data of other patients in a complete recovery task under the complete recovery project is collected. The historical recovery data includes: the original recovery data group corresponding to the patient, the pre-recovery patient data group, and the standard task feature group corresponding to the complete recovery task. The historical recovery data is the data obtained after the patient completes the complete recovery task under the guidance of the training physician. Then, the patient and the corresponding physician score the recovery situation. The score consists of the patient score and the physician score. The patient score represents the patient's satisfaction with the current complete recovery task. The higher the score, the more satisfied the patient is with the results of the recovery. The physician score represents the physician's degree of completion of the patient's complete recovery task. The higher the score, the higher the patient's degree of completion, that is, the more suitable the current complete recovery task is for the patient. The patient score and the physician score are weighted summed to obtain a task suitability score. Referring to the above acquisition method, a large number of task suitability scores of different patients in different recovery cycles and the historical recovery data corresponding to the patients can be obtained. The neural network model can be trained using the above large number of task suitability scores and historical recovery data.

[0041] In detail, determining the original recovery items in the complete recovery items based on the original recovery data set, the pre-recovery patient data set and the pre-trained neural network model includes: Extract the full recovery tasks in the full recovery project one by one; Based on the original recovery data set and the pre-recovery patient data set, the complete recovery task is scored using a neural network model to obtain a task suitability score, wherein the neural network model includes: an input layer, a hidden layer, and an output layer, wherein the output layer has one node, and the output value of the output layer is the task suitability score; Aggregating the task suitability scores to obtain a task suitability score set, and identifying an original suitability score set in the task suitability score set using a preset suitability score threshold; The original recovery task set corresponding to the original fitness score set is identified in the complete recovery project, and the original recovery task set is recorded as the original recovery project.

[0042] It is understood that the task suitability score refers to the output value of the neural network model, which indicates the suitability of the complete recovery task for the patient to be recovered. A higher score indicates a higher suitability. The suitability score threshold refers to a manually set constant, and the original suitability score refers to the task suitability score that is greater than the suitability score threshold. The original recovery task refers to the complete recovery task in the complete recovery project corresponding to the original suitability score.

[0043] In detail, the use of the neural network model to score the complete recovery task to obtain the task suitability score includes: identifying a standard task feature group of the complete recovery task, wherein the standard task feature group includes: a standard task number and a standard difficulty index; Based on the standard task feature group, the original recovery data group and the pre-recovery patient data group, a patient recovery feature vector is constructed, wherein the patient recovery feature vector is expressed as: in, represents the patient recovery feature vector, represents the standard task feature group, Indicates the number of standard tasks in the standard task feature group, represents the standard difficulty index in the standard task feature group, Represents the original recovery data group, and Respectively represent the first and second original recovery data in the original recovery data group. Original recovery data, Indicates the number of original recovery data in the original recovery data group, represents the patient data set before recovery, and Respectively represent the first and second patient data in the patient data group before recovery. Patient data before recovery, represents the number of pre-reconstruction patient data in the pre-reconstruction patient data group; The patient recovery feature vector is input into the neural network model to obtain a task suitability score, where the number of nodes in the input layer of the neural network model is the same as the vector dimension of the patient recovery feature vector.

[0044] It is understandable that the standard task number and standard difficulty index refer to the task completion number and task difficulty index corresponding to the complete recovery task, respectively. The patient recovery feature vector refers to a numerical vector composed of the standard task feature group, the original recovery data group, and the pre-recovery patient data group. The patient recovery feature vector is a data combination input into the neural network model and has no special physical meaning. It should be noted that before inputting the patient recovery feature vector into the neural network model, it is also necessary to perform data normalization on the vector. This normalization is a common and general technology in the field of deep learning and will not be repeated here.

[0045] S6. Determine the current recovery cycle of the patient to be recovered, and confirm the historical recovery cycle based on the current recovery cycle, and evaluate the training effect of the patient to be recovered within the historical recovery cycle to obtain a stage effect value.

[0046] It is understandable that the current recovery cycle refers to the period during which the patient to be recovered is currently undergoing limb recovery, for example, the current recovery cycle is the 5th week of limb recovery for the patient to be recovered. The historical recovery cycle refers to the recovery cycle before the current recovery cycle, for example, the previous historical recovery cycle before the 5th week is the 4th week. The stage effect value refers to a numerical value used to indicate the completion effect of the patient to be recovered in completing the established recovery task within the historical recovery cycle, wherein the established recovery task refers to the recovery task that the patient to be recovered needs to complete within the historical recovery cycle. The larger the stage effect value, the better the effect of the patient to be recovered in completing the recovery task.

[0047] It should be noted that if the current recovery cycle is the first week, the stage effect value will be recorded as 1.

[0048] Specifically, the training effect evaluation of the patient to be recovered during the historical recovery period to obtain the stage effect value includes: Identify a task completion cycle group in a historical recovery cycle and an actual recovery project corresponding to the historical recovery cycle, wherein the task completion cycle group includes multiple task completion cycles, the actual recovery project includes multiple actual recovery tasks, and within one task completion cycle, the patient to be recovered only needs to complete one corresponding actual recovery task; Extract task completion cycles in the task completion cycle group in sequence, identify the actual recovery task corresponding to the task completion cycle in the actual recovery project, and obtain the theoretical completion times of the actual recovery task; Performing a motion standard evaluation on the actual recovery task to obtain a motion standard value, wherein the motion standard evaluation is performed by a trained physician, and the value of the motion standard value is between 0 and 1; Counting the actual number of times the patient to be recovered completes the actual recovery task within the task completion period, and calculating the task completion ratio based on the actual number of completions and the theoretical number of completions, wherein the task completion ratio is the ratio of the actual number of completions to the theoretical number of completions; The action standard values and task completion ratios are summarized respectively to obtain an action standard value group and a task completion ratio group, and the stage effect value is calculated based on the action standard value group and the task completion ratio group.

[0049] It is understandable that the task completion cycle refers to the period of completing a certain actual recovery task in the actual recovery project, and the actual recovery project refers to the recovery project that the patient to be recovered needs to complete within the historical recovery cycle. For example: a historical recovery cycle is the 4th week, and a certain actual recovery project includes Task A, Task B and Task C, wherein Task A, Task B and Task C are all actual recovery tasks in the actual recovery project, and the period for the patient to be recovered to complete Task A is from the 1st day to the 3rd day, then the task completion cycle corresponding to the task to be recovered is from the 1st day to the 3rd day of the 4th week. The theoretical number of completions refers to the number of task completions corresponding to the actual recovery task, that is, the theoretical number of completions is the number of times the patient to be recovered should complete. The action standard value refers to the value used to indicate the degree of standardization of the action when the patient to be recovered performs the actual recovery task. The larger the action standard value, the higher the degree of standardization of the action when the patient to be recovered performs the actual recovery task. The actual number of completions refers to the number of times the patient to be recovered completes the actual recovery task.

[0050] Specifically, the step of calculating the stage effect value according to the action standard value group and the task completion ratio group includes: calculating the stage effect value using the following formula: in, Indicates the stage effect value, Indicates the number of action specification values in the action specification value group or the number of task completion ratios in the task completion ratio group. Indicates the first Action specification value, Indicates the number of tasks in the completion ratio group. The percentage of tasks completed, Indicates taking the absolute value.

[0051] It should be explained that the effect of patients completing tasks at different stages is different. For example, when patients perform a task, their movements are not very standard, and they do not complete the corresponding number of times. In order to describe the effect of such task completion, the action standard value and task completion ratio (i.e. Xiang Ji Item), among which, it is evaluated by trained physicians. The larger the value, the more standardized the action. The task completion ratio is the ratio of the actual number of completions to the number of times that should be completed. In addition, a comprehensive stage effect value is calculated by combining the action standard value and the task completion ratio. It represents the effect of the patient completing a task within a cycle. The larger the stage effect value, the better the effect of the patient completing the task. Due to the following situation: a patient completes a task very few times, even if the patient's action is very standard, its stage effect value should not be too large. Similarly, although the patient completes a task a lot of times, his action when completing the task is very non-standard. At this time, his stage effect value should not be too large. For this situation, the introduction of The purpose of this item is to punish the mismatch between the standardization of the action and the number of completions.

[0052] S7. Based on the stage effect value, the completion times of each original recovery task in the original recovery project are adjusted to obtain a target recovery project.

[0053] It is clear from the description of S5 that "In addition, , thereby obtaining the target recovery project” It can be seen that the purpose of the step of adjusting the completion times of each original recovery task in the original recovery project described here is.

[0054] Specifically, the process of adjusting the number of completions of each original restoration task in the original restoration project based on the stage effect value to obtain the target restoration project includes: Extracting original recovery tasks in the original recovery project in sequence, and determining the original completion times, original difficulty indexes, and original suitability scores of the original recovery tasks; Identify the maximum difficulty index of the original restoration project, and calculate the adjusted completion number using the following formula based on the original completion number, maximum difficulty index, original difficulty index, stage effect value, and original suitability score: in, Indicates the number of times adjustments are completed. Indicates the raw number of completions, represents the maximum difficulty index, represents the raw difficulty index, represents a power function with a natural constant as its base, represents the raw fitness score, Indicates the preset scoring coefficient; The original recovery task is updated based on the adjustment completion times to obtain a target recovery task, and the target recovery tasks are summarized to obtain a target recovery project.

[0055] It is understood that the original completion count, original difficulty index, and original suitability score refer to the task completion count, task difficulty index, and task suitability score corresponding to the original recovery task, respectively. Updating the original recovery task based on the adjusted completion count refers to adjusting the task completion count of the original recovery task from the original completion count to the adjusted completion count. The updated original recovery task is the target recovery task. The scoring coefficient refers to a manually set constant used to represent the weight of the original suitability score.

[0056] S8. Complete postoperative limb motor function recovery based on electrophysiological monitoring based on the target recovery program.

[0057] It should be explained that the target recovery project is the limb motor function recovery plan to be implemented for the patient to be recovered in the current recovery cycle. The above steps can be repeated for the formulation of recovery plans in other cycles.

[0058] In order to solve the problems described in the background technology, the present invention first determines the patient to be recovered and the part to be recovered. This step provides accurate positioning and direction for subsequent electrophysiological monitoring and recovery plan formulation by clarifying the target patient and target part. Then, electrophysiological monitoring is performed on the part to be recovered to obtain an original recovery data group. This step quantitatively evaluates the nerve and muscle function status through electrophysiological monitoring. Then, a questionnaire survey is conducted on the patient to be recovered to obtain a pre-recovery patient data group. This step comprehensively quantifies the patient's psychological state, limb function and quality of life through the questionnaire survey, and provides multi-dimensional patient initial state data for the formulation of a recovery plan, thereby realizing the formulation of a personalized recovery plan. By confirming the complete recovery project, an optional framework and task template are provided for the formulation of subsequent personalized recovery plans, ensuring that the recovery plan can dynamically change with different patients and different recovery cycles. Furthermore, according to the original recovery data group and the number of patients before recovery, According to the group and pre-trained neural network model, the original recovery project is determined in the complete recovery project. This step intelligently selects the recovery task suitable for the patient through the neural network model, and combines the patient's specific data to ensure the scientificity and personalization of the recovery plan, thereby improving the recovery efficiency and maximizing the recovery effect. Finally, the current recovery cycle of the patient to be recovered is determined, and the historical recovery cycle is confirmed based on the current recovery cycle. The training effect of the patient to be recovered is evaluated within the historical recovery cycle to obtain a stage effect value, and based on the stage effect value, the number of completions of each original recovery task in the original recovery project is adjusted to obtain a target recovery project. This step introduces the stage effect value to quantify the recovery situation of the patient to be recovered within the historical recovery cycle, and dynamically adjusts the number of completions of the recovery task in combination with the stage effect value, thereby optimizing the difficulty and intensity of the recovery plan, which ensures the adaptability of the recovery process to the patient and improves the recovery effect of different patients. Therefore, the present invention can realize the personalized formulation of postoperative recovery plans for different patients and improve the efficiency of postoperative limb motor function recovery.

[0059] like Figure 2 , which is a functional module diagram of a postoperative limb motor function recovery system based on electrophysiological monitoring provided by one embodiment of the present invention.

[0060] The electrophysiological monitoring-based postoperative limb motor function recovery system 100 described in the present invention can be installed in an electronic device. Depending on the functionality implemented, the electrophysiological monitoring-based postoperative limb motor function recovery system 100 can include a recovery data monitoring module 101, a pre-recovery data acquisition module 102, an original item determination module 103, and a task frequency adjustment module 104. A module, also referred to as a unit, is a series of computer program segments that can be executed by an electronic device processor and perform a fixed function, and is stored in the electronic device's memory.

[0061] The recovery data monitoring module 101 is configured to receive a limb recovery instruction, determine a patient to be recovered based on the limb recovery instruction, identify the part of the patient to be recovered, perform electrophysiological monitoring on the part to be recovered, and obtain an original recovery data set, wherein the electrophysiological monitoring includes: neuroelectrophysiological monitoring and electromyography monitoring; The pre-recovery data collection module 102 is used to conduct a questionnaire survey on the patients to be recovered to obtain a pre-recovery patient data group, wherein the pre-recovery patient data group includes: patient psychological data, patient limb data and quality of life data, and to identify a complete recovery project, wherein the complete recovery project includes multiple complete recovery tasks, and each complete recovery task is set with a task completion number and a task difficulty index; The original project determination module 103 is configured to determine an original recovery project in a complete recovery project based on the original recovery data set, the pre-recovery patient data set, and the pre-trained neural network model, wherein the original recovery project includes a plurality of original recovery tasks, and the neural network model can perform a suitability score on the complete recovery tasks in the complete recovery project; The task times adjustment module 104 is used to determine the current recovery cycle of the patient to be recovered, and confirm the historical recovery cycle based on the current recovery cycle, evaluate the training effect of the patient to be recovered within the historical recovery cycle, and obtain a stage effect value. Based on the stage effect value, the completion times of each original recovery task in the original recovery project are adjusted to obtain a target recovery project.

[0062] In detail, the modules in the postoperative limb motor function recovery system 100 based on electrophysiological monitoring according to the embodiment of the present invention are used in the same manner as above. Figure 1 The technical means are the same as the postoperative limb motor function recovery method based on electrophysiological monitoring described in, and can produce the same technical effects, so they will not be repeated here.

[0063] like Figure 3 FIG. 1 is a schematic structural diagram of an electronic device for implementing a method for recovering limb motor function after surgery based on electrophysiological monitoring, according to an embodiment of the present invention.

[0064] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a postoperative limb motor function recovery method program based on electrophysiological monitoring.

[0065] The memory 11 includes at least one type of readable storage medium, including flash memory, a mobile hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of the electronic device 1, such as a mobile hard disk of the electronic device 1. In other embodiments, the memory 11 may also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. equipped on the electronic device 1. Furthermore, the memory 11 includes both the internal storage unit of the electronic device 1 and an external storage device. The memory 11 can be used not only to store application software installed in the electronic device 1 and various types of data, such as the code of a postoperative limb motor function recovery method based on electrophysiological monitoring, but also to temporarily store data that has been output or is about to be output.

[0066] In some embodiments, the processor 10 may be comprised of an integrated circuit, such as a single packaged integrated circuit or a plurality of packaged integrated circuits with the same or different functions, including a combination of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control core (control unit) of the electronic device, connecting the various components of the electronic device using various interfaces and circuits. It executes programs or modules stored in the memory 11 (e.g., a postoperative limb motor function recovery method program based on electrophysiological monitoring) and accesses data stored in the memory 11 to perform various functions and process data.

[0067] The bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The bus 12 may be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to enable communication between the memory 11 and at least one processor 10, etc.

[0068] Figure 3 Only the electronic device with components is shown, and it can be understood by those skilled in the art that Figure 3The structure shown does not constitute a limitation on the electronic device 1 , and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0069] For example, although not shown, the electronic device 1 may further include a power source (e.g., a battery) to power various components. Preferably, the power source may be logically connected to the at least one processor 10 via a power management system, thereby enabling functions such as charge management, discharge management, and power consumption management through the power management system. The power source may further include any components such as one or more DC or AC power sources, a recharging system, a power failure detection circuit, a power converter or inverter, and a power status indicator. The electronic device 1 may further include various sensors, Bluetooth modules, Wi-Fi modules, etc., which are not further described here.

[0070] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0071] Optionally, the electronic device 1 may further include a user interface, which may be a display or an input unit (such as a keyboard). Optionally, the user interface may also be a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. The display may also be appropriately referred to as a display screen or a display unit, and is used to display information processed by the electronic device 1 and to display a visual user interface.

[0072] The postoperative limb motor function recovery method program based on electrophysiological monitoring stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve the following: receiving a limb recovery instruction, determining a patient to be recovered based on the limb recovery instruction, and confirming a part of the patient to be recovered; Performing electrophysiological monitoring on the part to be recovered to obtain an original recovery data set, wherein the electrophysiological monitoring includes: neuroelectrophysiological monitoring and electromyography monitoring; Conducting a questionnaire survey on the patients to be recovered to obtain a pre-recovery patient data group, wherein the pre-recovery patient data group includes: patient psychological data, patient limb data, and quality of life data; Identify a complete recovery project, wherein the complete recovery project includes multiple complete recovery tasks, and each complete recovery task is set with a task completion count and a task difficulty index; Determining an original recovery project in a complete recovery project based on the original recovery data set, the pre-recovery patient data set, and the pre-trained neural network model, wherein the original recovery project includes a plurality of original recovery tasks, and the neural network model can perform a suitability score on the complete recovery tasks in the complete recovery project; Determining a current recovery period of the patient to be recovered, and confirming a historical recovery period based on the current recovery period, and evaluating the training effect of the patient to be recovered within the historical recovery period to obtain a stage effect value; Based on the stage effect value, the completion times of each original recovery task in the original recovery project are adjusted to obtain the target recovery project; Postoperative limb motor function recovery based on electrophysiological monitoring was completed based on the target recovery project.

[0073] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figures 1 to 3 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0074] Furthermore, if the modules / units integrated into the electronic device 1 are implemented as software functional units and sold or used as independent products, they may be stored in a computer-readable storage medium. The computer-readable storage medium may be volatile or non-volatile. For example, the computer-readable medium may include any entity or system capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0075] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program. When the computer program is executed by a processor of an electronic device, the computer program can implement: receiving a limb recovery instruction, determining a patient to be recovered based on the limb recovery instruction, and confirming a part of the patient to be recovered; Performing electrophysiological monitoring on the part to be recovered to obtain an original recovery data set, wherein the electrophysiological monitoring includes: neuroelectrophysiological monitoring and electromyography monitoring; Conducting a questionnaire survey on the patients to be recovered to obtain a pre-recovery patient data group, wherein the pre-recovery patient data group includes: patient psychological data, patient limb data, and quality of life data; Identify a complete recovery project, wherein the complete recovery project includes multiple complete recovery tasks, and each complete recovery task is set with a task completion count and a task difficulty index; Determining an original recovery project in a complete recovery project based on the original recovery data set, the pre-recovery patient data set, and the pre-trained neural network model, wherein the original recovery project includes a plurality of original recovery tasks, and the neural network model can perform a suitability score on the complete recovery tasks in the complete recovery project; Determining a current recovery period of the patient to be recovered, and confirming a historical recovery period based on the current recovery period, and evaluating the training effect of the patient to be recovered within the historical recovery period to obtain a stage effect value; Based on the stage effect value, the completion times of each original recovery task in the original recovery project are adjusted to obtain the target recovery project; Postoperative limb motor function recovery based on electrophysiological monitoring was completed based on the target recovery project.

[0076] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only exemplary, and actual implementations may have other division methods.

[0077] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed across multiple network elements. Some or all of the modules may be selected to achieve the purpose of the solution of this embodiment according to actual needs.

[0078] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or hardware plus software functional modules.

[0079] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0080] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for postoperative limb motor function recovery based on electrophysiological monitoring, characterized in that: The method comprises: receiving a limb recovery instruction, determining a patient to be recovered based on the limb recovery instruction, and confirming a part of the patient to be recovered; Performing electrophysiological monitoring on the part to be recovered to obtain an original recovery data set, wherein the electrophysiological monitoring includes: neuroelectrophysiological monitoring and electromyography monitoring; Conducting a questionnaire survey on the patients to be recovered to obtain a pre-recovery patient data group, wherein the pre-recovery patient data group includes: patient psychological data, patient limb data, and quality of life data; Identify a complete recovery project, wherein the complete recovery project includes multiple complete recovery tasks, and each complete recovery task is set with a task completion count and a task difficulty index; Determining an original recovery project in a complete recovery project based on the original recovery data set, the pre-recovery patient data set, and the pre-trained neural network model, wherein the original recovery project includes a plurality of original recovery tasks, and the neural network model can perform a suitability score on the complete recovery tasks in the complete recovery project; Determining a current recovery period of the patient to be recovered, and confirming a historical recovery period based on the current recovery period, and evaluating the training effect of the patient to be recovered within the historical recovery period to obtain a stage effect value; Based on the stage effect value, the completion times of each original recovery task in the original recovery project are adjusted to obtain the target recovery project; Postoperative limb motor function recovery based on electrophysiological monitoring was completed based on the target recovery project.

2. The method for postoperative limb motor function recovery based on electrophysiological monitoring according to claim 1, characterized in that: The electrophysiological monitoring of the part to be recovered is performed to obtain an original recovery data group, including: Connecting a pre-built nerve conduction velocity monitor to the area to be restored, wherein the nerve conduction velocity monitor includes: a stimulating electrode and a recording electrode, wherein the stimulating electrode is connected to the nerve path corresponding to the area to be restored, and the recording electrode is connected to the muscle corresponding to the area to be restored; Using the connected nerve conduction velocity monitor to perform nerve testing on the area to be restored, obtaining a raw nerve potential data set, wherein the raw nerve potential data set includes: nerve conduction velocity and compound muscle action potential. The nerve testing is performed by continuously applying electrical stimulation using the stimulating electrode until the recording electrode records a muscle response; Connecting a pre-built electromyograph to the part to be restored, wherein the electromyograph includes needle electrodes, and the needle electrodes are inserted into the muscles corresponding to the part to be restored; Performing muscle testing on the part to be recovered using the connected electromyograph to obtain a motor electromyogram, wherein the muscle testing is performed by the patient to be recovered exercising the part to be recovered and recording the electrical activity generated during the exercise; Based on the original nerve potential data group and the motor electromyogram, the original recovery data group is identified.

3. The method for postoperative limb motor function recovery based on electrophysiological monitoring according to claim 2, characterized in that: The identifying of the original recovery data group based on the original nerve potential data group and the motor electromyogram includes: Acquire a standard nerve potential data set, compare the standard nerve potential data set with the original nerve potential data set, and obtain a restored nerve potential data set, wherein the restored nerve potential data set includes: a conduction velocity deviation value and a composite potential deviation value; Identify the motion muscle feature group in the motion electromyogram, compare the motion muscle feature group with a preset standard muscle feature group, and obtain a recovery muscle motion data group, wherein the motion muscle feature group includes: maximum amplitude and duration, and the recovery muscle motion data group includes: maximum amplitude deviation value and duration deviation value; The recovered nerve potential data set and the recovered muscle movement data set are combined to obtain an original recovered data set.

4. The method for postoperative limb motor function recovery based on electrophysiological monitoring according to claim 3, characterized in that: The patient data set before recovery is obtained by conducting a questionnaire survey on the patient to be recovered, including: Constructing a patient recovery questionnaire, wherein the patient recovery questionnaire includes: a psychological status questionnaire, a limb function questionnaire, and a quality of life questionnaire, and each questionnaire item in the patient recovery questionnaire has a corresponding score; Obtaining a patient recovery score group set based on a patient recovery questionnaire, wherein the patient recovery score group set includes multiple patient recovery score groups, and the patient recovery score groups include: a psychological state score group, a limb function score group, and a quality of life score group; All the patient recovery groups in the patient recovery group set are merged to obtain the pre-recovery patient data group, wherein the psychological state score group, limb function score group and quality of life score group correspond to the patient psychological data, patient limb data and quality of life data respectively.

5. The method for postoperative limb motor function recovery based on electrophysiological monitoring according to claim 4, characterized in that: Determining the original recovery items in the complete recovery items based on the original recovery data set, the pre-recovery patient data set, and the pre-trained neural network model includes: Extract the full recovery tasks in the full recovery project one by one; Based on the original recovery data set and the pre-recovery patient data set, the complete recovery task is scored using a neural network model to obtain a task suitability score, wherein the neural network model includes: an input layer, a hidden layer, and an output layer, wherein the output layer has one node, and the output value of the output layer is the task suitability score; Aggregating the task suitability scores to obtain a task suitability score set, and identifying an original suitability score set in the task suitability score set using a preset suitability score threshold; The original recovery task set corresponding to the original fitness score set is identified in the complete recovery project, and the original recovery task set is recorded as the original recovery project.

6. The method for postoperative limb motor function recovery based on electrophysiological monitoring according to claim 5, characterized in that: The neural network model is used to score the complete recovery task to obtain a task suitability score, including: Identify a standard task feature group for a complete recovery task, wherein the standard task feature group includes: a standard task number and a standard difficulty index; Based on the standard task feature group, the original recovery data group and the pre-recovery patient data group, a patient recovery feature vector is constructed, wherein the patient recovery feature vector is expressed as: in, represents the patient recovery feature vector, represents the standard task feature group, Indicates the number of standard tasks in the standard task feature group, represents the standard difficulty index in the standard task feature group, Represents the original recovery data group, and Respectively represent the first and second original recovery data in the original recovery data group. Original recovery data, Indicates the number of original recovery data in the original recovery data group, represents the patient data set before recovery, and Respectively represent the first and second patient data in the patient data group before recovery. Patient data before recovery, represents the number of pre-reconstruction patient data in the pre-reconstruction patient data group; The patient recovery feature vector is input into the neural network model to obtain a task suitability score, where the number of nodes in the input layer of the neural network model is the same as the vector dimension of the patient recovery feature vector.

7. The method for postoperative limb motor function recovery based on electrophysiological monitoring according to claim 6, characterized in that: The training effect evaluation of the patient to be recovered is performed during the historical recovery period to obtain a stage effect value, including: Identify a task completion cycle group in a historical recovery cycle and an actual recovery project corresponding to the historical recovery cycle, wherein the task completion cycle group includes multiple task completion cycles, the actual recovery project includes multiple actual recovery tasks, and within one task completion cycle, the patient to be recovered only needs to complete one corresponding actual recovery task; Extract task completion cycles in the task completion cycle group in sequence, identify the actual recovery task corresponding to the task completion cycle in the actual recovery project, and obtain the theoretical completion times of the actual recovery task; Performing a motion standard evaluation on the actual recovery task to obtain a motion standard value, wherein the motion standard evaluation is performed by a trained physician, and the value of the motion standard value is between 0 and 1; Counting the actual number of times the patient to be recovered completes the actual recovery task within the task completion period, and calculating the task completion ratio based on the actual number of completions and the theoretical number of completions, wherein the task completion ratio is the ratio of the actual number of completions to the theoretical number of completions; The action standard values and task completion ratios are summarized respectively to obtain an action standard value group and a task completion ratio group, and the stage effect value is calculated based on the action standard value group and the task completion ratio group.

8. The method for postoperative limb motor function recovery based on electrophysiological monitoring according to claim 7, characterized in that: The calculation of the stage effect value according to the action standard value group and the task completion ratio group includes: The stage effect value is calculated using the following formula: in, Indicates the stage effect value, Indicates the number of action specification values in the action specification value group or the number of task completion ratios in the task completion ratio group. Indicates the first Action specification value, Indicates the number of tasks in the completion ratio group. The percentage of tasks completed, Indicates taking the absolute value.

9. The method for postoperative limb motor function recovery based on electrophysiological monitoring according to claim 8, characterized in that: The method of adjusting the number of completions of each original restoration task in the original restoration project based on the stage effect value to obtain the target restoration project includes: Extracting original recovery tasks in the original recovery project in sequence, and determining the original completion times, original difficulty indexes, and original suitability scores of the original recovery tasks; Identify the maximum difficulty index of the original restoration project, and calculate the adjusted completion number using the following formula based on the original completion number, maximum difficulty index, original difficulty index, stage effect value, and original suitability score: in, Indicates the number of times adjustments are completed. Indicates the raw number of completions, represents the maximum difficulty index, represents the raw difficulty index, represents a power function with a natural constant as its base, represents the raw fitness score, Indicates the preset scoring coefficient; The original recovery task is updated based on the adjustment completion times to obtain a target recovery task, and the target recovery tasks are summarized to obtain a target recovery project.

10. A postoperative limb motor function recovery system based on electrophysiological monitoring, characterized in that: The system comprises: A recovery data monitoring module is configured to receive a limb recovery instruction, determine a patient to be recovered based on the limb recovery instruction, identify the part of the patient to be recovered, perform electrophysiological monitoring on the part to be recovered, and obtain an original recovery data set, wherein the electrophysiological monitoring includes: neuroelectrophysiological monitoring and electromyography monitoring; A pre-recovery data collection module is used to conduct a questionnaire survey on patients to be recovered to obtain a pre-recovery patient data group, wherein the pre-recovery patient data group includes: patient psychological data, patient limb data and quality of life data, and to identify a complete recovery project, wherein the complete recovery project includes multiple complete recovery tasks, and each complete recovery task is set with a task completion number and a task difficulty index; an original project determination module, configured to determine an original recovery project in a complete recovery project based on the original recovery data set, the pre-recovery patient data set, and the pre-trained neural network model, wherein the original recovery project includes a plurality of original recovery tasks, and the neural network model can perform a suitability score on the complete recovery tasks in the complete recovery project; The task frequency adjustment module is used to determine the current recovery cycle of the patient to be recovered, and confirm the historical recovery cycle based on the current recovery cycle, evaluate the training effect of the patient to be recovered within the historical recovery cycle, and obtain the stage effect value. Based on the stage effect value, the completion frequency of each original recovery task in the original recovery project is adjusted to obtain the target recovery project.

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