Sacral nerve implantation point location correctness evaluation method and system

Abstract

The invention discloses a sacral nerve implantation point position correctness evaluation method and system. Comprising the following steps: collecting a first sacral evoked response signal when a sacral nerve electrode is implanted into a correct point position and a second sacral evoked response signal when the sacral nerve electrode is not implanted into the correct point position; performing data processing and feature extraction on the sacral evoked response signals to obtain data features; constructing a decision tree model by taking the data features as input and taking whether the sacral nerve electrode is implanted into a correct point as an output result; and collecting a target sacral evoked response signal when a sacral nerve electrode to be detected is implanted into a target point location, and detecting whether the target point location belongs to a correct point location according to the target sacral evoked response signal and the decision tree model for identifying the implanted point location. According to the method disclosed by the embodiment of the invention, whether the sacral nerve electrode is at the correct point can be automatically judged.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of biological signal analysis, and in particular to a method and system for evaluating the correctness of sacral nerve implantation points. Background Art

[0002] Sacral neuromodulation (SNM) is an established treatment modality used to treat patients with symptoms such as overactive bladder (OAB), non-obstructive urinary retention (NOUR), and fecal incontinence (FI) who do not respond to conservative treatment. In the current standard of care for SNM, physicians rely on visually observed motor responses (motor thresholds) or patient-reported sensory responses (sensory thresholds) as a guide to determine if the implant site is correct and to set the treatment schedule based on this.

[0003] Motor threshold is defined as the stimulus amplitude required to elicit the first observable motor response, such as contraction of the external sphincter, abdomen, or toes. Sensory threshold is the stimulus amplitude at which the patient first reports a sensation of stimulation.

[0004] At present, the evaluation of whether the implantation point is correct and the setting of the stimulation program for treatment mainly relies on the motor threshold or sensory threshold, but both methods have disadvantages. The motor threshold relies on the visual observation of the motor response, and slight motor responses may be difficult to distinguish, and are related to the subjective judgment of the doctor, which may lead to inaccurate judgment results. In addition, after the sacral nerve electrode is implanted, it may be displaced due to the patient's movement and other reasons, affecting the treatment effect. At present, it can only be judged whether the electrode is displaced and whether the treatment is effective based on the patient's feeling, and the patient's perception of stimulation may be unreliable. Therefore, the process of judging whether the implantation point is correct, whether the electrode is displaced after implantation, and whether the treatment effect is effective is cumbersome and lacks clear evidence. Using these methods cannot correctly and objectively evaluate the correctness of the electrode implantation point or predict the effectiveness of the treatment. Summary of the invention

[0005] The present invention aims to solve at least one of the problems in the related art to a certain extent. The embodiment of the present invention provides a method and system for evaluating the correctness of sacral nerve implantation points, which reduces the variability caused by humans by measuring and characterizing the sacral evoked response generated by sacral nerve stimulation, and more reliably evaluates the implantation point and treatment effect.

[0006] In a first aspect, an embodiment of the present invention provides a method for evaluating the correctness of a sacral nerve implantation point, comprising: Collecting the first sacral evoked response signal when the sacral nerve electrode is implanted in the correct position, and the second sacral evoked response signal when the sacral nerve electrode is not implanted in the correct position; Processing the first sacral evoked response signal to obtain first processed data when the sacral nerve electrode is implanted at a correct location; processing the second sacral evoked response signal to obtain second processed data when the sacral nerve electrode is not implanted at a correct location; Performing feature extraction on the first processed data to obtain first data features when the sacral nerve electrode is implanted at a correct location; performing feature extraction on the second processed data to obtain second data features when the sacral nerve electrode is not implanted at a correct location; Taking the first data feature as input and the correct implantation position of the sacral nerve electrode as output; and taking the second data feature as input and the incorrect implantation position of the sacral nerve electrode as output; constructing a decision tree model for identifying the implantation position and completing model training; A target sacral evoked response signal is collected when the sacral nerve electrode to be detected is implanted in the target point, and whether the target point is a correct point is detected based on the target sacral evoked response signal and the decision tree model for identifying the implantation point.

[0007] Furthermore, the data processing method includes: data clipping and data preprocessing; The data clipping process is specifically as follows: Through the time window, the sacral evoked response signal within the preset time period after the stimulation is intercepted as the effective signal data, and the formula is:

[0008] in, is the valid signal data, is the sacral evoked response signal, is the stimulation end time, is the start time of the preset time period, is the end time of the preset time period. is the sampling rate.

[0009] Furthermore, the data preprocessing process specifically includes: filtering the effective signal data; using the recursive least squares method to remove stimulation artifacts from the filtered data to obtain processed data.

[0010] Further, the first data feature includes the peak-to-peak amplitude, latency, and number of peaks in the first processed data; the second data feature includes the peak-to-peak amplitude, latency, and number of peaks in the second processed data; The peak-to-peak amplitude is the difference between the maximum positive peak and the maximum negative peak in the data following the maximum negative peak; The latent period is the time delay from the start time of the preset time period to the appearance time of the maximum negative peak; The peak number is the number of peaks in the data after the maximum negative peak.

[0011] Furthermore, the decision tree generation algorithm adopts the ID3 information gain algorithm or the CART Gini index algorithm.

[0012] Furthermore, after the decision tree is built, cross-validation is used to test the accuracy of the model.

[0013] Further, a target sacral evoked response signal is collected when the sacral nerve electrode to be detected is implanted in the target point, and whether the target point is a correct point is detected according to the target sacral evoked response signal and the decision tree model for identifying the implantation point, including: Stimulate the target point where the sacral nerve electrode to be detected is implanted, and collect the target sacral induced response signal after the target point is stimulated; perform data processing on the target sacral induced response signal to obtain target processed data; perform feature extraction on the target processed data to obtain target data features; input the target data features into the decision tree model for identifying the implanted point, and obtain a detection result of whether the target point is a correct point.

[0014] In a second aspect, an embodiment of the present invention provides a sacral nerve implant position correctness assessment system, comprising: Data acquisition module: used to collect the first sacral evoked response signal when the sacral nerve electrode is implanted in the correct position, and the second sacral evoked response signal when the sacral nerve electrode is not implanted in the correct position; Data processing module: used for performing data processing on the first sacral evoked response signal to obtain first processed data when the sacral nerve electrode is implanted at a correct position; performing data processing on the second sacral evoked response signal to obtain second processed data when the sacral nerve electrode is not implanted at a correct position; Feature extraction module: used to extract features from the first processed data to obtain first data features when the sacral nerve electrode is implanted at the correct location; and to extract features from the second processed data to obtain second data features when the sacral nerve electrode is not implanted at the correct location; A model building module, for taking the first data feature as input and the correct implantation point of the sacral nerve electrode as output; and taking the second data feature as input and the incorrect implantation point of the sacral nerve electrode as output; building a decision tree model for identifying the implantation point and completing model training; The evaluation module is used to collect the target sacral induced response signal when the sacral nerve electrode to be detected is implanted in the target point, and detect whether the target point is a correct point according to the target sacral induced response signal and the decision tree model for identifying the implantation point.

[0015] In a third aspect, an embodiment of the present invention further provides an electronic device, the electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for assessing the correctness of sacral nerve implantation position as described in any embodiment of the present invention.

[0016] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for evaluating the correctness of sacral nerve implantation points described in any embodiment of the present invention when executed.

[0017] The embodiment of the present invention provides a method and system for evaluating the correctness of sacral nerve implantation points. The method collects sacral evoked response signals from different patients, performs preprocessing and feature extraction on the signals, and finally performs statistics and analysis on the extracted features to determine the relationship between the sacral evoked response and the electrode implantation point. In subsequent use, the sacral evoked response is used as an objective evaluation basis for the implantation point and the therapeutic effect. The method does not rely on the subjective feedback of the patient or the subjective observation of the doctor, and can provide objective feedback for the implantation point and the therapeutic effect evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is a schematic diagram of a sacral evoked response provided by the present invention; Figure 2 is a flow chart of a method for evaluating the correctness of sacral nerve implantation points in Embodiment 1 of the present invention; Figure 3 is a characteristic example diagram of the sacral evoked response in the first embodiment of the present invention; Figure 4 is a flow chart of the training phase in the first embodiment of the present invention; Figure 5 It is a flow chart of the use phase in the first embodiment of the present invention. DETAILED DESCRIPTION

[0019] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0020] Before introducing the contents of the embodiments of the present invention, it is necessary to explain that: Sacral evoked responses refer to the electrophysiological responses recorded from the S3 sacral nerve after stimulation of the represented sacral nerve. Figure 1 Schematic diagram of sacral evoked response provided by the present invention, such as Figure 1 As shown in the figure, the horizontal axis represents time and the vertical axis represents the amplitude of the electrical signal. At the moment of stimulation, the amplitude of the electrical signal reaches its maximum. After the stimulation ends, the amplitude of the electrical signal begins to gradually weaken, and neural reactions and myoelectric reactions occur successively, and then tend to stabilize. In addition, Figure 1 The amplitude of the electrical signal represents the intensity of the response of the sacral nerve after being stimulated, relative to the non-stimulated state; Figure 1 The electrical signal amplitude in the non-stimulation state is not shown in the figure. The values ​​above this amplitude represent positive stimulation responses, and the values ​​below this amplitude represent negative stimulation responses.

[0021] After stimulation, sacral evoked responses mainly include the following two electrophysiological responses: (1) Neural Responses: Evoked Compound Action Potentials (ECAPs), which are electrical signals recorded from near the electrodes when nerve fibers are stimulated; (2) Myoelectric Responses: electrical signals generated by muscle activation, usually muscle contraction caused by nerve stimulation.

[0022] Embodiment 1: Figure 2 This is a flow chart of a method for evaluating the correctness of sacral nerve implantation points provided in Embodiment 1 of the present invention. This embodiment can be applied to evaluating the correctness of electrode positions in sacral nerve regulation therapy. The method can be executed by a sacral nerve implantation point correctness evaluation system, such as Figure 2 As shown, the method specifically comprises the following steps: Step 210, collecting a first sacral evoked response signal when the sacral nerve electrode is implanted at a correct location, and a second sacral evoked response signal when the sacral nerve electrode is not implanted at a correct location.

[0023] Specifically, during the operation, the sacral evoked response signals of different patients after stimulation are collected when the sacral nerve electrodes are implanted in the correct position and when they are not implanted in the correct position; different sacral evoked response signals are marked with different labels according to whether the sacral nerve electrodes are implanted in the correct position. The labels are then used to distinguish whether the data corresponding to different features belongs to the data when the position is correct or the data when the position is incorrect.

[0024] Step 220, data processing is performed on the first sacral evoked response signal to obtain first processed data when the sacral nerve electrode is implanted at the correct location; data processing is performed on the second sacral evoked response signal to obtain second processed data when the sacral nerve electrode is not implanted at the correct location.

[0025] The data processing methods include: data clipping and data preprocessing. The preprocessing mainly includes filtering and recursive least squares method to remove artifacts.

[0026] In the embodiment of the present invention, the process of data clipping is specifically as follows: Through the time window, the sacral evoked response signal within the preset time period after the stimulation is intercepted as the effective signal data, and the formula is:

[0027] in, is the valid signal data, is the sacral evoked response signal, is the stimulation end time, is the start time of the preset time period, is the end time of the preset time period. is the sampling rate.

[0028] In a preferred embodiment, Select 1.25 milliseconds, 30 milliseconds were selected. That is, the data between 1.25 milliseconds and 30 milliseconds after the end of stimulation were used as valid signal data for subsequent analysis.

[0029] In the embodiment of the present invention, the process of data preprocessing is specifically as follows: Filter the effective signal data to remove the power frequency noise. Use a notch filter to filter out the 50Hz power frequency noise.

[0030]

[0031] in, is the filter function.

[0032] Furthermore, the recursive least squares method RLS is used to remove stimulation artifacts from the filtered data to obtain processed data.

[0033] The core idea of ​​the RLS algorithm is to recursively update the filter coefficients to minimize the error between the output signal and the expected signal.

[0034] Assuming the input signal , expected output signal , output signal is the input signal and the filter weight The weighted sum of:

[0035] The error is the difference between the expected output and the actual data:

[0036] The goal of RLS is to adjust the filter coefficients by , making the error Minimize. RLS adopts the least squares criterion and updates the filter through a recursive formula.

[0037] The filter is updated as follows:

[0038] in, is the gain vector:

[0039] The signal after removing the stimulation artifact is used as the processed data: .

[0040] In the embodiment of the present invention, the effective signal data is filtered to remove noise, and the recursive least square method is used to remove stimulation artifacts to ensure that the signal quality is suitable for subsequent analysis.

[0041] Step 230, feature extraction is performed on the first processed data to obtain first data features when the sacral nerve electrode is implanted at the correct location; feature extraction is performed on the second processed data to obtain second data features when the sacral nerve electrode is not implanted at the correct location.

[0042] Feature extraction was performed on the preprocessed data to obtain key physiological features such as peak-to-peak amplitude, peak latency, and the total number of peaks in each sacral evoked response.

[0043] Specifically, the sacral evoked response signals when the sacral nerve electrodes are implanted in the correct positions and the sacral evoked response signals when the sacral nerve electrodes are not implanted in the correct positions are collected. Both signals include the neural response process and the electromyographic response process. By comparing the features of the neural response process and the electromyographic response process, features that can distinguish between the two types of data are obtained.

[0044] The first data feature includes the peak-to-peak amplitude, latency, and number of peaks in the first processed data; the second data feature includes the peak-to-peak amplitude, latency, and number of peaks in the second processed data.

[0045] Figure 3 This is a characteristic example diagram of the sacral evoked response in the first embodiment of the present invention, combined with Figure 1 and Figure 3 , Figure 1The starting time is the end time of stimulation: , Figure 3 The starting time of is the starting time of the valid signal data time: .

[0046] In the embodiment of the present invention, the peak-to-peak amplitude is the difference between the maximum positive peak and the maximum negative peak in the data after the maximum negative peak:

[0047] by Figure 3 For example, the peak-to-peak amplitude is collected from the time when the largest negative peak appears. The largest negative peak is the first marked point, and the largest positive peak is the second marked point. The peak-to-peak amplitude is the difference between the two.

[0048] In an embodiment of the present invention, the incubation period is the time delay from the start time of the preset time period to the occurrence time of the maximum negative peak: Figure 3 The starting time of is the starting time of the valid signal data time: , as the starting time of the incubation period, and the end time of the incubation period is the time when the maximum negative peak occurs.

[0049] In the embodiment of the present invention, the peak number is the number of peaks in the data after the maximum negative peak: by Figure 3 For example, the peak number is also collected from the time when the largest negative peak appears, including two positive peaks and two negative peaks, among which, Figure 3 The first negative peak in is the maximum negative peak.

[0050] Step 240, taking the first data feature as input and the correct implantation location of the sacral nerve electrode as output; and taking the second data feature as input and the incorrect implantation location of the sacral nerve electrode as output; constructing a decision tree model for identifying the implantation location and completing model training.

[0051] In the embodiment of the present invention, the relationship between the sacral evoked response and the electrode position is determined by analyzing the sacral evoked response characteristics when the correct point is implanted and when the correct potential is not implanted. Specifically, the extracted feature data is input into a decision tree model and trained to obtain a classification model. The model can automatically determine whether the sacral nerve electrode is in the correct point based on different sacral evoked response signals.

[0052] Specifically, the peak-to-peak amplitude, latency, and number of peaks of the sacral evoked response signal when the sacral nerve electrode is implanted in the correct position, and the peak-to-peak amplitude, latency, and number of peaks of the sacral evoked response signal when the sacral nerve electrode is not implanted in the correct position, are input into the decision tree model. The decision tree recursively selects features for division according to the different labels attached to different sacral evoked response signals, and continuously optimizes the structure of the tree, and finally generates a decision tree that can classify different sacral evoked response signals.

[0053] Furthermore, after the decision tree is built, cross-validation is used to test the accuracy of the model. Based on the evaluation results, the structure of the tree is further optimized to avoid overfitting or underfitting.

[0054] In an embodiment of the present invention, features extracted from electrophysiological signals (such as peak-to-peak amplitude, latency, number of peaks, etc.) are used to construct a decision tree. The core of decision tree generation is to select the best features to split the data set so as to maximize the purity of each subset. The decision tree generation algorithm uses the ID3 information gain algorithm or the CART Gini index algorithm, which usually selects the optimal splitting features based on information gain or Gini index.

[0055] For ID3 information gain algorithm: There is a feature A and a data set D, information gain Calculated as:

[0056] Among them, is the entropy of the data set D, which represents the uncertainty of the data.

[0057]

[0058] in, is the probability of the i-th class in the data set, It is the entropy of the subset after segmentation by the value v of feature A in the dataset.

[0059] For the CART Gini index algorithm, the choice of split is based on the Gini index. The Gini index is defined as:

[0060] in, is the probability of the i-th class in the data set. For each feature A, the Gini index is calculated, and the feature with the smallest Gini index is selected to split the data set.

[0061] The process of generating a decision tree is as follows: Input features: The signal features collected in each test are used as data input. Each data point contains these feature values, which serve as the basis for decision tree training.

[0062] Training data set: Take whether the data is in the correct position as the category label and train the decision tree. Based on the different signal characteristics of the two types of data, the decision tree learns to divide the data into different categories according to these characteristics.

[0063] Decision tree generation: By recursively selecting features for division and continuously optimizing the tree structure, a decision tree is eventually generated that can classify whether the sacral evoked response signal is in the correct position.

[0064] Evaluation and optimization: After the decision tree is built, cross-validation is used to test the accuracy of the model. Based on the evaluation results, the structure of the tree is further optimized to avoid overfitting or underfitting.

[0065] Step 250, collecting a target sacral evoked response signal when the sacral nerve electrode to be detected is implanted in the target location, and detecting whether the target location is a correct location based on the target sacral evoked response signal and the decision tree model for identifying the implantation location.

[0066] In the embodiment of the present invention, during actual implantation, whether the implantation point is correct is evaluated based on the sacral evoked response characteristics; preferably, whether the electrode is displaced can also be determined based on the sacral evoked response characteristics; further, whether the treatment effect is effective can also be determined.

[0067] Specifically, stimulation is applied to the target point where the sacral nerve electrode to be detected is implanted, and the target sacral evoked response signal after the target point is stimulated is collected; data processing is performed on the target sacral evoked response signal to obtain target processed data; feature extraction is performed on the target processed data to obtain target data features; the target data features are input into a decision tree model for identifying implanted points to obtain a detection result of whether the target point is a correct point.

[0068] In the embodiment of the present invention, the implementation of the entire scheme includes two stages: a training stage and a use stage. The training stage refers to collecting sacral evoked response signals of a large number of subjects at different implantation sites, performing statistics and analysis after preprocessing and feature extraction, determining the relationship between sacral evoked responses and electrode sites, and then establishing a decision tree model. The use stage refers to the evaluation of the correctness of the implantation site based on the sacral evoked response data during the implantation process of the patient after the modeling is completed, and evaluating whether the electrode is displaced and whether the treatment is effective during the subsequent treatment process.

[0069] Figure 4 1 is a flow chart of the training phase in an embodiment of the present invention. The training phase is aimed at the detected sacral nerve electrode implantation points, and the sacral evoked response signals of these points are used as training data sets.

[0070] Specifically, the sacral evoked response signals of these points are first collected; then the sacral evoked response signals are subjected to data trimming and data preprocessing; then the processed data are subjected to feature extraction; finally, a decision tree is generated according to different features of different data.

[0071] Figure 5 It is a flow chart of the use phase in an embodiment of the present invention, and the use phase is aimed at the sacral nerve electrode implantation point to be detected.

[0072] Data collection: Collect the patient's physiological signal data in real time.

[0073] Data trimming: Data between 1.25 milliseconds and 30 milliseconds after the end of stimulation were selected as valid signal data for subsequent analysis.

[0074] Data preprocessing: Valid signal data were filtered to remove noise, and stimulation artifacts were removed using recursive least squares method to ensure that the signal quality was suitable for subsequent analysis.

[0075] Feature extraction: Extract key features from the processed signal, including peak-to-peak amplitude, latency, and number of peaks.

[0076] Effect evaluation: The extracted feature data is input into a pre-trained decision tree model to determine whether the electrode implantation point is correct; during use, determine whether the electrode is displaced and whether the therapeutic effect is effective.

[0077] Specifically, during the use phase, the sacral evoked response signals of the sacral nerve implantation points to be tested are first collected; then the sacral evoked response signals are subjected to data trimming and data preprocessing; then, feature extraction is performed on the processed data; then, these features are statistically analyzed and input into a trained decision tree model; finally, based on the classification results of the decision tree model, the effect of the sacral nerve implantation points to be tested is evaluated.

[0078] The technical solution of this embodiment collects sacral evoked response signals from different patients, pre-processes and extracts features, and finally performs statistics and analysis on the extracted features to determine the relationship between the sacral evoked response and the electrode implantation point. The sacral evoked response is used as an objective evaluation basis for the implantation point and the efficacy in subsequent use, avoiding the user's subjective feedback and the doctor's subjective observation. At the same time, no extra electrodes are required, which reduces the damage to the patient and reduces the power consumption of the entire system.

[0079] Embodiment 2: The embodiment of the present invention provides a sacral nerve implant point correctness assessment system, which is configured to implement the method described in any embodiment of the present invention. This embodiment can be applied to the situation of assessing the correctness of electrode position in sacral nerve regulation treatment. The system can be implemented in software and / or hardware. The sacral nerve implant point correctness assessment system specifically includes: Data acquisition module: used to collect the first sacral evoked response signal when the sacral nerve electrode is implanted in the correct position, and the second sacral evoked response signal when the sacral nerve electrode is not implanted in the correct position; Data processing module: used to process the first sacral evoked response signal to obtain first processed data when the sacral nerve electrode is implanted in the correct position; and to process the second sacral evoked response signal to obtain second processed data when the sacral nerve electrode is not implanted in the correct position; Feature extraction module: used to extract features from the first processed data to obtain first data features when the sacral nerve electrode is implanted at the correct location; and to extract features from the second processed data to obtain second data features when the sacral nerve electrode is not implanted at the correct location; A model building module, for taking the first data feature as input and the correct implantation point of the sacral nerve electrode as output; and taking the second data feature as input and the incorrect implantation point of the sacral nerve electrode as output; building a decision tree model for identifying the implantation point and completing model training; The evaluation module is used to collect the target sacral induced response signal when the sacral nerve electrode to be detected is implanted in the target point, and detect whether the target point is a correct point according to the target sacral induced response signal and the decision tree model for identifying the implantation point.

[0080] The technical solution of this embodiment does not require complex models and calculations, and has high operational efficiency. It uses data as an evaluation indicator, and the judgment of the implantation point is more accurate, and the judgment standards of different doctors or patients are consistent. It does not rely on the patient's subjective feedback or the doctor's subjective observation, and can provide objective feedback for the implantation point and efficacy evaluation.

[0081] Embodiment 3: The embodiment of the present invention further provides an electronic device, the electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for assessing the correctness of sacral nerve implantation position as described in any embodiment of the present invention.

[0082] Electronic devices are intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the invention described and / or claimed herein.

[0083] The embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processing device, a method for evaluating the correctness of sacral nerve implantation points in the embodiment of the present invention is implemented. The above-mentioned computer-readable medium of the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by an instruction execution system, device or device or used in combination with it. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0084] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0085] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0086] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: collects a first sacral evoked response signal when the sacral nerve electrode is implanted in the correct position, and a second sacral evoked response signal when the sacral nerve electrode is not implanted in the correct position; performs data processing on the first sacral evoked response signal to obtain first processed data when the sacral nerve electrode is implanted in the correct position; performs data processing on the second sacral evoked response signal to obtain second processed data when the sacral nerve electrode is not implanted in the correct position; performs feature extraction on the first processed data to obtain a first processed data when the sacral nerve electrode is implanted in the correct position. The invention discloses a method for detecting a first data feature when the sacral nerve electrode is not implanted at the correct location; extracting features from the second processed data to obtain a second data feature when the sacral nerve electrode is not implanted at the correct location; taking the first data feature as input and the sacral nerve electrode being implanted at the correct location as output; and taking the second data feature as input and the sacral nerve electrode not being implanted at the correct location as output; constructing a decision tree model for identifying the implantation location and completing model training; collecting a target sacral evoked response signal when the sacral nerve electrode to be detected is implanted at the target location, and detecting whether the target location is a correct location based on the target sacral evoked response signal and the decision tree model for identifying the implantation location.

[0087] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including, but not limited to, object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0088] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0089] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the name of a unit does not, in some cases, limit the unit itself.

[0090] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0091] In the context of the present disclosure, a computer-readable storage medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0092] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A method for evaluating the correctness of sacral nerve implantation points, characterized in that: include: Collecting the first sacral evoked response signal when the sacral nerve electrode is implanted in the correct position, and the second sacral evoked response signal when the sacral nerve electrode is not implanted in the correct position; Processing the first sacral evoked response signal to obtain first processed data when the sacral nerve electrode is implanted at a correct location; processing the second sacral evoked response signal to obtain second processed data when the sacral nerve electrode is not implanted at a correct location; Performing feature extraction on the first processed data to obtain a first data feature when the sacral nerve electrode is implanted at a correct location; Performing feature extraction on the second processed data to obtain second data features when the sacral nerve electrode is not implanted at a correct location; Taking the first data feature as input and the correct implantation position of the sacral nerve electrode as output; and taking the second data feature as input and the incorrect implantation position of the sacral nerve electrode as output; constructing a decision tree model for identifying the implantation position and completing model training; A target sacral evoked response signal is collected when the sacral nerve electrode to be detected is implanted in the target point, and whether the target point is a correct point is detected based on the target sacral evoked response signal and the decision tree model for identifying the implantation point.

2. The method according to claim 1, characterized in that The data processing method includes: data cutting and data preprocessing; The data clipping process is specifically as follows: Through the time window, the sacral evoked response signal within the preset time period after the stimulation is intercepted as the effective signal data, and the formula is: ； in, is the valid signal data, is the sacral evoked response signal, is the stimulation end time, is the start time of the preset time period, is the end time of the preset time period. is the sampling rate.

3. The method according to claim 2, characterized in that The data preprocessing process specifically includes: filtering the effective signal data; using the recursive least square method to remove stimulation artifacts from the filtered data to obtain processed data.

4. The method according to claim 2, characterized in that: The first data feature includes the peak-to-peak amplitude, latency, and number of peaks in the first processed data; the second data feature includes the peak-to-peak amplitude, latency, and number of peaks in the second processed data; The peak-to-peak amplitude is the difference between the maximum positive peak and the maximum negative peak in the data following the maximum negative peak; The latent period is the time delay from the start time of the preset time period to the appearance time of the maximum negative peak; The peak number is the number of peaks in the data after the maximum negative peak.

5. The method according to claim 1, characterized in that The decision tree generation algorithm uses the ID3 information gain algorithm or the CART Gini index algorithm.

6. The method according to claim 1, characterized in that After the decision tree is built, cross validation is used to test the accuracy of the model.

7. The method according to claim 1, characterized in that The target sacral evoked response signal when the sacral nerve electrode to be detected is implanted in the target point is collected, and whether the target point is a correct point is detected according to the target sacral evoked response signal and the decision tree model for identifying the implantation point, including: Stimulate the target point where the sacral nerve electrode to be detected is implanted, and collect the target sacral induced response signal after the target point is stimulated; perform data processing on the target sacral induced response signal to obtain target processed data; perform feature extraction on the target processed data to obtain target data features; input the target data features into the decision tree model for identifying the implanted point, and obtain a detection result of whether the target point is a correct point.

8. A sacral nerve implantation point correctness assessment system, characterized in that: The system is configured to implement the method according to any one of claims 1 to 7, and the system comprises: Data acquisition module: used to collect the first sacral evoked response signal when the sacral nerve electrode is implanted in the correct position, and the second sacral evoked response signal when the sacral nerve electrode is not implanted in the correct position; Data processing module: used for performing data processing on the first sacral evoked response signal to obtain first processed data when the sacral nerve electrode is implanted at a correct position; performing data processing on the second sacral evoked response signal to obtain second processed data when the sacral nerve electrode is not implanted at a correct position; Feature extraction module: used to extract features from the first processed data to obtain first data features when the sacral nerve electrode is implanted at the correct location; and to extract features from the second processed data to obtain second data features when the sacral nerve electrode is not implanted at the correct location; A model building module, for taking the first data feature as input and the correct implantation point of the sacral nerve electrode as output; and taking the second data feature as input and the incorrect implantation point of the sacral nerve electrode as output; building a decision tree model for identifying the implantation point and completing model training; The evaluation module is used to collect the target sacral induced response signal when the sacral nerve electrode to be detected is implanted in the target point, and detect whether the target point is a correct point based on the target sacral induced response signal and the decision tree model for identifying the implantation point.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the method for assessing the correctness of sacral nerve implantation position according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the sacral nerve implant position correctness assessment method according to any one of claims 1 to 7 when executed.

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