Neural pathfinding method, apparatus, and electronic device

Abstract

This invention relates to the field of electrophysiological monitoring technology, providing a method, device, and electronic device for determining neural pathways. The method includes: sequentially stimulating each stimulation point on a planar electrode array covering a region to be detected; acquiring electromyographic response signals corresponding to each stimulation point and determining the response intensity value; generating a visual distribution map based on the spatial position and response intensity value of each stimulation point in the planar electrode array; and determining the neural pathway based on the visual distribution map. This invention acquires electromyographic response signals from each stimulation point in space through multi-point sequential electrical stimulation of a planar electrode array, and generates a visual distribution map by combining the spatial position and quantified response intensity value. This allows for rapid and comprehensive acquisition of neural response characteristics within the region to be detected, transforming the originally discrete single-point response into a continuous neural pathway image, improving the operational efficiency and real-time performance of neural localization, and ensuring the objectivity and automation of neural localization.

Description

Technical Field

[0001] This invention relates to the field of electrophysiological monitoring technology, and in particular to a method, device, and electronic device for determining neural pathways. Background Technology

[0002] The nervous system, due to its delicate structure, specialized function, and high sensitivity to mechanical injury, is extremely susceptible to irreversible damage during clinical surgery. Especially in high-risk areas involving complex anatomical relationships and limited surgical fields, such as the brainstem, spinal cord, and recurrent laryngeal nerve, precise identification and functional protection of nerves during surgery are crucial for ensuring postoperative patient safety and maintaining basic physiological functions (such as speech, swallowing, and breathing).

[0003] To reduce the risk of intraoperative nerve injury, intraoperative neurophysiological monitoring (IONM) has been widely used and rapidly developed in recent years. This technology, by continuously and in real-time acquiring the electrophysiological response signals of the patient's nervous system under general anesthesia, helps surgeons identify potential risk events such as nerve traction, compression, and transection, and adjust surgical procedures accordingly, thereby effectively reducing the incidence of postoperative functional impairment. Especially in surgeries involving the recurrent laryngeal nerve (RLN), such as thyroid surgery, neck tumor resection, and spinal decompression, IONM has gradually become an important intraoperative nerve protection method. In current clinical applications, nerve pathway determination methods, represented by evoked electromyography (EMG), are one of the core tools of IONM. This method uses electrical stimulation probes to stimulate the surgical area tissue point by point and observes whether the corresponding muscles produce electromyographic responses to help determine the presence and distribution of nerves.

[0004] However, existing technologies still face a series of major problems in practical applications. Regarding operational efficiency, traditional trigger-based electromyography (EMG) localization relies on blind, point-by-point scanning by the physician, resulting in long operation times and poor real-time performance, easily causing delays in the surgical process. In terms of identification information, this method can only provide discrete point-like information indicating the presence or absence of a nerve, and cannot continuously and completely depict the nerve's trajectory, increasing the risk of missed detections or misdiagnosis. Furthermore, the entire nerve identification process is highly dependent on the physician's personal experience, is highly subjective, and is difficult to standardize and automate. Summary of the Invention

[0005] This invention provides a method, device, and electronic device for determining nerve pathways, which addresses the shortcomings of existing technologies, such as low operational efficiency, discontinuous information recognition, heavy reliance on doctors' personal experience, resulting in high risk of surgical delays, easy missed detections and misjudgments, and difficulty in standardization.

[0006] This invention provides a method for determining neural pathways, comprising the following steps.

[0007] Sequential electrical stimulation is applied to each stimulation point on the planar electrode array covering the area to be detected; Acquire the electromyographic response signals corresponding to each of the stimulation points, and determine the response intensity value characterizing the response intensity of each of the electromyographic response signals; Based on the spatial location of each stimulation point in the planar electrode array and the response intensity value, a visual distribution map characterizing the neural pathways in the area to be detected is generated. Based on the visualized distribution map, the neural pathway is determined.

[0008] According to a method for determining neural pathways provided by the present invention, determining the response intensity value characterizing the response intensity of each electromyographic response signal includes: Within a preset time window, the amplitude of each electromyographic response signal is determined; The response intensity value is determined based on the amplitude.

[0009] According to a method for determining neural pathways provided by the present invention, the method further includes: Determine the response start point; wherein the response start point includes a first time moment or a second time moment; the first time moment is the time when the slope of each electromyographic response signal exceeds a preset slope threshold, and the second time moment is the time when the amplitude of each electromyographic response signal continuously rises and reaches a preset minimum amplitude, and the duration of the rise in the amplitude of the electromyographic response signal is greater than the time corresponding to a preset minimum duration window. The latency period is determined based on the time difference between the response start point and the triggering time of each of the sequential electrical stimuli.

[0010] According to a method for determining neural pathways provided by the present invention, the method further includes: Stimulation points whose amplitude is less than a preset amplitude threshold or whose latency is greater than a preset latency threshold are identified as invalid points. The invalid points are then removed.

[0011] According to a method for determining neural pathways provided by the present invention, the step of generating a visual distribution map characterizing the neural pathways within the detection area based on the spatial position of each stimulation point in the planar electrode array and the response intensity value further includes: Based on the spatial position of each stimulation point in the planar electrode array, the adjacent points of each stimulation point are determined; The similarity of response intensity values ​​is determined based on the response intensity values ​​of each stimulus point and the response intensity values ​​of the adjacent points. If the similarity of the response intensity values ​​is higher than a preset similarity threshold, the stimulation current of the sequential electrical stimulation is reduced.

[0012] According to a method for determining neural pathways provided by the present invention, the method further includes: If all the response intensity values ​​are lower than the preset response threshold, the stimulation current of the sequential electrical stimulation is increased.

[0013] According to a method for determining neural pathways provided by the present invention, the step of generating a visual distribution map characterizing the neural pathways within the detection area based on the spatial position of each stimulation point in the planar electrode array and the response intensity value includes: The spatial locations corresponding to the stimulation points whose response intensity values ​​are greater than or equal to the preset display threshold are marked with a first color, and the spatial locations corresponding to the stimulation points whose response intensity values ​​are less than the preset display threshold are marked with a second color, thus obtaining the visualization distribution map; or, Based on the magnitude of the response intensity value, the spatial location corresponding to each stimulus point is mapped to a continuous color scale to obtain the visualization distribution map.

[0014] The present invention also provides a neural pathway determination device, comprising the following modules: An electrical stimulation module is used to sequentially electrically stimulate each stimulation point on a planar electrode array covering the area to be detected. The acquisition module is used to acquire the electromyographic response signal corresponding to each of the stimulation points and determine the response intensity value that characterizes the response intensity of each of the electromyographic response signals. The generation module is used to generate a visual distribution map characterizing the neural pathways in the area to be detected based on the spatial position of each of the stimulation points in the planar electrode array and the response intensity value. The determination module is used to determine the neural pathway based on the visualized distribution map.

[0015] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the neural pathway determination method as described above.

[0016] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the neural pathway determination method as described above.

[0017] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the neural pathway determination method as described above.

[0018] The present invention provides a method, apparatus, and electronic device for determining neural pathways. This method involves sequentially stimulating stimulation points on a planar electrode array covering a region to be detected; acquiring electromyographic (EMG) response signals corresponding to each stimulation point; determining response intensity values ​​characterizing the response intensity of each EMG signal; generating a visual distribution map characterizing the neural pathways within the region to be detected based on the spatial location of each stimulation point in the planar electrode array and the response intensity values; and determining the neural pathways based on the visual distribution map. This invention acquires EMG response signals from each stimulation point in space through multi-point sequential electrical stimulation of a planar electrode array, and generates a visual distribution map by combining spatial location and quantified response intensity values. This allows for rapid and comprehensive acquisition of neural response characteristics within the region to be detected, transforming previously discrete single-point responses into continuous neural pathway images. This improves the operational efficiency and real-time performance of neural localization, avoiding the problems of missed detections or misjudgments caused by traditional blind point-by-point scanning which only provides discrete information on presence or absence. This makes the neural recognition process less reliant on the operator's subjective experience, ensuring the objectivity and automation of neural localization. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0020] Figure 1 This is one of the flowcharts illustrating the neural pathway determination method provided by the present invention.

[0021] Figure 2 This is the second flowchart of the neural pathway determination method provided by the present invention.

[0022] Figure 3 This is the third flowchart of the neural pathway determination method provided by the present invention.

[0023] Figure 4 This is a schematic diagram of the electromyographic response signal preprocessing and artifact recognition process provided by the present invention.

[0024] Figure 5 This is a schematic diagram of the neural pathway determination device provided by the present invention.

[0025] Figure 6This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0027] The terms "first," "second," etc., used in this invention are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention can be implemented in orders other than those illustrated or described herein, and that the objects distinguished by "first," "second," etc., are generally of the same class.

[0028] This invention provides a method for determining nerve pathways, which is essentially a data processing and visualization mapping method based on multi-point electrical stimulation and electromyographic signal feature calculation. This method is not limited to the diagnosis or treatment of human diseases during clinical surgery. The core of this invention lies in achieving intuitive visualization of nerve pathways through multi-point stimulation, signal feature extraction, and matrix mapping, rather than drawing a diagnostic conclusion for a specific disease. This method can be applied to neurophysiological monitoring systems or related electrophysiological testing equipment. Figure 1 This is one of the flowcharts illustrating the neural pathway determination method provided by the present invention, such as... Figure 1 As shown, the method includes the following: Step 110: Sequential electrical stimulation is performed on each stimulation point on the planar electrode array covering the area to be detected.

[0029] Specifically, firstly, sequential electrical stimulation can be performed on each stimulation point on the planar electrode array covering the area to be detected.

[0030] The area to be detected refers to any physical space or tissue carrier that may contain neural pathways and requires detection and physiological signal analysis. For example, the area to be detected can be an isolated animal tissue specimen in scientific research experiments, such as an isolated thyroid gland and recurrent laryngeal nerve animal specimen, a biomimetic medical teaching model used in biomedical engineering research and development, or a non-medical in vivo animal experimental area used for research on neurophysiological mechanisms. In addition, the area to be detected can also be a target tissue or anatomical area that doctors need to identify and protect during surgical procedures, such as thyroid surgery, neck tumor resection, spinal decompression, and other surgeries involving complex anatomical areas such as the recurrent laryngeal nerve. This embodiment of the invention does not specifically limit this.

[0031] Here, a planar electrode array refers to a flexible cover with a planar shape and multiple independent electrode contacts distributed thereon. To achieve rapid localization of nerve pathways, this invention proposes a flexible planar electrode with a rectangular sheet structure that can cover the area to be tested. This planar electrode achieves automated sequential stimulation of multiple sites through multiple electrode contacts distributed in a rectangular dot matrix. In practical use, the planar electrode (or guide sheet) can be attached to the target area to be tested based on anatomical reference points (such as the thyroid cartilage, sternocleidomastoid muscle, etc. corresponding to the test subject). Furthermore, the circular stimulation electrode contacts on the planar electrode are arranged in a rectangular pattern, and the system supports adjusting the electrode contact spacing according to different test scenarios and area sizes, thereby forming multi-specification planar electrodes to adapt to different requirements. As an optional alternative implementation, when using manual stimulation, a guide sheet with a rectangular dot matrix can also be used in conjunction with a separate stimulation probe to achieve sequential stimulation of different sites according to the array points on the guide sheet. After the electrodes or guide plates are in place, the system uses control software to automatically perform sequential pulse electrical stimulation on each stimulation point (i.e., electrode contact) on the facial electrode array one by one according to the set electrical stimulation sequence, thereby achieving full coverage stimulation exploration of the area to be detected.

[0032] Step 120: Obtain the electromyographic response signal corresponding to each of the stimulation points, and determine the response intensity value that characterizes the response intensity of each of the electromyographic response signals.

[0033] Specifically, electromyographic (EMG) response signals corresponding to each stimulation point are acquired, and response intensity values ​​characterizing the response intensity of each EMG response signal are determined. Here, the response intensity value is used to measure the intensity of the EMG response induced by a specific stimulation point. The response intensity value can be the energy integral, effective value, root mean square (RMS), or other numerical values ​​that can characterize the intensity of signal fluctuations for each EMG response signal. This embodiment of the invention does not specifically limit this value.

[0034] It should be understood that by obtaining the response intensity value, the originally complex waveform signal can be transformed into quantized data that is easy to compare.

[0035] Step 130: Based on the spatial location of each stimulation point in the planar electrode array and the response intensity value, generate a visual distribution map characterizing the neural pathways within the detection area.

[0036] Specifically, in the process of acquiring and analyzing electromyographic response signals, in order to accurately identify and model the target nerve response at multiple points, it is necessary to first perform high-quality preprocessing on the raw electromyographic response signals to effectively suppress various non-physiological interferences and highlight the true electromyographic response signals induced by electrical stimulation. Therefore, in the signal preprocessing stage, this invention employs cascaded IIR (Infinite Impulse Response) high-pass, low-pass, and notch filters for multi-stage optimized filtering. The specific parameters configured for the spectral characteristics of the electromyographic response signal are as follows: A high-pass filter removes low-frequency interference such as poor electrode contact, motion artifacts, and baseline drift to stabilize the signal baseline and preserve the effective components of the neural response; a low-pass filter simultaneously suppresses high-frequency interference such as electromagnetic radiation from peripheral equipment (e.g., radiation signals from high-frequency electrosurgical units) and channel thermal noise, significantly improving the signal-to-noise ratio and ensuring the accuracy of short-time-window response recognition; a notch filter specifically eliminates power frequency (50Hz) and its harmonics (e.g., 100Hz / 150Hz) interference, precisely attenuating power supply noise by setting a narrow-band blocking range (e.g., 49-51Hz) to avoid damaging the neural signal frequency band. This combined filtering structure provides a high-fidelity electromyographic signal foundation for subsequent neural localization through frequency-band coordinated noise reduction.

[0037] Furthermore, after completing the above filtering steps, most of the noise interference has been eliminated from the retained signal. However, during the stimulation process, stimulation artifacts caused by the electrical stimulation current itself still exist. These stimulation artifacts have the following significant characteristics: large signal amplitude; waveform start time almost coincides with the stimulation occurrence time, latency close to 0 ms; and they exhibit a unidirectional spike-shaped waveform. If these artifacts are not removed, they are easily misidentified by the system as real neural responses, thus seriously interfering with subsequent data modeling and analysis. Therefore, this method identifies and removes stimulation artifacts with the above characteristics at this stage to extract effective neural response signals.

[0038] After obtaining the effective electromyographic response signal after denoising and artifact removal, the system performs time-domain feature extraction on the effective electromyographic response signal to calculate the response intensity value.

[0039] After obtaining the response intensity value, a visual distribution map representing the neural pathways within the detection area can be generated based on the spatial location of each stimulation point in the planar electrode array and the response intensity value.

[0040] Since each stimulation point on the planar electrode array has a definite physical spatial location, such as being arranged in an m×n two-dimensional matrix, the system can map the calculated response intensity value of each stimulation point to its physical coordinates, thereby constructing a signal matrix containing spatial information. Subsequently, the signal matrix is ​​transformed into a visual distribution map.

[0041] As an optional implementation, the visualized distribution map can be displayed using a two-color classification. For example, a fixed or manually set response intensity threshold can be set. When the response intensity value of a stimulus point is greater than or equal to the response intensity threshold, the response is determined to be significant and marked with one color, such as red; if the response intensity value of a stimulus point is less than the response intensity threshold, the response is determined to be weak or invalid and marked with another color, such as green. This method is suitable for rapid initial screening.

[0042] As an alternative implementation, the visualized distribution map can also be displayed using a continuous color gradient. The system supports rendering each point using a gradient color map, such as a heatmap color map like Hot or Jet, according to the magnitude of the response intensity value. Low response points are marked with blue tones, and high response points are marked with red or yellow tones, forming a heatmap-like distribution to present the intensity gradient of the response distribution.

[0043] Step 140: Determine the neural pathway based on the visualized distribution map.

[0044] Specifically, after obtaining the visualized distribution map, the neural pathways can be determined based on it. After generating the visualized distribution map, the channels or lines formed by high-response intensity areas, such as red areas or highlighted areas on a heatmap, visually outline the possible pathways of the nerves within the detection area. In other words, the neural pathways reflect the pathway information of the nerves in the detection area. Operators or the system can quickly and accurately determine the specific direction and location of the nerves through direct observation or image recognition algorithms.

[0045] The method provided in this invention involves sequentially stimulating each stimulation point on a planar electrode array covering the area to be detected; acquiring the electromyographic (EMG) response signal corresponding to each stimulation point; determining the response intensity value characterizing the response intensity of each EMG response signal; generating a visual distribution map characterizing the neural pathway within the area to be detected based on the spatial position of each stimulation point in the planar electrode array and the response intensity value; and determining the neural pathway based on the visual distribution map. This invention acquires EMG response signals from each stimulation point in space through multi-point sequential electrical stimulation of a planar electrode array, and generates a visual distribution map by combining the spatial position and quantified response intensity value. This allows for rapid and comprehensive acquisition of neural response characteristics within the area to be detected, transforming the originally discrete single-point response into a continuous neural trajectory image. This improves the operational efficiency and real-time performance of neural localization, avoiding the problems of missed detections or misjudgments caused by traditional blind point-by-point scanning which only provides discrete information on the existence of neural pathways. This makes the neural recognition process less dependent on the operator's subjective experience, ensuring the objectivity and automation of neural localization.

[0046] Based on the above embodiments, step 120, determining the response intensity value characterizing the response intensity of each electromyographic response signal, includes: Step 120-1: Within a preset time window, determine the amplitude of each electromyographic response signal; Step 120-2: Determine the response intensity value based on the amplitude.

[0047] Specifically, within a preset time window, the amplitude of each electromyographic (EMG) response signal is determined, and then the response intensity value is determined based on the amplitude. The system divides the corresponding acquired EMG response signals into a preset time window with the stimulus occurrence time as a reference point, for example, a range of 0-25 ms after the stimulus occurs. This preset time window is sufficient to cover the conduction time required for a typical neuromuscular response, while excluding non-target time periods as much as possible. Within the set preset time window, the system automatically detects the maximum and minimum values ​​of the EMG response signals within that segment and calculates the difference between the maximum and minimum values; this difference is the amplitude.

[0048] Compared to the maximum amplitude or root mean square (RMS) of a single side, amplitude can more comprehensively reflect whether the electromyographic response signal has a clear bidirectional potential swing characteristic. The system uses this amplitude as the response intensity value for subsequent matrix construction and visualization.

[0049] The method provided in this invention effectively filters out random noise outside the time window by calculating the peak amplitude of the electromyographic response signal within a preset time window covering specific nerve conduction, thereby improving the accuracy and reliability of the response intensity value.

[0050] Based on the above embodiments, the method further includes: Step 210, determine the response start point; wherein, the response start point includes a first moment or a second moment; the first moment is the moment when the slope of each of the electromyographic response signals exceeds a preset slope threshold, and the second moment is the moment when the amplitude of each of the electromyographic response signals continuously rises and reaches a preset minimum amplitude, and the duration of the rise in the amplitude of the electromyographic response signal is greater than the time corresponding to the preset minimum duration window. Step 220: Determine the latency period based on the time difference between the response start point and the triggering time of each of the sequential electrical stimuli.

[0051] Specifically, in this embodiment, in addition to amplitude, latency calculation is also introduced. Latency reflects the time delay between the occurrence of electrical stimulation and the first effective response of the electromyographic signal. It can be used to some extent to distinguish between real neural responses and artifacts or secondary responses far from the stimulation point. The key to latency is the accurate determination of the response initiation point.

[0052] First, determine the response start point, which includes a first time point or a second time point. The first time point is the time when the slope of each electromyographic response signal exceeds a preset slope threshold, and the second time point is the time when the amplitude of each electromyographic response signal continuously rises and reaches a preset minimum amplitude, and the duration of the rise in the amplitude of the electromyographic response signal is longer than the time corresponding to the preset minimum duration window.

[0053] To enhance the robustness of response start point determination in complex noise environments, the system also adopts a strategy combining sliding window peak detection and threshold thresholding. This requires that the amplitude of each electromyographic response signal continuously rises to a preset minimum amplitude (e.g., 20 μV), and the duration of this upward trend in the amplitude of the electromyographic response signal must be greater than a preset minimum duration window (e.g., 0.5 ms). The time corresponding to this moment is the second moment.

[0054] After determining the response initiation point, the latency can be determined based on the time difference between the response initiation point and the triggering time of each sequential electrical stimulus. The formula for the latency is as follows: ; in, Indicates the incubation period. Indicates the starting point of the response. This indicates the triggering time for each sequential electrical stimulation.

[0055] The method provided in this invention determines the response start point; wherein the response start point includes a first time moment or a second time moment; the first time moment is the time when the slope of each electromyographic response signal exceeds a preset slope threshold, and the second time moment is the time when the amplitude of each electromyographic response signal continuously rises and reaches a preset minimum amplitude, and the duration of the rise in the amplitude of the electromyographic response signal is greater than the time corresponding to the preset minimum duration window. Based on the time difference between the response start point and the triggering time of each sequential electrical stimulation, the latency is determined. The latency calculated in this way can be used to distinguish between real direct neural responses and secondary responses or interference potentials far from the stimulation point, which greatly improves the reliability of neural discrimination in multi-point models.

[0056] Based on the above embodiments, the method further includes: Step 310: Stimulation points with amplitudes less than a preset amplitude threshold or latency greater than a preset latency threshold are identified as invalid points. Step 320: Remove the invalid points.

[0057] Specifically, after calculating the amplitude and latency, the system needs to screen the effectiveness of the responses at all stimulus points. Specifically, a preset amplitude threshold is set, which can be empirically set as a lower limit for the response. If the amplitude of the electromyographic response at a certain stimulation point If the stimulus site shows a weak signal, it is considered to be in a nerve conduction blind zone or not a neural response at all. Simultaneously, a preset latency threshold is set, which can be empirically set to the maximum reasonable delay. If the latency period of a certain stimulus point This indicates that the response delay is too significant, possibly due to a delayed potential generated by a false stimulus far from the neural region. When any of the above conditions are met, the system identifies the stimulus site as an invalid site and removes it. For example, when constructing an m×n matrix model, the data of invalid sites is set to zero, as shown in the following formula: ; in, Indicates the first The effective electromyographic response signal corresponding to each stimulation point Indicates the first The original electromyographic response signal corresponding to each stimulation point.

[0058] The method provided in this invention identifies stimulus points with amplitudes less than a preset amplitude threshold or latency greater than a preset latency threshold as invalid points. By eliminating invalid points, invalid data such as artifacts, remote delayed responses, and weak non-target responses can be accurately removed, effectively purifying the data source of the mapping matrix and preventing invalid points from generating misleading artifacts in the visualization distribution map.

[0059] Based on the above embodiments, step 130 further includes the following prior steps: Step 1301: Based on the spatial position of each stimulation point in the planar electrode array, determine the adjacent points of each stimulation point; Step 1302: Determine the similarity of response intensity values ​​based on the response intensity values ​​of each stimulus point and the response intensity values ​​of adjacent points; Step 1303: If the similarity of the response intensity value is higher than a preset similarity threshold, reduce the stimulation current of the sequential electrical stimulation.

[0060] Specifically, in this embodiment, the system introduces an adaptive adjustment mechanism to obtain a finer spatial resolution. Since the planar electrode array contains an m×n two-dimensional arrangement, the adjacent points of each stimulation point can be determined based on the spatial position of each stimulation point in the planar electrode array. The system can traverse each stimulation point in the signal matrix and find the adjacent points of the stimulation point in the up-down, left-right, and other directions.

[0061] Subsequently, the similarity of response intensity values ​​is determined based on the response intensity values ​​of each stimulation point and those of adjacent points. This involves comparing the response intensity values ​​(e.g., amplitude) of the stimulation point with those of adjacent points and calculating the similarity, for example, calculating the percentage difference between the response intensity values ​​of each stimulation point and those of adjacent points. If the amplitude difference between adjacent points is found to be extremely small, meaning the similarity of response intensity values ​​is higher than a preset similarity threshold (e.g., the difference is less than 10%), it indicates that the current stimulation current is too large, causing a large area of ​​adjacent regions to be over-excited, resulting in a saturated approximate response. The preset similarity threshold can be set to 10%. In this case, the system determines that the current current intensity is slightly higher than the optimal neural stimulation threshold, automatically reduces the stimulation current by one step, and then performs sequential electrical stimulation again, acquiring the electromyographic response signals corresponding to each stimulation point and determining the response intensity values ​​characterizing each electromyographic response signal.

[0062] The method provided in this invention introduces a similarity judgment of response intensity values ​​of adjacent points and an adaptive negative feedback adjustment mechanism for current, which can automatically find the best electrical stimulation parameters that are closest to the nerve excitation threshold. This avoids the "charge flooding" effect caused by excessive stimulation current, significantly reduces the diffusion range of excitation, and thus greatly improves the spatial resolution and localization precision of the nerve path in the visualization distribution map.

[0063] Based on the above embodiments, the method further includes: If all the response intensity values ​​are lower than the preset response threshold, the stimulation current of the sequential electrical stimulation is increased.

[0064] Specifically, when all response intensity values ​​are lower than the preset response threshold, the stimulation current of sequential electrical stimulation is increased.

[0065] The system uses a planar array of stimulation-recording electrodes, with the electrode points arranged in a predetermined pattern to form an m×n two-dimensional array. After completing one round of sequential electrical stimulation and signal acquisition at the m×n points, the system organizes the final effective electromyographic response intensity values ​​(specifically amplitudes in this embodiment) retained at each point into a matrix. ,in This forms a signal matrix that directly reflects the response intensity distribution of the region to be detected. Subsequently, the system will process the signal matrix. A global evaluation is performed on all response intensity values ​​within the range.

[0066] Here, the preset response threshold T refers to a minimum intensity standard used to determine whether an electromyographic response is effective. The preset response threshold is typically set based on empirical data or baseline noise levels; for example, it might be set to 20 μV. Its purpose is to distinguish genuine electromyographic signals induced by neural excitation from background noise or weak, nonspecific electrical activity.

[0067] During the evaluation process, if the system detects that the response intensity values ​​(e.g., amplitudes) at all stimulus points are lower than the preset response threshold, that is, if the system detects that all elements in signal matrix A are below the threshold, then the system has detected all elements below the threshold. If all values ​​are below the preset response threshold, the system will determine that the current stimulation current intensity is insufficient to effectively activate the target nerve. This may occur due to individual differences leading to a higher nerve excitation threshold, or due to imperfect electrode-tissue contact. In this case, the system will automatically increase the stimulation current, for example, by increasing the current value by a preset step (e.g., 0.1 mA), and then restart the entire sequential stimulation and signal acquisition process that began in step 110. This process can be repeated until at least one stimulation point's response intensity value exceeds the preset response threshold, i.e., until at least one response intensity value in signal matrix A is exceeded. If the response exceeds the preset threshold, it indicates that an effective stimulation level has been reached.

[0068] The method provided in this invention constructs an adaptive closed-loop adjustment system for stimulation intensity by automatically increasing the stimulation current when the response intensity values ​​at all stimulation points are lower than a preset response threshold. This mechanism effectively addresses the problem of global no response or weak response caused by an excessively low initial current setting, avoids neural localization failure due to insufficient stimulation, and ensures that the system can automatically search for and achieve an effective stimulation level, thereby greatly improving the robustness of neural localization and its universality of application in different individuals or tissues.

[0069] Based on the above embodiments, step 130 includes: The spatial locations corresponding to the stimulation points whose response intensity values ​​are greater than or equal to the preset display threshold are marked with a first color, and the spatial locations corresponding to the stimulation points whose response intensity values ​​are less than the preset display threshold are marked with a second color, thus obtaining the visualization distribution map; or, Based on the magnitude of the response intensity value, the spatial location corresponding to each stimulus point is mapped to a continuous color scale to obtain the visualization distribution map.

[0070] Specifically, firstly, the system performs large-scale, multi-point sequential stimulation and acquisition using a planar electrode array, and then uses a cascaded IIR filter to remove high-pass, low-pass, and power frequency noise from the raw signal. Subsequently, analysis based on characteristics such as short latency, narrow pulse, high amplitude, and peak slope effectively identifies the true electromyographic response signal and eliminates stimulation artifacts and interference. After filtering out invalid response points using dual thresholds for amplitude and latency, the system organizes the final effective electromyographic response intensity value (i.e., amplitude, in μV or mV) for each stimulation point into an m×n signal matrix A. This two-dimensional matrix directly corresponds to the geometric relationship of the electrode layout in the actual operating area, ensuring a one-to-one correspondence between the interface display and the actual electrode positions. Based on the signal matrix A, the system provides at least two main visualization mapping schemes, displaying the results in real-time on the interface in m×n matrix form.

[0071] As an optional implementation, the system uses a two-color classification display.

[0072] In this dual-color classification display mode, a preset display threshold needs to be set. This threshold is used to distinguish between "significant responses" and "non-significant responses" at the visualization level and can be adjusted by the user based on experience or real-time feedback. The system iterates through each stimulus point in the m×n matrix and compares the response intensity value of each stimulus point with the preset display threshold.

[0073] When the response intensity value of a certain stimulus point is greater than or equal to the preset display threshold, the system will display each element in the signal matrix A. Compare with the preset display threshold. Since each element in the signal matrix A is a response intensity value, therefore, when the response intensity value... Greater than or equal to the preset display threshold ( When a stimulus is applied, the system determines that the point is a valid neural response point and marks it with the first color at the corresponding spatial location on the visualization distribution map. The first color here is typically a high-alertness, high-contrast color, such as red, to prominently indicate the area that the neural pathway may traverse.

[0074] When the response intensity value of a certain stimulus point is less than the preset display threshold, the system will display each element in the signal matrix A. Compare with the preset display threshold. When the response intensity value Greater than or equal to the preset display threshold ( When a point is identified as invalid or weakly responding, the system determines it to be a secondary color and marks it as such. This secondary color is typically a low-saturation color or a color that contrasts sharply with the primary color, such as green or blue, representing non-neural or safe regions. In this way, a well-defined and easily interpretable binary neural localization map can be quickly generated.

[0075] As an alternative implementation, the system uses a continuous color gradation display, i.e., generates a heat map.

[0076] In this continuous color gradation display mode, the system normalizes the response intensity values ​​of all valid points (for example, mapping them to the range of 0-1), and then assigns a corresponding color in the continuous color gradation to each point in space based on the normalized value.

[0077] The continuous color scale (also known as a color map) here refers to a smoothly transitioning sequence of colors, such as a gradient from cool tones (like dark blue) to warm tones (like bright yellow and red). The system maps the lowest response intensity value to the starting color of the cool tone, the highest response intensity value to the ending color of the warm tone, and intermediate intensity values ​​to transitional colors in the color scale proportionally. In this way, the areas with the strongest response (the core of the neural trunk) are highlighted with the brightest warm color (like red), while areas with gradually decreasing response are displayed as a color gradient from yellow to green to blue, visually demonstrating the spatial distribution of response intensity.

[0078] Understandably, this method combines quantitative calculation of electromyographic signals at stimulation points with analysis of the spatial relationships between these points, enabling rapid determination of nerve pathway locations. Compared to traditional blind methods, this invention significantly improves localization efficiency and accuracy, reduces the incidence of nerve damage due to misjudgment, and makes the nerve identification process less reliant on the operator's subjective experience, thus possessing promising clinical application prospects and practical value.

[0079] The method provided in this invention achieves intuitive visualization of the spatial distribution of neural responses by mapping quantified response intensity values ​​to a two-color classification map or a continuous color-gradient heatmap. This method not only transforms abstract numerical matrices into easily recognizable graphical information but also caters to both rapid interpretation and detailed analysis by providing two selectable display modes: the two-color classification mode can quickly outline the general contours of the nerves with high contrast, suitable for preliminary screening; the continuous color-gradient mode can delicately display the gradient changes in response intensity, helping to accurately determine the core location and boundaries of the neural trunk. This flexible visualization strategy greatly enhances the intuitiveness and information richness of the neural localization results, enabling operators to more efficiently and accurately understand the neural pathways within the detection area.

[0080] Based on any of the above embodiments Figure 2 This is the second flowchart illustrating the neural pathway determination method provided by the present invention, as shown below. Figure 2 As shown, the process begins with a planar electrode array. The system first sequentially stimulates and acquires signals from each point on the m×n array to obtain raw electromyographic response signals. Subsequently, the system performs signal denoising preprocessing on the acquired signals, effectively suppressing various interferences through filtering and other methods. After obtaining a clean signal, the system performs core quantitative calculations based on time-domain features to accurately extract key parameters such as response amplitude and latency for each stimulation point. Then, based on these quantitative results, the system filters valid points, removing invalid data points that do not meet preset thresholds (such as excessively low amplitude or excessively long latency). Next, the system uses all the data from the filtered valid points to construct a signal matrix, which spatially corresponds one-to-one with the electrode array. Finally, this signal matrix is ​​transformed into an intuitive visual distribution map, allowing the operator to determine the neural pathway. The most critical innovation of this process lies in its closed-loop characteristic: the system analyzes the final visualization results or signal matrix and performs adaptive adjustment of stimulation parameters. Based on the strength and distribution of the response, it automatically optimizes parameters such as stimulation current, and then restarts the entire process with new parameters, thus iterating in a loop until the most accurate and clear neural localization results are obtained.

[0081] Based on any of the above embodiments Figure 3 This is the third flowchart of the neural pathway determination method provided by the present invention, as shown below. Figure 3 As shown, the process begins with a preprocessing step, where the raw electromyographic (EMG) response signals are preprocessed to filter out background noise. After preprocessing, the system performs quantitative calculations of stimulation points based on time-domain features, accurately extracting key parameters such as amplitude and latency from the signal. Next, the process moves to point selection and signal matrix construction. In this stage, invalid or unsuitable points are eliminated, and data from valid points are used to construct a signal matrix reflecting spatial relationships. Following this, the process enters a crucial judgment stage: whether a neural pathway can be determined. If the judgment is negative, it indicates that the current data is insufficient to clearly delineate the neural pathway. The system will then adjust parameters and re-stimulate, returning to the EMG response signal preprocessing step, forming a closed-loop feedback regulation to optimize the stimulation effect. Conversely, if the judgment is positive, it means a valid signal matrix has been successfully obtained, and the process continues, ultimately entering the result visualization stage, where the determined neural pathway is presented to the user graphically.

[0082] Figure 4 This is a schematic diagram of the electromyographic response signal preprocessing and artifact recognition process provided by the present invention, as shown below. Figure 4As shown, the process first initializes the algorithm structure to configure relevant parameters for subsequent signal processing tasks. Then, the system acquires electrical stimulation signals, obtaining the raw electromyographic response signals corresponding to specific stimulation points. The acquired signals immediately undergo a series of noise reduction operations, including high-pass, low-pass, and power frequency removal, to effectively filter out background noise and power supply interference. After noise reduction, the process enters a crucial artifact identification and judgment stage, determining whether the signal is an electrical stimulation artifact. If the judgment result is yes based on signal characteristics (such as extremely short latency, unidirectional spikes in the waveform), it indicates that the signal is an invalid stimulation artifact, and the system will discard this segment of signal. Conversely, if the judgment result is no, it proves that the signal is the real electromyographic response to be analyzed, and the system will retain this segment of signal for subsequent quantitative feature calculation and analysis.

[0083] The neural pathway determination device provided by the present invention is described below. The neural pathway determination device described below and the neural pathway determination method described above can be referred to in correspondence.

[0084] Based on any of the above embodiments, the present invention provides a neural pathway determination device. Figure 5 This is a schematic diagram of the neural pathway determination device provided by the present invention, as shown below. Figure 5 As shown, the device includes: The electrical stimulation module 510 is used to sequentially electrically stimulate each stimulation point on the planar electrode array covering the area to be detected. The acquisition module 520 is used to acquire the electromyographic response signal corresponding to each of the stimulation points and determine the response intensity value characterizing the response intensity of each of the electromyographic response signals. The generation module 530 is used to generate a visual distribution map characterizing the neural pathways in the area to be detected based on the spatial position of each of the stimulation points in the planar electrode array and the response intensity value. The determination module 540 is used to determine the neural pathway based on the visualized distribution map.

[0085] The device provided in this invention sequentially electrically stimulates each stimulation point on a planar electrode array covering the area to be detected; acquires the electromyographic response signal corresponding to each stimulation point, and determines the response intensity value characterizing the response intensity of each electromyographic response signal; based on the spatial position of each stimulation point in the planar electrode array and the response intensity value, generates a visual distribution map characterizing the neural pathway within the area to be detected; and determines the neural pathway based on the visual distribution map. This invention acquires the electromyographic response signal of each stimulation point in space based on multi-point sequential electrical stimulation of a planar electrode array, and generates a visual distribution map by combining the spatial position and quantified response intensity value. This allows for rapid and comprehensive acquisition of neural response characteristics within the area to be detected, transforming the originally discrete single-point response into a continuous neural trajectory image, improving the operational efficiency and real-time performance of neural localization, and avoiding the problems of missed detections or misjudgments caused by traditional blind point-by-point scanning which only provides discrete information on existence. This makes the neural recognition process less dependent on the operator's subjective experience, ensuring the objectivity and automation of neural localization.

[0086] Based on any of the above embodiments, the acquisition module 520 is specifically used for: Within a preset time window, the amplitude of each electromyographic response signal is determined; The response intensity value is determined based on the amplitude.

[0087] Based on any of the above embodiments, a module for determining the incubation period is further included, wherein the module for determining the incubation period is specifically used for: Determine the response start point; wherein the response start point includes a first time moment or a second time moment; the first time moment is the time when the slope of each electromyographic response signal exceeds a preset slope threshold, and the second time moment is the time when the amplitude of each electromyographic response signal continuously rises and reaches a preset minimum amplitude, and the duration of the rise in the amplitude of the electromyographic response signal is greater than the time corresponding to a preset minimum duration window. The latency period is determined based on the time difference between the response start point and the triggering time of each of the sequential electrical stimuli.

[0088] Based on any of the above embodiments, a rejection module is further included, wherein the rejection module is specifically used for: Stimulation points whose amplitude is less than a preset amplitude threshold or whose latency is greater than a preset latency threshold are identified as invalid points. The invalid points are removed.

[0089] Based on any of the above embodiments, a stimulation reduction module is further included, wherein the stimulation reduction module is specifically used for: Based on the spatial position of each stimulation point in the planar electrode array, the adjacent points of each stimulation point are determined; The similarity of response intensity values ​​is determined based on the response intensity values ​​of each stimulus point and the response intensity values ​​of the adjacent points. If the similarity of the response intensity values ​​is higher than a preset similarity threshold, the stimulation current of the sequential electrical stimulation is reduced.

[0090] Based on any of the above embodiments, a stimulation module for increasing size is further included, wherein the stimulation module for increasing size is specifically used for: If all the response intensity values ​​are lower than the preset response threshold, the stimulation current of the sequential electrical stimulation is increased.

[0091] Based on any of the above embodiments, the generation module 530 is specifically used for: The spatial locations corresponding to the stimulation points whose response intensity values ​​are greater than or equal to the preset display threshold are marked with a first color, and the spatial locations corresponding to the stimulation points whose response intensity values ​​are less than the preset display threshold are marked with a second color, thus obtaining the visualization distribution map; or, Based on the magnitude of the response intensity value, the spatial location corresponding to each stimulus point is mapped to a continuous color scale to obtain the visualization distribution map.

[0092] Figure 6 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 6 As shown, the electronic device may include a processor 610, a communications interface 620, a memory 630, and a communication bus 640, wherein the processor 610, the communications interface 620, and the memory 630 communicate with each other via the communication bus 640. The processor 610 can call logical instructions in the memory 630 to execute a neural pathway determination method, which includes: sequentially stimulating each stimulation point on a planar electrode array covering the region to be detected; acquiring electromyographic response signals corresponding to each stimulation point, and determining response intensity values ​​characterizing the response intensity of each electromyographic response signal; generating a visual distribution map characterizing the neural pathway within the region to be detected based on the spatial location of each stimulation point in the planar electrode array and the response intensity values; and determining the neural pathway based on the visual distribution map.

[0093] Furthermore, the logical instructions in the aforementioned memory 630 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0094] On the other hand, the present invention also provides a computer program product, the computer program product including a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the neural pathway determination method provided by the above methods. The method includes: sequentially stimulating each stimulation point on a planar electrode array covering a region to be detected; acquiring electromyographic response signals corresponding to each stimulation point, and determining a response intensity value characterizing the response intensity of each electromyographic response signal; generating a visual distribution map characterizing the neural pathway in the region to be detected based on the spatial position of each stimulation point in the planar electrode array and the response intensity value; and determining the neural pathway based on the visual distribution map.

[0095] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements a neural pathway determination method provided by the methods described above. The method includes: sequentially stimulating stimulation points on a planar electrode array covering a region to be detected; acquiring electromyographic response signals corresponding to each stimulation point, and determining response intensity values ​​characterizing the response intensity of each electromyographic response signal; generating a visual distribution map characterizing the neural pathway within the region to be detected based on the spatial location of each stimulation point in the planar electrode array and the response intensity values; and determining the neural pathway based on the visual distribution map.

[0096] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0097] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for determining neural pathways, characterized in that, include: Sequential electrical stimulation is applied to each stimulation point on the planar electrode array covering the area to be detected; Acquire the electromyographic response signals corresponding to each of the stimulation points, and determine the response intensity value characterizing the response intensity of each of the electromyographic response signals; the response intensity value is used to measure the intensity of the electromyographic response induced by a specific stimulation point, and the response intensity value includes the energy integral, effective value and root mean square of each electromyographic response signal; Based on the spatial location of each stimulation point in the planar electrode array and the response intensity value, a visual distribution map characterizing the neural pathways in the area to be detected is generated. Based on the visualized distribution map, the neural pathway is determined; The determination of the response intensity value, which characterizes the response intensity of each of the electromyographic response signals, includes: Within a preset time window, the amplitude of each electromyographic response signal is determined; The response intensity value is determined based on the amplitude.

2. The method for determining neural pathways according to claim 1, characterized in that, The method further includes: Determine the response start point; wherein the response start point includes a first time moment or a second time moment; the first time moment is the time when the slope of each electromyographic response signal exceeds a preset slope threshold, and the second time moment is the time when the amplitude of each electromyographic response signal continuously rises and reaches a preset minimum amplitude, and the duration of the rise in the amplitude of the electromyographic response signal is greater than the time corresponding to a preset minimum duration window. The latency period is determined based on the time difference between the response start point and the triggering time of each of the sequential electrical stimuli.

3. The method for determining neural pathways according to claim 2, characterized in that, The method further includes: Stimulation points whose amplitude is less than a preset amplitude threshold or whose latency is greater than a preset latency threshold are identified as invalid points. The invalid points are then removed.

4. The method for determining neural pathways according to any one of claims 1 to 3, characterized in that, The step of generating a visual distribution map characterizing the neural pathways within the detection area based on the spatial location of each stimulation point in the planar electrode array and the response intensity value, further includes: Based on the spatial position of each stimulation point in the planar electrode array, the adjacent points of each stimulation point are determined; The similarity of response intensity values ​​is determined based on the response intensity values ​​of each stimulus point and the response intensity values ​​of the adjacent points. If the similarity of the response intensity values ​​is higher than a preset similarity threshold, the stimulation current of the sequential electrical stimulation is reduced.

5. The method for determining neural pathways according to claim 4, characterized in that, The method further includes: If all the response intensity values ​​are lower than the preset response threshold, the stimulation current of the sequential electrical stimulation is increased.

6. The method for determining neural pathways according to any one of claims 1 to 3, characterized in that, The step of generating a visual distribution map characterizing the neural pathways within the detection area based on the spatial location of each stimulation point in the planar electrode array and the response intensity value includes: The spatial locations corresponding to the stimulation points whose response intensity values ​​are greater than or equal to the preset display threshold are marked with a first color, and the spatial locations corresponding to the stimulation points whose response intensity values ​​are less than the preset display threshold are marked with a second color, thus obtaining the visualization distribution map; or, Based on the magnitude of the response intensity value, the spatial location corresponding to each stimulus point is mapped to a continuous color scale to obtain the visualization distribution map.

7. A device for determining neural pathways, characterized in that, include: An electrical stimulation module is used to sequentially electrically stimulate each stimulation point on a planar electrode array covering the area to be detected. The acquisition module is used to acquire the electromyographic response signal corresponding to each of the stimulation points and determine the response intensity value that characterizes the response intensity of each of the electromyographic response signals. The response intensity value is used to measure the intensity of the electromyographic response induced at a specific stimulation point. The response intensity value includes the energy integral, effective value, and root mean square of each electromyographic response signal. The generation module is used to generate a visual distribution map characterizing the neural pathways in the area to be detected based on the spatial position of each of the stimulation points in the planar electrode array and the response intensity value. The determination module is used to determine the neural pathway based on the visualized distribution map; The acquisition module is specifically used for: Within a preset time window, the amplitude of each electromyographic response signal is determined; The response intensity value is determined based on the amplitude.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the neural pathway determination method as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the neural pathway determination method as described in any one of claims 1 to 6.

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