Method, apparatus and electronic device for neurovascular coupling analysis of peripheral nerves

Abstract

The present invention discloses a method, apparatus, and electronic device for neurovascular coupling analysis of peripheral nerves. The method includes: receiving the exercise myo-oxygen signal of a target detection muscle in a motion state collected by a blood oxygen signal acquisition device; determining the myo-oxygen signal time response characteristic of the exercise myo-oxygen signal according to the state time information of the motion state; and determining the neurovascular coupling analysis result of the target peripheral nerve based on the myo-oxygen signal time response characteristic. The method provided in this embodiment realizes the evaluation of the neurovascular coupling function of peripheral nerves by using the analysis of the time and space response characteristics of myo-oxygen signals, and simplifies the difficulty of evaluating the neurovascular coupling function of peripheral nerves.

Description

Technical Field

[0001] The present invention relates to the technical field of neurovascular coupling detection, and particularly to a neurovascular coupling analysis method, device and electronic device for peripheral nerves. Background Art

[0002] The neurovascular coupling function reflects the level of the function of the nerve supply system and is very necessary for maintaining continuous nerve activity.

[0003] Currently, the detection of neurovascular coupling function is mostly used for evaluating the neurovascular coupling function of cranial nerves. For example, electroencephalogram signal testing, transcranial Doppler testing, etc. are adopted. However, the diameter of peripheral nerves is less than 1 mm and they are embedded in peripheral tissues, resulting in difficult localization of peripheral nerves and making it impossible to evaluate the neurovascular coupling function of peripheral nerves by the methods used for evaluating the neurovascular coupling function of cranial nerves. Summary of the Invention

[0004] The present invention provides a neurovascular coupling analysis method, device and electronic device for peripheral nerves to solve the technical problem that it is difficult to evaluate the neurovascular coupling function of peripheral nerves due to the difficult localization of peripheral nerves.

[0005] According to one aspect of the present invention, there is provided a neurovascular coupling analysis method for peripheral nerves, the method comprising:

[0006] Receiving the exercise myo-oxygen signal of a target detection muscle in a motion state collected by a blood oxygen signal acquisition device;

[0007] Determining the myo-oxygen signal time response characteristic of the exercise myo-oxygen signal according to the state time information of the motion state;

[0008] Determining the neurovascular coupling analysis result of the target peripheral nerve based on the myo-oxygen signal time response characteristic.

[0009] According to another aspect of the present invention, there is provided a neurovascular coupling analysis device for peripheral nerves, characterized by comprising:

[0010] A myo-oxygen signal receiving module, configured to receive the exercise myo-oxygen signal of a target detection muscle in a motion state collected by a blood oxygen signal acquisition device;

[0011] A myo-oxygen response characteristic module, configured to determine the myo-oxygen signal time response characteristic of the exercise myo-oxygen signal according to the state time information of the motion state;

[0012] A neurovascular coupling analysis module, configured to determine the neurovascular coupling analysis result of the target peripheral nerve based on the myo-oxygen signal time response characteristic.

[0013] According to another aspect of the present invention, there is provided an electronic device, which includes:

[0014] at least one processor; and

[0015] a memory communicatively connected to the at least one processor; wherein,

[0016] the memory stores a computer program executable by the at least one processor, and when the computer program is executed by the at least one processor, the at least one processor is enabled to execute the method for analyzing neurovascular coupling of peripheral nerves according to any embodiment of the present invention.

[0017] According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement the method for analyzing neurovascular coupling of peripheral nerves according to any embodiment of the present invention when executed.

[0018] The technical solution of the embodiment of the present invention includes receiving the exercise myo-oxygen signal of the target detection muscle in the exercise state collected by the blood oxygen signal acquisition device; determining the myo-oxygen signal time response characteristic parameters of the exercise myo-oxygen signal according to the state time information of the exercise state; and determining the neurovascular coupling analysis result of the target peripheral nerve based on the myo-oxygen signal time response characteristic parameters. The method provided in this embodiment realizes the evaluation of the neurovascular coupling function of peripheral nerves by using the analysis of the time and space response characteristics of myo-oxygen signals, and simplifies the difficulty of evaluating the neurovascular coupling function of peripheral nerves.

[0019] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

[0021] Figure 1 is a flowchart of a method for analyzing neurovascular coupling of peripheral nerves provided in Embodiment 1 of the present invention;

[0022] Figure 2 is a flowchart of a method for analyzing neurovascular coupling of peripheral nerves provided in Embodiment 2 of the present invention;

[0023] Figure 3 It is a schematic diagram of the spatial arrangement of the multi-channel electrodes and optrodes provided in Embodiment 3 of the present invention;

[0024] Figure 4 It is a flowchart of the sEMG and NIRS signal feature extraction and image reconstruction provided in Embodiment 3 of the present invention;

[0025] Figure 5 It is a schematic diagram of calculating the time response characteristics of the muscle oxygen signal within the ROI region provided in Embodiment 3 of the present invention;

[0026] Figure 6 It is a flowchart of the method for evaluating the motor neuromuscular vascular coupling function provided in Embodiment 3 of the present invention;

[0027] Figure 7 It is a schematic structural diagram of a neurovascular coupling analysis device for peripheral nerves provided in Embodiment 4 of the present invention;

[0028] Figure 8 It is a schematic structural diagram of an electronic device provided in Embodiment 5 of the present invention. Detailed implementation manners

[0029] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0030] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order different from those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily need to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0031] Embodiment 1

[0032] Figure 1This is a flowchart of a method for analyzing neurovascular coupling of peripheral nerves provided by the first embodiment of the present invention. The system can be used for analyzing neurovascular coupling. The method can be performed by a neurovascular coupling analysis device for peripheral nerves. The neurovascular coupling analysis device for peripheral nerves can be implemented in the form of hardware and / or software. The neurovascular coupling analysis device for peripheral nerves can be configured in a detection device. Figure 1 As shown, the method includes:

[0033] S110, receiving the exercise muscle oxygen signal of the target detection muscle in the exercise state collected by the blood oxygen signal collection device.

[0034] Peripheral nerves are embedded in peripheral tissues and are less than 1 mm in diameter. It is difficult to non-invasively locate the position of peripheral nerves when evaluating the peripheral neurovascular coupling function. When the motor nerves in the peripheral nerves are excited, they can control muscle contraction. Considering the nerves and muscles as a whole, the functional coupling of motor nerves, muscles and blood vessels can be indirectly evaluated by detecting the electrophysiological activity and blood oxygen status of the muscles controlled by the motor nerves.

[0035] In this embodiment, the muscles corresponding to the peripheral nerves can be used as target detection muscles. The detected object performs a motion task to trigger the movement of the target muscles. The exercise muscle oxygen signal of the target detection muscle in the motion state is collected by the blood oxygen signal acquisition device, and the collected exercise muscle oxygen signal is sent to the neurovascular coupling analysis device for peripheral nerves. The neurovascular coupling analysis device for peripheral nerves evaluates the muscle-vascular coupling function based on the received exercise muscle oxygen signal, thereby realizing the muscle-vascular coupling function evaluation.

[0036] Muscle oxygen signal generally refers to a signal that can indicate the blood oxygen saturation in muscle capillaries, that is, the percentage of hemoglobin volume bound by oxygen in the blood to the total amount of hemoglobin that can be bound in muscle capillaries. Blood oxygen signal can be understood as a signal that indicates blood oxygen saturation, that is, the percentage of hemoglobin volume bound by oxygen in the blood to the total amount of hemoglobin that can be bound. When muscles move, the blood oxygen saturation in muscle capillaries changes accordingly.

[0037] The muscle oxygen signal during muscle movement can be collected by a blood oxygen signal collection device, and the collected muscle oxygen signal can be sent to a neurovascular coupling analysis device for peripheral nerves, wherein the blood oxygen signal collection device can be attached to the surface of the target detection muscle. Exemplarily, the blood oxygen signal collection device can be a smart bracelet, a photoelectric electrode, etc., and the collected muscle oxygen signal can be transmitted to the neurovascular coupling analysis device via Bluetooth, wireless or other transmission methods.

[0038] In one implementation, the blood oxygen signal acquisition device is implemented by means of an opto - electrical electrode. The opto - electrical electrode may include a light source and a detector. When acquiring the blood oxygen signal, the light source is controlled to emit dual - wavelength near - infrared light, and the detector is used to detect the emitted light signal. Based on the incident light signal and the emitted light signal, the absorption photometric value of the near - infrared light by the human body surface tissue can be determined, thereby determining the blood oxygen saturation.

[0039] S120. Determine the myogenic oxygen signal time - response characteristic parameter of the myogenic oxygen signal according to the state time information of the motion state.

[0040] After receiving the myogenic oxygen signal, the received myogenic oxygen signal is processed, and the myogenic oxygen signal time - response characteristic parameter of the myogenic oxygen signal is determined in combination with the state time information of the motion state, thereby determining the evaluation result of the coupling function of the muscle blood vessels.

[0041] Optionally, the myogenic oxygen signal time - response characteristic parameter may be a related parameter such as blood oxygen saturation. A myogenic oxygen signal time - response characteristic curve showing the change of the myogenic oxygen signal time - response characteristic parameter over time can be plotted. Based on the myogenic oxygen signal time - response characteristic curve, the muscle blood vessel coupling analysis result of the peripheral nerve of the target - detected muscle is determined. For example, when the muscle is in motion, the obtained myogenic oxygen signal time - response characteristic curve is compared with the standard myogenic oxygen signal time - response characteristic curve. The greater the difference between the two curves, the worse the coupling of the muscle blood vessels.

[0042] Optionally, determining the myogenic oxygen signal time - response characteristic parameter of the myogenic oxygen signal according to the state time information of the motion state includes: determining the target hemoglobin concentration change information associated with the state time information of the myogenic oxygen signal; determining the response recovery parameter of the myogenic oxygen signal based on the target hemoglobin concentration change information, and using the response recovery parameter as the myogenic oxygen signal time - response characteristic parameter.

[0043] Among them, the target hemoglobin concentration change information is at least one of the oxygenated hemoglobin concentration change information, deoxygenated hemoglobin concentration change information, and total hemoglobin concentration change information. At least one of the above - mentioned concentration change information can be selected as the target hemoglobin concentration change information according to actual needs.

[0044] The time response characteristic parameters of the muscle oxygen signal can characterize the change of the muscle oxygen signal with time during exercise. First, the corresponding target hemoglobin concentration can be determined based on the exercise muscle oxygen signal, and the response recovery parameter of the exercise muscle oxygen signal can be determined according to the change information of the target hemoglobin concentration. It can be understood that the exercise muscle oxygen signal is a signal that changes with time, so the target hemoglobin concentration determined based on the exercise muscle oxygen signal also changes with time. The change information of the target hemoglobin concentration within the exercise time period can be directly used as the change information of the target hemoglobin concentration associated with the state time information, and the response recovery parameter of the exercise muscle oxygen signal can be determined based on the change information of the target hemoglobin concentration as the time response characteristic parameter of the muscle oxygen signal.

[0045] In one implementation, near-infrared spectroscopy (NIRS) can be used to irradiate tissue with dual-wavelength near-infrared light. Due to the different absorption spectra of oxygenated hemoglobin and deoxygenated hemoglobin in the tissue, the detected intensity of the outgoing light is different. The detected light intensity signal can be used as the exercise muscle oxygen signal, and the concentration changes of oxygenated hemoglobin, deoxygenated hemoglobin, and total hemoglobin in the tissue can be analyzed based on the intensity of the outgoing light. Specifically, the optical density value can be obtained based on the incident light intensity value and the outgoing light intensity value, where the optical density value = log(incident light intensity value / outgoing light intensity value). According to the Beer-Lambert law, the relationship between the optical density value and the optical path length can be obtained. Among them, "incident optical density value 1 = (molar extinction coefficient 1 of oxygenated hemoglobin × change value of oxygenated hemoglobin concentration + molar extinction coefficient 1 of deoxygenated hemoglobin × change value of deoxygenated hemoglobin concentration) × optical path length × differential path factor", "incident optical density value 2 = (molar extinction coefficient 2 of oxygenated hemoglobin × change value of oxygenated hemoglobin concentration + molar extinction coefficient 2 of deoxygenated hemoglobin × change value of deoxygenated hemoglobin concentration) × optical path length × differential path factor", "total change value of hemoglobin concentration = change value of deoxygenated hemoglobin concentration + change value of oxygenated hemoglobin concentration".

[0046] The parameter value of the response recovery parameter can characterize the exercise response state and exercise recovery state of the muscle oxygen signal during exercise, and further characterize the exercise response state and exercise recovery state of the muscle blood vessels during exercise. Optionally, the coupling state of the muscle blood vessels can be determined based on the parameter value of the response recovery parameter. Exemplarily, when the parameter value of the response recovery parameter meets the set value, it can be determined that the coupling of the muscle blood vessels, i.e., the neurovascular coupling, meets the corresponding requirements.

[0047] In one implementation, the response recovery parameter can be represented by response time, response amplitude, recovery time, and recovery amplitude. When the response time is shorter and the response amplitude is higher, it can be determined that the coupling of the muscle blood vessels is better; when the recovery time is longer and the recovery amplitude is larger, it can be determined that the coupling of the muscle blood vessels is poorer. On the contrary, when the recovery time is shorter and the recovery amplitude is smaller, the coupling of the muscle blood vessels is stronger.

[0048] In one implementation, the state time information can be determined based on the movement task issuance time. A task can be issued to enable the detection object to control the movement of the target muscle. To simplify the detection complexity, the state time information can be directly determined based on the task issuance time and the task end time.

[0049] In one implementation, the state time information can also be determined by measuring the electromyography (EMG) signal of the target detected muscle. Based on this, before determining the myogenic oxygen signal time response characteristic parameters of the myogenic oxygen signal according to the state time information of the movement state, it further includes: receiving the movement EMG signal of the target detected muscle in the movement state collected by the surface EMG acquisition device, where the surface EMG acquisition device includes a single-channel electrode, and the single-channel electrode is attached to the surface of the target muscle; determining the state time information of the movement state according to the movement EMG signal.

[0050] It can be understood that when the muscle moves, the EMG signal will change. Therefore, the state time information of the movement state can be determined by the movement EMG signal of the target detected muscle. Exemplarily, the EMG acquisition device can be attached to the surface of the target detected muscle to collect the EMG signal of the target detected muscle as the movement EMG signal, and the collected movement EMG signal is sent to the neurovascular coupling analysis device for the peripheral nerve. The neurovascular coupling analysis device for the peripheral nerve receives the movement EMG signal collected by the surface EMG device and determines the state time information of the movement state according to the movement EMG signal. For example, the movement EMG signal can be represented as a set of data that changes over time, can be processed into a time function representing the movement state with the EMG signal, and the relationship between the muscle movement state and time can be determined. Based on the movement EMG signal, the movement start time and the movement end time are determined. Combining with the myogenic oxygen signal, the myogenic oxygen signal value corresponding to each time point can be obtained, and the time curve of the myogenic oxygen signal can be obtained according to each time point and the myogenic oxygen signal value corresponding to each time point.

[0051] After determining the state time information based on the movement EMG signal, the time response process of the myogenic oxygen signal is determined by combining the myogenic oxygen signal collected by the blood oxygen signal acquisition device, realizing the coupling function analysis of the muscle blood vessels, and further realizing the coupling function analysis of the neurovascular.

[0052] When the collected movement EMG signal is only used to determine the state time information of the movement state, the surface EMG acquisition device can be a single-channel electrode, as long as it is attached to the surface of the target detected muscle, and its positional relationship with the blood oxygen signal acquisition device is not limited. Among them, the single-channel electrode can be understood as having only one electrode.

[0053] S130. Determine the neurovascular coupling analysis result of the target peripheral nerve based on the myogenic oxygen signal time response characteristic parameters.

[0054] In this embodiment, the myogenic oxygen signal time response characteristic parameter can characterize the coupling of muscle blood vessels in the time dimension. The coupling of neurovascular and muscle blood vessels is strongly correlated. Therefore, the coupling analysis result of muscle blood vessels determined based on the myogenic oxygen signal time response characteristic parameter can be directly used as the coupling analysis result of neurovascular.

[0055] In one implementation, when determining the myogenic oxygen signal time response characteristic parameter of the exercise myogenic oxygen signal according to the state time information of the exercise state, it includes: determining the target hemoglobin concentration change information associated with the exercise myogenic oxygen signal and the state time information; based on the target hemoglobin concentration change information, determining the response recovery parameter of the exercise myogenic oxygen signal, and using the response recovery parameter as the myogenic oxygen signal time response characteristic parameter; when determining the neurovascular coupling analysis result of the target peripheral nerve based on the myogenic oxygen signal time response characteristic, it includes: determining the time coupling characteristic analysis result of the neurovascular coupling analysis based on the myogenic oxygen signal time response characteristic parameter.

[0056] The coupling analysis of neurovascular can be divided into time coupling characteristic analysis and spatial coupling characteristic analysis. The myogenic oxygen signal time response characteristic parameter can characterize the change of myogenic oxygen signal over time. Therefore, the neurovascular coupling analysis result of the target peripheral nerve determined based on the myogenic oxygen signal time response characteristic parameter is the time coupling characteristic analysis result of neurovascular coupling analysis.

[0057] The technical solution of this embodiment is to receive the exercise myogenic oxygen signal of the target detection muscle in the exercise state collected by the blood oxygen signal acquisition device; determine the myogenic oxygen signal time response characteristic of the exercise myogenic oxygen signal according to the state time information of the exercise state; determine the neurovascular coupling analysis result of the target peripheral nerve based on the myogenic oxygen signal time response characteristic. The method provided in this embodiment realizes the coupling function evaluation of neurovascular for peripheral nerves by using the analysis of the time and spatial response characteristics of myogenic oxygen signals, and simplifies the difficulty of coupling function evaluation of neurovascular for peripheral nerves.

[0058] Embodiment 2

[0059] Figure 2 It is a flowchart of a method for neurovascular coupling analysis of peripheral nerves provided by the second embodiment of the present invention. This embodiment is further optimized on the basis of the above embodiment. As Figure 2 shown, the method includes:

[0060] S210. Receive the exercise myogenic oxygen signal of the target detection muscle in the exercise state collected by the blood oxygen signal acquisition device.

[0061] S220. Determine the myogenic oxygen signal time response characteristic parameter of the myogenic oxygen signal according to the state time information of the motion state.

[0062] S230. Determine the neurovascular coupling analysis result of the target peripheral nerve based on the myogenic oxygen signal time response characteristic parameter.

[0063] S240. Receive the myogenic electromyogram signal of the target detected muscle in the motion state collected by the surface electromyogram acquisition device.

[0064] On the basis of the above solution, collect multi-channel myogenic electromyogram signals through the surface electromyogram acquisition device with multi-channel electrodes, and combine the multi-channel myogenic oxygen signals to perform spatial coupling feature analysis of muscle blood vessel coupling, so as to realize spatial coupling feature analysis of neurovascular coupling.

[0065] In this embodiment, the surface electromyogram acquisition device includes multi-channel electrodes, the multi-channel electrodes are attached to the surface of the target muscle, and the central position of the electrode of each channel in the multi-channel electrodes coincides with the central position of the acquisition device of the associated channel in the blood oxygen signal acquisition device. The coincidence of the central position of the electrode of each channel in the multi-channel electrodes with the central position of the acquisition device of the associated channel in the blood oxygen signal acquisition device can ensure the consistency of the positions of the myogenic oxygen signal and the electromyogram signal. The blood oxygen signal acquisition device is arranged between the corresponding channel electrodes, and each channel electrode in the multi-channel electrodes corresponds to a blood oxygen signal acquisition device.

[0066] Among them, the multi-channel electrode can be multiple electrodes, that is, the surface electromyogram acquisition device includes multiple electrodes, and the multi-channel electrodes are attached to the surface of the target muscle. Detect the myogenic electromyogram signal of the target detected muscle in the motion state through the multi-channel electrode, and send the detected myogenic electromyogram signal to the neurovascular coupling analysis device for the peripheral nerve.

[0067] S250. For each channel, generate a neuroelectromyogram feature image according to the myogenic electromyogram signal corresponding to the channel, generate a neuro-myogenic oxygen feature image according to the myogenic oxygen signal corresponding to the channel, and determine the neurovascular related parameter based on the neuroelectromyogram feature image and the neuro-myogenic oxygen feature image.

[0068] In this embodiment, for each channel, the spatial coupling characteristics of muscle blood vessels can be analyzed by combining the exercise myo-oxygen signal and the multi-channel exercise electromyogram signal. When the muscle blood vessel coupling is strong, the positions where the exercise myo-oxygen changes and the exercise electromyogram changes are basically the same during exercise. Based on this, the coupling degree of muscle blood vessels can be judged by the correlation between the electromyogram feature image and the myo-oxygen feature image during exercise. Optionally, the correlation between the electromyogram feature image and the myo-oxygen feature image can be judged by methods such as calculating the two-dimensional correlation coefficient, comparing the activation area, and comparing the activation position. For example, when judging the correlation between the electromyogram feature image and the myo-oxygen feature image by comparing the activation area, the smaller the difference between the activation area of the electromyogram feature image and the activation area of the myo-oxygen feature image, the greater the correlation; when judging the correlation between the electromyogram feature image and the myo-oxygen feature image by comparing the activation position, the center position can be used as a reference point, and the smaller the distance between the activation position of the electromyogram feature image and the activation position of the myo-oxygen feature image, the greater the correlation.

[0069] In this embodiment, the exercise electromyogram signal can be obtained through the electrodes of each channel in the multi-channel electrode, the obtained exercise electromyogram signal can be used to generate a neuromuscular electromyogram feature image, and the neuromyo-oxygen feature image can be generated according to the received exercise myo-oxygen signal. The neuromuscular electromyogram feature image can represent the response of the electromyogram signal in time or space and can be represented by a two-dimensional image. The abscissa and ordinate in the electromyogram feature image represent the distribution position of the charge, and the pixel value of the pixel point represents the electromyogram feature value; the neuromyo-oxygen feature image can represent the response of the myo-oxygen signal in time or space and can be represented by a two-dimensional image. The abscissa and ordinate of the neuromyo-oxygen feature image represent the distribution position of the myo-oxygen signal, and the pixel value of the pixel point represents the target hemoglobin concentration value. Based on the correlation between the neuromuscular electromyogram feature image and the neuromyo-oxygen feature image, the coupling of muscle blood vessels can be determined, and then the coupling of neurovascular can be determined. Optionally, the correlation between the neuromuscular electromyogram feature image and the neuromyo-oxygen feature image can be determined by the methods such as calculating the two-dimensional correlation coefficient, comparing the activation area, and comparing the activation position in the above embodiments. Taking the method of determining the image correlation by the two-dimensional correlation coefficient as an example, methods such as two-dimensional correlation analysis method and Pearson correlation analysis method can be used to obtain the correlation coefficient, and the coupling function analysis of muscle blood vessels can be determined according to the calculated relevant parameters, and then the spatial coupling characteristic analysis result of neurovascular coupling analysis can be determined, where the correlation coefficient is proportional to the neurovascular coupling, and the larger the correlation coefficient value, the stronger the neurovascular coupling.

[0070] Optionally, generating a neuromyoelectric feature image according to the motion electromyographic signal corresponding to the channel and generating a neuromyooxygen feature image according to the motion myooxygen signal corresponding to the channel includes: extracting feature signals from the motion electromyographic signal to obtain electromyographic feature signals, performing interpolation processing based on the electromyographic feature signals to obtain the neuromyoelectric feature image; parsing the motion myooxygen signal to obtain myooxygen feature signals, and performing interpolation processing based on the myooxygen feature signals to obtain the neuromyooxygen feature image.

[0071] In this embodiment, feature extraction can be a method of transforming a set of measured values of a certain pattern to highlight the representative features of the pattern. By extracting features from the electromyographic signal, representative electromyographic feature signals can be obtained, and by extracting features from the myooxygen signal, representative myooxygen feature signals can be obtained. For example, when extracting features from the electromyographic signal, feature extraction can be performed by time-domain extraction methods of signal features, including root mean square, integral value, average value, standard deviation, etc.; feature extraction can be performed by frequency-domain extraction methods of signal features, including average frequency, median frequency, etc. Interpolation processing can estimate other data between reference data under known reference conditions, which can make the data curve smoother or make the pixel values of the image higher. By performing interpolation processing based on the electromyographic feature signals, electromyographic feature signals at more positions can be obtained, and a neuromyoelectric feature image can be constructed based on the electromyographic feature signals; similarly, by performing interpolation processing based on the myooxygen feature signals, a neuromyooxygen feature image can be obtained.

[0072] It should be noted that different types of features can be extracted from the electromyographic signal and the myooxygen signal, and corresponding images can be generated based on any feature. Taking the electromyographic signal as an example, corresponding time-domain features and frequency-domain features can be extracted, and a neuromyoelectric feature image can be generated based on the time-domain features or a neuromyoelectric feature image can be generated based on the frequency-domain features.

[0073] Optionally, the original signal can be filtered to remove physiological and system noises, motion interferences, etc. Among them, the processing of the electromyographic signal includes spatial filtering and feature extraction. For example, spatial filtering includes differential combination of multi-channel electromyographic signals, obtaining double-difference signals, quadruple-difference signals, Laplacian signals from the original monopolar signals, and reducing spatial crosstalk. The processing of the myooxygen signal can refer to the method in the above embodiment, and the hemoglobin concentration value can be calculated based on the myooxygen signal value.

[0074] Optionally, determining the neurovascular related parameters based on the neuromyoelectric feature image and the neuromyogenic oxygen feature image includes: normalizing the original myoelectric image matrix of the neuromyoelectric feature image to obtain a target myoelectric image matrix; normalizing the original myogenic oxygen image matrix of the neuromyogenic oxygen feature image to obtain a target myogenic oxygen image matrix; determining a target correlation coefficient between the target myoelectric image matrix and the target myogenic oxygen image matrix, and using the target correlation coefficient as the neurovascular related parameter.

[0075] In this embodiment, the myoelectric feature signals in the myoelectric image matrix may be parameters such as voltage and current, and the myogenic oxygen feature signals in the myogenic oxygen image matrix may be blood oxygen concentration, etc. It can be seen that the data dimensions in the two image matrices are different. To ensure the accuracy of data processing, it is necessary to normalize the myoelectric feature signals in the myoelectric image matrix and the myogenic oxygen feature signals in the myogenic oxygen image matrix. For example, when normalizing the myoelectric feature signals in the myoelectric image matrix, the maximum value in the myoelectric feature signals can be used as a reference value, and the normalized myoelectric feature signals can be represented by the ratio of the original value to the reference value. At this time, a target myoelectric image matrix is obtained; when normalizing the myogenic oxygen feature signals in the myogenic oxygen image matrix, the maximum value in the myogenic oxygen feature signals can be used as a reference value, and the normalized myogenic oxygen feature signals can be represented by the ratio of the original value to the reference value. At this time, a target myogenic oxygen image matrix is obtained. Calculate the target correlation coefficient based on the target myoelectric image matrix and the target myogenic oxygen image matrix, and use the target correlation coefficient as the neurovascular related parameter. Correspondingly, the target correlation coefficient can be calculated according to the two-dimensional correlation analysis method, Pearson correlation analysis method, etc. in the above embodiment.

[0076] S260. Determine the spatial coupling feature analysis result of the neurovascular coupling analysis based on the neurovascular related parameters of each channel.

[0077] The technical solution of this embodiment, by adding on the basis of the above embodiment to obtain multi-channel motion myoelectric signals collected by a multi-channel myoelectric acquisition device, for each channel, generating a neuromyoelectric feature image according to the motion myoelectric signal corresponding to the channel, generating a neuromyogenic oxygen feature image according to the motion myogenic oxygen signal corresponding to the channel, determining neurovascular related parameters based on the neuromyoelectric feature image and the neuromyogenic oxygen feature image; determining the spatial coupling feature analysis result of the neurovascular coupling analysis based on the neurovascular related parameters of each channel. By analyzing the spatial coupling characteristics of the neurovascular system, the spatial distribution information of the neurovascular response can be obtained, and the characteristics of neurovascular coupling can be more intuitively displayed.

[0078] Embodiment III

[0079] Figure 3This is a schematic diagram of the spatial arrangement of the multi-channel electrodes and optrodes provided in the third embodiment of the present invention. On the basis of the above embodiments, the present invention provides a preferred example of a method for analyzing neurovascular coupling for peripheral nerves, and the method includes: synchronous and co-located acquisition of sEMG signals and NIRS signals, acquisition of sEMG and NIRS signals according to a certain experimental paradigm, feature extraction and image reconstruction of sEMG and NIRS signals, calculation of the spatial correlation of sEMG and NIRS images, and calculation of the time response characteristics of the muscle oxygen signal.

[0080] (1) Synchronous and co-located acquisition of sEMG signals and NIRS signals

[0081] In this embodiment, multi-channel surface electromyography (sEMG) is used to collect neuromuscular electrical signals, and near-infrared spectroscopy (NIRS) is used to collect neuromuscular blood oxygen signals. The multi-channel sEMG electrodes are numerous and arranged closely, so a two-dimensional electromyogram distribution signal can be obtained. The NIRS technology uses dual-wavelength near-infrared light to irradiate tissues. Due to the different absorption spectra of oxygenated hemoglobin and deoxygenated hemoglobin in tissues, the concentration changes of oxygenated hemoglobin (HbO2), deoxygenated hemoglobin (Hb), and total hemoglobin (tHb) in tissues can be analyzed according to the intensity of the emitted light. The multi-channel NIRS optrodes (including light sources and detectors) are arranged closely, and a two-dimensional muscle oxygen distribution signal can be obtained. The detection channel of NIRS is located between a pair of optrodes (a pair of light sources and detectors), while the surface electromyography detection channel is located under the electrode, and the optical signal and the electrical signal will not interfere with each other. Therefore, surface electromyography electrodes are arranged between a pair of optrodes to achieve co-located acquisition of electrical signals and blood oxygen signals. A synchronous trigger is set for the electromyogram signal and the muscle oxygen signal to achieve synchronous acquisition of the two physiological signals.

[0082] sEMG and NIRS signals can be collected according to a certain experimental paradigm. The strength of different subjects is different, so in this embodiment, the exercise stimulus intensity is quantified according to the proportion of the maximum voluntary contraction (MVC). In the experiment, the subject needs to perform the exercise task with the maximum strength first and repeat it continuously 5 times, and the average value of the 5 measured strengths is taken as the MVC. Then, the exercise intensity is increased in increments of 10% MVC, 30% MVC, 50% MVC, and 70% MVC, and the changes in electromyogram signals and blood oxygen signals under different exercise stimulus intensities are detected. The task duration should fully consider whether it can cause changes in the muscle oxygen signal of the subject and the endurance limit of the subject. The muscle oxygen signal is a slow-changing signal, so the task duration should be at least 1 s or more. The exercise stimulus task contains multiple trials, and there is a rest between two trials. The average response of multiple tasks is finally analyzed for data analysis to exclude the random interference of a single task.

[0083] (2) Feature extraction and image reconstruction of sEMG and NIRS signals

[0084] Figure 4 This is the flowchart of sEMG and NIRS signal feature extraction and image reconstruction provided in the third embodiment of the present invention, which performs two-dimensional interpolation on multi-channel muscle oxygen characteristic signals and reconstructs images. Among them, the processing of sEMG signals may include spatial filtering and feature extraction. The original sEMG and NIRS signals are filtered to remove physiological and system noises, as well as motion interference, to obtain the filtered signals. Spatial filtering includes the differential combination of multi-channel sEMG signals, and bipolar signals such as double-differential signals, quadruple-differential signals, and Laplacian signals are obtained from the original monopolar signals to reduce spatial crosstalk. sEMG signal feature extraction includes time-domain features such as root mean square, integral value, average value, standard deviation, etc.; frequency-domain features such as average frequency, median frequency, etc. The sEMG signals that have undergone the above spatial filtering and eigenvalue extraction are subjected to two-dimensional interpolation to form various spatial distribution images of electromyography features.

[0085] The NIRS signal collects the change signal of neuromuscular blood oxygen concentration. The blood oxygen parameters that can be analyzed from the NIRS signal are: the change in oxyhemoglobin concentration (ΔHBO), the change in deoxyhemoglobin concentration (ΔHB), and the change in total hemoglobin concentration (ΔHBT). The calculation method is as follows:

[0086]

[0087] OD is the optical density value, I0 is the incident light intensity value, and I is the transmitted light intensity value. According to the Beer-Lambert law, the optical density value is related to ΔHBO, ΔHB, and the optical path length d. The two incident wavelengths are represented by subscripts 1 and 2 respectively. and are the molar extinction coefficients of oxyhemoglobin and deoxyhemoglobin respectively. DPF is the differential path factor. The change in total hemoglobin concentration ΔHBT is the sum of ΔHBO and ΔHB.

[0088]

[0089]

[0090] ΔHBT = ΔHBO + ΔHB

[0091] (3) Calculation of the spatial correlation of sEMG and NIRS images

[0092] The degree of spatial matching between neuromuscular electrical activity and blood circulation activity determines whether blood circulation accurately enters the neuromuscular activity area and is one of the important indicators for judging neuromuscular coupling function. In this study, the two-dimensional correlation coefficient corr2 was used to characterize the spatial correlation between neuromuscular electrical activity and blood circulation activity. First, regions of interest (ROIs) representing neuromuscular activity were selected on the electromyogram image and the muscle oxygenation image; then, the electromyogram image and the muscle oxygenation image were normalized; finally, the two-dimensional correlation coefficient corr2 was calculated, and the formula is as follows:

[0093]

[0094] where EMG and NIRS represent the electromyogram image and the muscle oxygenation image matrices respectively, and m and n represent the row coordinate and column coordinate values of the matrices.

[0095] (4) Calculation of the time response characteristics of the muscle oxygen signal

[0096] Figure 5 is a schematic diagram for calculating the time response characteristics of the muscle oxygen signal in the ROI region provided in Embodiment III of the present invention. Figure 6 is a flowchart of the method for evaluating the motor neuromuscular vascular coupling function provided in Embodiment III of the present invention. During the task time, muscle contraction causes a decrease in blood flow and an increase in oxygen consumption, and the corresponding time response parameters include response time and response amplitude; during the rest period after the task, blood flow and blood recover, and the reperfusion volume is higher than the baseline value, and the corresponding time response parameters include recovery time and recovery peak.

[0097] In the embodiment of the present invention, the motor neuromuscular vascular coupling function is evaluated by jointly detecting electromyogram and muscle oxygen information using multi-channel surface electromyogram (sEMG) and near-infrared spectroscopy (NIRS). By synchronously and co-locating the multi-channel sEMG and NIRS on muscle tissue, the time and amplitude responses of muscle blood oxygen during neuromuscular electrical activity can be obtained, and at the same time, the spatial distribution information of electromyogram and muscle oxygen can be obtained, realizing the analysis of neuromuscular vascular coupling function from both time and space dimensions.

[0098] Embodiment IV

[0099] Figure 7 is a schematic structural diagram of a neurovascular coupling analysis device for peripheral nerves provided in Embodiment IV of the present invention. As Figure 7 shown, the device includes:

[0100] A muscle oxygen signal receiving module 710, configured to receive the motor muscle oxygen signal sent by the blood oxygen signal acquisition device;

[0101] A muscle oxygen response characteristic module 720, configured to determine the muscle oxygen signal time response characteristics of the motor muscle oxygen signal according to the state time information of the motion state;

[0102] A neurovascular coupling analysis module 730 is configured to determine a neurovascular coupling analysis result of the target peripheral nerve based on the time response characteristics of the muscle oxygen signal.

[0103] The technical solution of this embodiment provides a neurovascular coupling analysis device for a peripheral nerve. By receiving the exercise muscle oxygen signal of a target detection muscle in a motion state collected by a blood oxygen signal acquisition device; determining the muscle oxygen signal time response characteristic parameters of the exercise muscle oxygen signal according to the state time information of the motion state; and determining the neurovascular coupling analysis result of the target peripheral nerve based on the muscle oxygen signal time response characteristic parameters. The method provided in this embodiment realizes the evaluation of the neurovascular coupling function for the peripheral nerve by using the analysis of the time and space response characteristics of the muscle oxygen signal, and simplifies the difficulty of evaluating the neurovascular coupling function of the peripheral nerve.

[0104] Based on the above embodiment, optionally, the muscle oxygen response characteristic module 720 is specifically configured to:

[0105] Determine the target hemoglobin concentration change information associated with the exercise muscle oxygen signal and the state time information; determine the response recovery parameter of the exercise muscle oxygen signal based on the target hemoglobin concentration change information, and use the response recovery parameter as the muscle oxygen signal time response characteristic parameter;

[0106] Correspondingly, the neurovascular coupling analysis module 730 is specifically configured to:

[0107] Determine the time coupling characteristic analysis result of the neurovascular coupling analysis based on the muscle oxygen signal time response characteristic parameter.

[0108] Based on the above embodiment, optionally, the target hemoglobin concentration change information is at least one of the oxygenated hemoglobin concentration change information, deoxygenated hemoglobin concentration change information, and total hemoglobin concentration change information.

[0109] Based on the above embodiment, optionally, the device further includes a state time information determination module, configured to:

[0110] Before determining the muscle oxygen signal time response characteristic parameters of the exercise muscle oxygen signal according to the state time information of the motion state, receive the exercise electromyogram signal of the target detection muscle in the motion state collected by a surface electromyogram acquisition device, where the surface electromyogram acquisition device includes a single-channel electrode, and the single-channel electrode is attached to the surface of the target muscle;

[0111] Determine the state time information of the motion state according to the exercise electromyogram signal.

[0112] Based on the above embodiments, optionally, the neurovascular spatial coupling analysis module is configured to:

[0113] Receive the motor electromyogram signal of the target detected muscle in the motion state collected by the surface electromyogram acquisition device, where the surface electromyogram acquisition device includes multi-channel electrodes, the multi-channel electrodes are attached to the surface of the target muscle, and the central position of the electrodes of each channel in the multi-channel electrodes coincides with the central position of the acquisition device of the associated channel in the blood oxygen signal acquisition device;

[0114] For each channel, generate a neuromyoelectric feature image according to the motor electromyogram signal corresponding to the channel, generate a neuromyogenic oxygen feature image according to the motor myogenic oxygen signal corresponding to the channel, and determine neurovascular related parameters based on the neuromyoelectric feature image and the neuromyogenic oxygen feature image;

[0115] Determine the spatial coupling feature analysis result of the neurovascular coupling analysis based on the neurovascular related parameters of each channel.

[0116] Based on the above embodiments, optionally, the neurovascular spatial coupling analysis module is specifically configured to:

[0117] Extract features from the motor electromyogram signal to obtain an electromyogram feature signal, and perform interpolation processing based on the electromyogram feature signal to obtain the neuromyoelectric feature image;

[0118] Parse the motor myogenic oxygen signal to obtain a myogenic oxygen feature signal, and perform interpolation processing based on the myogenic oxygen feature signal to obtain the neuromyogenic oxygen feature image.

[0119] Based on the above embodiments, optionally, the neurovascular spatial coupling analysis module is specifically configured to:

[0120] Perform normalization processing on the original electromyogram image matrix of the neuromyoelectric feature image to obtain a target electromyogram image matrix;

[0121] Perform normalization processing on the original myogenic oxygen image matrix of the neuromyogenic oxygen feature image to obtain a target myogenic oxygen image matrix;

[0122] Determine the target correlation coefficient between the target electromyogram image matrix and the target myogenic oxygen image matrix, and use the target correlation coefficient as the neurovascular related parameter.

[0123] The neurovascular coupling analysis device for peripheral nerves provided by the embodiments of the present invention can execute the neurovascular coupling analysis method for peripheral nerves provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the method.

[0124] Embodiment Five

[0125] Figure 8 This is a schematic structural diagram of an electronic device provided in Embodiment 5 of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as, for example, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, for example, personal digital processors, cellular telephones, 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 present invention described and / or claimed herein.

[0126] As Figure 8 shown, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc. The memory stores a computer program executable by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0127] Multiple components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disc, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0128] The processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the method for neurovascular coupling analysis of peripheral nerves.

[0129] In some embodiments, a method for neurovascular coupling analysis of peripheral nerves can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for neurovascular coupling analysis of peripheral nerves described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform the method for neurovascular coupling analysis of peripheral nerves by any other suitable means (e.g., by means of firmware).

[0130] Various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuitry, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0131] The computer program for implementing the method for neurovascular coupling analysis of peripheral nerves of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowchart and / or block diagram are implemented. The computer program can be executed entirely on the machine, partially on the machine, as a stand-alone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0132] Embodiment Six

[0133] Embodiment Six of the present invention further provides a computer-readable storage medium storing computer instructions for causing a processor to execute a method for neurovascular coupling analysis of peripheral nerves, the method including:

[0134] Receiving the exercise muscle oxygen signal of the target detection muscle in the exercise state collected by the blood oxygen signal acquisition device;

[0135] Determine the myo-oxygen signal time response characteristic parameters of the myo-oxygen signal according to the state time information of the motion state;

[0136] Determine the neurovascular coupling analysis result of the target peripheral nerve based on the myo-oxygen signal time response characteristic parameters.

[0137] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0138] In order to provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which the user can provide input to the electronic device. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and the input received from the user can be in any form (including acoustic input, voice input, or tactile input).

[0139] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected to each other by digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), blockchain network, and the Internet.

[0140] A computing system can include a client and a server. The client and the server are generally far from each other and typically interact through a communication network. The client-server relationship is created by computer programs running on respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, and solves the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0141] It should be understood that various forms of the processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved, and no limitation is made herein.

[0142] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for neurovascular coupling analysis of peripheral nerves, characterized in that, Including: Receiving the exercise myo-oxygen signal of the target detection muscle in the exercise state collected by the blood oxygen signal collection device; Determining the myo-oxygen signal time response characteristic parameter of the exercise myo-oxygen signal according to the state time information of the exercise state; Determining the time coupling characteristic analysis result of the neurovascular coupling analysis of the target peripheral nerve based on the myo-oxygen signal time response characteristic parameter; Receiving the exercise myoelectric signal of the target detection muscle in the exercise state collected by the surface electromyogram collection device, wherein the surface electromyogram collection device includes a multi-channel electrode, the multi-channel electrode is attached to the surface of the target detection muscle, and the center position of the electrode of each channel in the multi-channel electrode coincides with the center position of the collection device of the associated channel in the blood oxygen signal collection device; For each channel, generating a neuromyoelectric characteristic image according to the exercise myoelectric signal corresponding to the channel, generating a neuromyo-oxygen characteristic image according to the exercise myo-oxygen signal corresponding to the channel, determining the correlation coefficient between the neuromyoelectric characteristic image and the neuromyo-oxygen characteristic image, and using the correlation coefficient between the neuromyoelectric characteristic image and the neuromyo-oxygen characteristic image as the neurovascular correlation parameter; Determining the spatial coupling characteristic analysis result of the neurovascular coupling analysis based on the neurovascular correlation parameters of each channel.

2. The method according to claim 1, characterized in that, The determining the myo-oxygen signal time response characteristic parameter of the exercise myo-oxygen signal according to the state time information of the exercise state includes: Determining the target hemoglobin concentration change information associated with the exercise myo-oxygen signal and the state time information; Determining the response recovery parameter of the exercise myo-oxygen signal based on the target hemoglobin concentration change information, and using the response recovery parameter as the myo-oxygen signal time response characteristic parameter; Correspondingly, the determining the neurovascular coupling analysis result of the target peripheral nerve based on the myo-oxygen signal time response characteristic includes: Determining the time coupling characteristic analysis result of the neurovascular coupling analysis based on the myo-oxygen signal time response characteristic parameter.

3. The method according to claim 2, characterized in that, The target hemoglobin concentration change information is at least one of the oxygenated hemoglobin concentration change information, deoxygenated hemoglobin concentration change information, and total hemoglobin concentration change information.

4. The method according to claim 1, characterized in that, Before determining the myo-oxygen signal time response characteristic parameter of the exercise myo-oxygen signal according to the state time information of the exercise state, it further includes: Receiving the exercise myoelectric signal of the target detection muscle in the exercise state collected by the surface electromyogram collection device, wherein the surface electromyogram collection device includes a single-channel electrode, and the single-channel electrode is attached to the surface of the target detection muscle; Determining the state time information of the exercise state according to the exercise myoelectric signal.

5. The method according to claim 1, characterized in that, The generating a neuromyoelectric characteristic image according to the exercise myoelectric signal corresponding to the channel and generating a neuromyo-oxygen characteristic image according to the exercise myo-oxygen signal corresponding to the channel includes: Performing feature extraction on the exercise myoelectric signal to obtain a myoelectric feature signal, and performing interpolation processing based on the myoelectric feature signal to obtain the neuromyoelectric characteristic image; Based on the analysis of the exercise myogenic oxygen signal, a myogenic oxygen characteristic signal is obtained, and interpolation processing is performed based on the myogenic oxygen characteristic signal to obtain the neuro-myogenic oxygen characteristic image.

6. The method according to claim 1, characterized in that, Determining the correlation coefficient between the neuro-electromyogram characteristic image and the neuro-myogenic oxygen characteristic image, and using the correlation coefficient between the neuro-electromyogram characteristic image and the neuro-myogenic oxygen characteristic image as the neurovascular correlation parameter, includes: Performing normalization processing on the original electromyogram image matrix of the neuro-electromyogram characteristic image to obtain a target electromyogram image matrix; Performing normalization processing on the original myogenic oxygen image matrix of the neuro-myogenic oxygen characteristic image to obtain a target myogenic oxygen image matrix; Determining the correlation coefficient between the target electromyogram image matrix and the target myogenic oxygen image matrix, and using the correlation coefficient as the neurovascular correlation parameter.

7. A device for neurovascular coupling analysis of peripheral nerves, characterized in that, Including: A myogenic oxygen signal receiving module, configured to receive the exercise myogenic oxygen signal of the target detection muscle collected by a blood oxygen signal acquisition device during exercise; A myogenic oxygen response characteristic module, configured to determine the myogenic oxygen signal time response characteristic parameter of the exercise myogenic oxygen signal according to the state time information of the exercise state; A neurovascular coupling analysis module, configured to determine the time coupling characteristic analysis result of the neurovascular coupling analysis of the target peripheral nerve based on the myogenic oxygen signal time response characteristic parameter; Receiving the exercise electromyogram signal of the target detection muscle during exercise collected by a surface electromyogram acquisition device, where the surface electromyogram acquisition device includes multi-channel electrodes, the multi-channel electrodes are attached to the surface of the target detection muscle, and the central position of the electrode of each channel in the multi-channel electrodes coincides with the central position of the acquisition device of the associated channel in the blood oxygen signal acquisition device; For each channel, generating a neuro-electromyogram characteristic image according to the exercise electromyogram signal corresponding to the channel, generating a neuro-myogenic oxygen characteristic image according to the exercise myogenic oxygen signal corresponding to the channel, determining the correlation coefficient between the neuro-electromyogram characteristic image and the neuro-myogenic oxygen characteristic image, and using the correlation coefficient between the neuro-electromyogram characteristic image and the neuro-myogenic oxygen characteristic image as the neurovascular correlation parameter; Determining the spatial coupling characteristic analysis result of the neurovascular coupling analysis based on the neurovascular correlation parameters of each channel.

8. An electronic device, characterized in that, The electronic device includes: 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 neurovascular coupling analysis of peripheral nerves according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing a processor to implement the method for neurovascular coupling analysis of peripheral nerves according to any one of claims 1-6 when executed.

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