Motor unit number measurement system and measurement method, myoelectric evoked potential instrument

Abstract

The present invention belongs to the technical field of muscle nerve assessment, and specifically relates to a motor unit number measurement system and measurement method, and an electromyographic evoked potential meter. The motor unit number measurement system includes: a gear setting unit, which sets a detection gear in the gear of the measurement system; a processor, on which a first detection unit, a second detection unit, and a data analysis unit are arranged; wherein the first detection unit detects valid data of a passive induced signal in the detection gear, that is, the first valid data; the second detection unit detects valid data of 1 active induced signal, that is, the second valid data; the data analysis unit obtains the measurement result of the motor unit number based on the first valid data and the second valid data.

Description

Technical Field

[0001] The invention belongs to the technical field of muscle nerve evaluation, and in particular relates to a motor unit number measurement system and method, and an electromyographic evoked potential instrument. Background Art

[0002] The motion unit is defined as Motor neurons and the muscle fibers they control, motor units achieve complex motor functions by coordinating and precisely controlling muscle contraction, and motor units can show obvious changes in some degenerative diseases. For example, in amyotrophic lateral sclerosis, the number of motor units decreases. Therefore, quantifying the number of motor units in muscles is of great value for monitoring, prognosis, and evaluating drug experiments for such diseases.

[0003] In the related art, the Motor Unit Number Index (MUNIX) is used to evaluate the number of motor units in the muscle. This method has little irritation to the human body, is fast and easy to implement. The motor unit number (MUN) is estimated based on the waveform set of the muscle fiber action potential (SMUP) and the waveform size of the maximum compound muscle action potential (CMAP). Since the maximum CMAP wave is difficult to determine and the muscle fiber action potential (SMUP) is easily interfered by noise, the data is unstable, resulting in poor repeatability of the motor unit number (Motor Unit Number Index, MUN) obtained, and unstable results. For example, US Patent US7848797B2 Motor unit number estimation (MUNE) for the assessment of neuromuscular function discloses a method for assessing neuromuscular function by estimating the number of motor units, and uses the screening response waveform to improve the stability of the assessment result; in the process of preprocessing the response waveform, the response waveform is screened by similarity measurement (which can be the correlation coefficient between two waveforms or the mean square error between two waveforms) and noise level (the noise level of the activity area assessment of the motor unit response induced by the threshold stimulus is defined); in the process of alternation recognition (any change between the maximum CMAP waves is identified as the alternation of another motor unit or the activation of a new motor unit), the response waveform is determined by global search and direct alternation recognition, and finally used for the assessment of the number of motor units (MUN). Obviously, the main way to screen data in this technical solution is to exclude noise based on data characteristics, and there is no correlation between data characteristics and actual measurement results or assessment levels, so as to jointly screen out the most effective data for the assessment results in a specific detection scenario. In related studies, in order to obtain accurate evaluation results, all gears are generally evaluated and the amount of detection data for each gear is uniformly set to the highest sample data amount to pursue the reliability of the conclusion. In actual product applications, more attention is paid to the adaptability of detection time and detection scenarios. For example, in the process of sports rehabilitation training, it is necessary to frequently evaluate the number of motor units in the patient's muscles and design training plans based on the evaluation results. Since users do not try to use uniform samples as much as in experimental research, and everyone's recovery situation is different, not all patients need to test all gears, and not all gears require the same amount of test data. If traditional detection methods continue to be used, the difference in data validity caused by different evaluation gears will be ignored, and a lot of time will be spent on collecting data samples that have no obvious effect on the evaluation results. Therefore, this case aims to study a measurement system for the number of motor units that can adjust the sampling times at any time according to the usage scenario, so as to reduce the detection time as much as possible while ensuring the test results. Summary of the invention

[0004] The present invention provides a motor unit number measurement system and method, and an electromyographic evoked potential instrument, which can detect the number of motor units by judging the n rounds collected in turn by the gear position and the number of motor units collected by the gear position. The value size determines whether to collect passive induced signals, which can effectively reduce the sampling time. At the same time, the corresponding calculation method for obtaining the number of motor units is selected, and the evaluation behavior can be terminated at any time according to the evaluation results, which improves the flexibility of product use.

[0005] In order to solve the above technical problems, the present invention provides a system for measuring the number of motion units, including: a gear setting unit, which sets the gear in the measuring system a detection gear; a processor, on which a first detection unit, a second detection unit, and a data analysis unit are arranged; wherein the first detection unit is in the Detected in the detection gear The second detection unit detects valid data of a passive induced signal, namely, the first valid data; the second detection unit detects valid data of an active induced signal, namely, the second valid data; the data analysis unit is based on The first valid data and the second valid data are combined to obtain the measurement result of the number of motion units, including: obtaining the area of ​​the maximum waveform of the second valid data and power ; Get The waveform area of ​​the first valid data and power ; Get Number of motor units; The number of motion units is used to obtain multiple sets of fitting coefficients; based on the multiple sets of fitting coefficients, the measurement results of the number of motion units are obtained; and the limit value setting unit is set in the first In the detection position The maximum limit is not a uniform value; parameter setting interface, set The order of collecting the first valid data is to collect the gears in turn, that is, to collect the M detection gears as one round of valid data, and to collect at most one first valid data for each detection gear in each round of valid data collection, and to form a single group of first valid data first; the acquisition The number of motion units includes: In the detection position The maximum value of , collect the first valid data of n rounds, then , ; Determine n and any detection gear Value size, if ,at this time The total number of first valid data of the detection gear ;like , No. The detection gear skips the acquisition of passive induced signals. The total number of first valid data of the detection gear , then the The number of motion units in the detection gear is supplemented to according to The number of motion units to obtain multiple sets of fitting coefficients includes: The number of motion units of each detection gear is set to the same group to obtain n groups of data; and n groups of fitting coefficients are obtained based on the n groups of data.

[0006] Further, the first detection unit includes: a data stability detection module, configured to detect data stability, that is, whether the characteristic value of the passively induced signal meets the first set threshold of the current detection gear; and\or a gear matching verification module, configured to detect the gear matching degree, that is, whether the waveform area of ​​the passively induced signal meets the second set threshold of the current detection gear.

[0007] Furthermore, the data stability detection module includes: dividing the passive induced signal of a certain detection gear to obtain multiple segments of data; judging whether the data characteristics of the current segment of data meet the first set threshold; if so, the current data is counted as the first valid data and the next data is collected according to the set collection method; if not, it is not counted and the passive induced signal is continued to be collected in the current detection gear.

[0008] Furthermore, the gear matching verification module includes: setting a gear numerical threshold for each detection gear; obtaining the waveform area of ​​the passive induced signal in the current detection gear; judging whether the waveform area meets the gear numerical threshold of the current detection gear; if so, the current data is counted as the first valid data and the next data is collected according to the set collection method; if not, it is not counted and the passive induced signal is continued to be collected in the current detection gear.

[0009] Further, the limit setting unit includes: according to the the waveform area of ​​the passive induced signal in the first detection gear; the number of passive induced signals is increased successively; it is determined whether the stability of the waveform area meets the third set threshold; if so, the first The first valid data in the detection gear The value is set to the number of current passive induced signals; if not, the number of passive induced signals continues to increase until the stability of the mean meets the third set threshold.

[0010] Further, the data analysis unit is based on The measurement result of the number of motion units obtained by combining the first valid data with the second valid data includes: obtaining the area of ​​the maximum waveform of the second valid data and power ; Get The waveform area of ​​the first valid data and power ; Get The number of motion units corresponding to valid data; The plurality of groups of fitting coefficients are obtained based on the number of motion units; and the measurement results of the number of motion units are obtained based on the plurality of groups of fitting coefficients.

[0011] Further, the method of obtaining the measurement results of the number of motion units based on multiple sets of fitting coefficients includes: setting , the number of motion units , represents the first coefficient in the fitting coefficients, represents the second coefficient in the fitting coefficient; let Get the measurement result of the number of moving units , Indicates the set waveform area of ​​the first valid data.

[0012] Furthermore, the second detection unit includes: obtaining the relative increment of the peak-to-peak amplitude of the waveform and the stimulation intensity; determining whether the relative increment meets a fourth set threshold; if so, taking the current waveform as the maximum waveform of the second valid data; if not, continuing to increase the stimulation intensity until the maximum waveform is obtained; if the maximum waveform is still not obtained by continuing to increase the stimulation intensity to the stimulation intensity threshold, taking the waveform corresponding to the stimulation intensity threshold as the maximum waveform of the second valid data.

[0013] Further, when When the measurement result of the number of motion units is configured to calculate the measurement results of the number of motion units of each group according to the n groups of fitting coefficients and then take the average.

[0014] In a second aspect, the present invention provides an electromyographic evoked potential instrument, comprising: an acquisition system, including a plurality of acquisition units, respectively used to acquire active evoked signals and passive evoked signals; the measurement system, used to output the measurement results of the number of motor units; an electrical stimulation device, outputting electrical stimulation for active evoked signals.

[0015] In a third aspect, the present invention provides a method for measuring the number of motion units, wherein the measurement result of the number of motion units is based on The first valid data and the second valid data are jointly obtained. .

[0016] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the motion unit number measurement method.

[0017] The beneficial effect of the present invention is that the measuring system and method of the number of motion units of the present invention are based on The detection gears collect passive induced signals and active induced signals respectively, and detect the validity of each induced signal through multiple detection units arranged on the processor to detect The valid data of the passive induced signal and the valid data of the active induced signal are then combined through the data analysis unit. Detection gear analysis ( ) valid data, thereby obtaining the measurement results of the number of motion units. Determine the n rounds collected in turn by the gear position and the The value size can be used to determine whether to collect passive induced signals, which can effectively reduce the sampling time. At the same time, the corresponding calculation method for obtaining the number of motor units can be selected, and the evaluation behavior can be terminated at any time according to the evaluation results, which improves the flexibility of product use, reduces the inspection time while ensuring the stability of the data, and can improve the stability and repeatability of the measurement results.

[0018] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0019] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 This is the principle block diagram of the measurement system.

[0022] Figure 2 This is a detection flow chart of the first valid data.

[0023] Figure 3 It is a flow chart of collecting the first valid data in a gear rotation collection manner.

[0024] Figure 4 This is a flow chart for detecting the maximum waveform of the second valid data.

[0025] Figure 5It is a schematic diagram of collecting the first valid data in a gear rotation collection manner.

[0026] Figure 6 It is a schematic diagram of collecting the first valid data in a gear-by-gear manner. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] Example 1.

[0029] See Figure 1-Figure 6 This embodiment 1 provides a system for measuring the number of motion units, including: a gear setting unit, which sets the gear in the measuring system a detection gear; a processor, on which a first detection unit, a second detection unit, and a data analysis unit are arranged; wherein the first detection unit is in the Detected in the detection gear The second detection unit detects valid data of a passive induced signal, namely, the first valid data; the second detection unit detects valid data of an active induced signal, namely, the second valid data; the data analysis unit is based on The measurement result of the number of motion units is obtained by combining the first valid data with the second valid data. .

[0030] Specifically, since the gears of the measurement system of the number of motion units are divided according to the measurement value interval of the number of motion units, taking the SIP area value as an example, the gear interval of the measurement system is set to one gear every 10, that is, the minimum gear is 30 to the maximum gear is 500; M detection gears are set equally or unequally between the minimum gear and the maximum gear. The gear is initially set to 30 because the smaller the gear, the smaller the contraction force of the subject, and the difficulty of obtaining data increases. The gear 500 represents the maximum contraction force. In the actual measurement process, all or part of the gears can be selected as the detection gears according to the measurement purpose or experience value using the human-computer interaction interface of the gear setting unit, that is, the number of detection gears. Not greater than the total number of gears in the measurement system. The measured value is based on The first valid data and the second valid data are jointly acquired, so the valid data of the passive induced signal needs to be determined according to the number of detected gears. Corresponding to collection and detection, the valid data of the active induced signal only needs to be judged as a whole, and there is no need to use the gear value threshold for judgment, thus forming an inconsistent judgment model for each valid data, that is, the passive induced signal uses the data stability detection module in the first detection unit and\or the gear matching detection to obtain the first valid data; the active induced signal uses the second detection unit to detect the maximum waveform to obtain the second valid data.

[0031] Optionally, the passive induced signal may be, for example but not limited to, a surface electromyography interference pattern (SIP) and a needle electromyography interference pattern (IP); the active induced signal may be, for example but not limited to, a compound muscle action potential (CMAP) and an H wave.

[0032] As an optional implementation of the first detection unit.

[0033] The first detection unit includes: a data stability detection module, configured to detect data stability, that is, whether the characteristic value of the passively induced signal meets the first set threshold of the current detection gear; and\or a gear matching verification module, configured to detect the gear matching degree, that is, whether the waveform area of ​​the passively induced signal meets the second set threshold of the current detection gear.

[0034] Optionally, the data stability detection module includes: dividing the passive induced signal of a certain detection gear to obtain multiple segments of data; judging whether the data characteristics of the current segment of data meet the first set threshold; if so, the current data is counted as the first valid data and the next data is collected according to the set collection method; if not, the data is not counted and the passive induced signal is collected in the current detection gear. The data characteristics are, for example, but not limited to, the standard deviation of the root mean square (RMS) of the data. The data stability detection module not only judges whether the data is valid, but also judges whether the data is induced by a stabilizing force. The data stability detection module divides the collected passive induced signal data by windowing, calculates the root mean square of the corresponding divided data respectively, calculates the standard deviation of the root mean square of all divided data as a basis for measuring whether the current data is stable, and compares it with the set threshold to determine whether the data is induced by a stable contraction force. If only the induced waveform is included in the local data, the obtained data is not induced by a stable force, and the corresponding data will not be saved. It is impossible to determine whether the data is stable based on only a few waveforms. The data stability detection module solution can ensure that the data is valid data induced by a stable force, thereby ensuring the accuracy of the results.

[0035] Optionally, the gear matching verification module includes: setting a gear numerical threshold for each detection gear; obtaining the waveform area of ​​the passive induced signal in the current detection gear; judging whether the waveform area meets the gear numerical threshold of the current detection gear; if so, the current data is counted as the first valid data and the next data is collected according to the set collection method; if not, it is not counted and the passive induced signal continues to be collected in the current detection gear.

[0036] In this case, the data stability detection module and the gear matching verification module can exist separately, and only detect the data stability or the gear matching degree to determine the validity of the passive induced signal. Preferably, the two can exist at the same time and the data stability is detected first. Only the passive induced signal collected that meets both detection results can be saved as the first valid data. Figure 2 , taking the surface electromyography interference pattern (SIP) as an example, after collecting the SIP signal in a single detection gear, first draw a window to calculate the RMS, and then compare it with the first set threshold value based on the standard deviation of the RMS. When the standard deviation of the RMS is less than the first set threshold value, calculate the SIP area and compare the SIP area with the gear value threshold value of the current detection gear. If the SIP area meets the gear value threshold value, it is counted as the first valid data and saved. When the standard deviation of the RMS is less than the first set threshold value or the SIP area exceeds the gear value threshold value requirement, re-collect the valid data of the current gear.

[0037] As mentioned earlier, in Detected in the detection gear The first valid data is the valid data of the passive induced signal. Therefore, according to the data collection method requirements, there can be two sorting methods to specify the gear sorting and collection order respectively. Specifically, the parameter setting interface of the measurement system can also be provided with a collection method option, including gear sorting and collection order, for setting The first valid data collection order. The gear sorting includes: automatically sorting the detection gear according to the gear level to form a default order for the detection gear; or setting the sorted detection gear according to the detection requirements to form a setting order for the detection gear. In general, if the detection gear is all the gears of the measurement system, it can be sorted from high to low or from low to high according to the order of the gear itself to form a default order, which can be executed without selection. However, if the detection is a partial gear that is screened, not all the gears of the measurement system, it is necessary to specifically select the gear that needs to be detected and sort it purposefully according to the expected or empirical value. After selecting the gear sorting, since the first valid data is divided into M detection gears and each detection gear has In general, the acquisition process can be divided into two types: gear-by-gear acquisition, that is, in each round of data acquisition, each detection gear acquires at most one first valid data; or gear-by-gear acquisition, that is, the first Detection gear collection After the first valid data is obtained, the next detection gear is entered.

[0038] Gear-by-gear acquisition is to first meet the first valid data quantity of the current detection gear, and then enter the next detection gear, giving priority to forming the first valid data of a single detection gear. and the second coefficient , so the first valid data must completely cover ( ) can only calculate the fitting coefficient within a single group of motion units by collecting data in each gear. Therefore, the method of collecting data in each gear cannot calculate the fitting coefficient during the collection of valid data. Group fitting can only be performed after all valid data are collected. Gear-by-gear collection can reduce the number of times the subject adjusts the experimental state and make it easier to collect stable data. Figure 6 Each gear represents the SIP area in mV*ms. At the beginning, the data is collected from the detection gear 30. For example, the first valid data quantity to be collected at the detection gear 30 is It is 6. After the collection is completed, the detection gears 100, 45, 125, 55, 250, 70, 350, and 80 will be collected in sequence according to the gear order. A rest will be taken after the collection is completed at each detection gear to avoid the influence of muscle fatigue on the results. After collecting the detection gear 500, all the first valid data collection is completed.

[0039] The gear rotation collection is based on Each detection gear is used as a round of effective data collection, and a single group of first effective data is formed first. Generally, the fitting coefficient can be calculated after collecting more than two groups of first data, without waiting. After all the first valid data are completed, the measurement process can be ended at any time according to the measurement results and expectations. For example, after the first valid data is collected in the detection gear 1, the first valid data is collected in the detection gear 2, until the detection gear 2 is completed. The first valid data is also collected, which is called a round of valid data collection. Then the second first valid data is collected in the detection gear 1. The maximum value N in the equation is obtained, and so on for n rounds ( ) The first valid data collection, until the amount of the first valid data of each detection gear In each round of first valid data collection, if The first valid data of a detection gear is When the detection gear skips the first valid data collection, for example, during the collection process, after the sixth round of valid data collection, the detection gear The amount of the first valid data has reached ,and However, it is necessary to continue the 7th round of valid data collection. In the 7th round of valid data collection, the gear position is detected. No passive induced signals are detected, and the first valid data is not collected. Figure 3 Taking the SIP signal as an example to meet both data stability and gear matching, the gear rotation acquisition method is explained First, the SIP signal of a certain detection gear is collected, and the judgment process of data stability and gear matching is as described above, which will not be repeated here; when the first detection unit determines that the SIP signal meets the validity requirements, it is necessary to continue to determine whether the number of first valid data of the current gear has met the corresponding If yes, the current gear will no longer collect data, if no, enter the next detection gear to collect data; determine whether to perform n rounds of valid data collection, if yes (or N groups of first valid data have been collected or any detection gear meets the requirements If the number of valid data is greater than 100, the first valid data will be collected, otherwise, the next test gear will be entered for data collection. Gear rotation collection can collect M gears in one round and calculate a set of fitting coefficients. According to the stability of the measurement results of multiple sets of fitting coefficients, the measurement process can be terminated at any time to reduce the collection time. Figure 5 Each round of SIP signal acquisition includes M detection gears, each gear represents the SIP area, and the unit is mV*ms. At the beginning, acquisition starts from detection gear 30, and then detection gears 100, 45, 125, 55, 250, 70, 350, and 80 are acquired in sequence according to the gear sequence. Rest is required before and after the maximum contraction (i.e., detection gear 500) to avoid the influence of muscle fatigue on the results. One round of acquisition is completed after the detection gear 500 is acquired.

[0040] As an optional implementation of the limit setting unit.

[0041] Since the data values ​​collected for each gear are different, the number of gears required for stability is different, and each gear is set with a corresponding value, can reduce the number of data collection and reduce the collection time; The value represents the minimum number of stable gears in the current gear. The measurement result calculated by using the data is stable. Each detection gear needs to detect data validity. The amount of the first valid data in each detection gear is determined based on whether the stability of the detection gear meets the third set threshold. Therefore, the amount of the first valid data in each detection gear is can be the same or different, and is not a uniform value. Therefore, the measurement system also includes a limit setting unit to set the The first valid data in the detection gear value; the limit setting unit comprises: according to the the waveform area of ​​the passive induced signal in the first detection gear; the number of passive induced signals is increased successively; it is determined whether the stability of the waveform area meets the third set threshold; if so, the first The first valid data in the detection gear If not, the number of passive induced signals is continuously increased until the stability of the mean value satisfies the third set threshold. Optionally, the passive induced signal can be a historical signal or an online signal.

[0042] As an optional implementation of the second detection unit.

[0043] The second detection unit includes: obtaining the peak-to-peak amplitude of the waveform and the relative increment of the stimulation intensity; determining whether the relative increment meets the fourth set threshold; if so, taking the current waveform as the maximum waveform of the second valid data; if not, continuing to increase the stimulation intensity until the maximum waveform is obtained; if the maximum waveform is still not obtained by continuing to increase the stimulation intensity to the stimulation intensity threshold, taking the waveform corresponding to the stimulation intensity threshold as the maximum waveform of the second valid data. For details, see Figure 4 , taking the maximum wave of the compound muscle action potential (CMAP) as an example, the maximum CMAP waveform includes the following processing steps.

[0044] (1) According to the waveform recognition algorithm, the collected CMAP wave data is full-wave rectified, and peak detection is performed after the stimulation artifact to automatically extract the CMAP waveform peak. CMAP wave features include but are not limited to: area, power, etc.

[0045] (2) Calculate the peak-to-peak amplitude and draw an amplitude-stimulation intensity curve. Use the slope of the amplitude-stimulation intensity curve as a reference for increasing the stimulation intensity. When the slope is large, a larger stimulation intensity interval should be used and the number of stimulations should be reduced. When the slope is small, it means that the maximum CMAP wave is approaching and the stimulation intensity interval should be reduced. Before each stimulation, determine whether the stimulation intensity is lower than the stimulation intensity threshold. If it is not lower than the stimulation intensity threshold, use the stimulation intensity threshold as the stimulation intensity to collect the maximum CMAP wave.

[0046] (3) Starting from the second stimulation, the CMAP amplitude and the relative increment of the stimulation intensity are calculated to determine whether the stimulation intensity and relative increment of the two stimulations meet the requirements (for example, the difference between the two stimulation intensities is > 0, and the relative increment is < the fourth threshold). If so, the stimulation intensity is increased to a super-strong stimulation to determine the maximum CMAP wave. If it does not meet the requirements, the stimulation intensity is increased and the data is collected until the maximum CMAP wave is found.

[0047] (4) Before each stimulation, the stimulation intensity needs to be compared with the set threshold to ensure that the stimulation intensity does not exceed the safe stimulation current and to ensure the safety of the subject. In the case of exceeding the stimulation intensity threshold, the stimulation intensity is set to the same value as the stimulation intensity threshold to obtain the maximum CMAP waveform.

[0048] The second detection unit of this embodiment calculates the relative increment of the active induced signal amplitude and the stimulation intensity to determine whether it is less than the intensity threshold. If it meets the requirements, the stimulation is continued to be increased to super-strong stimulation to obtain the maximum active induced signal. In addition, each stimulation must ensure that the stimulation intensity does not exceed the safe stimulation current to ensure the safety of the subject. In the case of exceeding the intensity threshold, the stimulation intensity is set to the same value of the intensity threshold to obtain the maximum active induced signal.

[0049] As an optional implementation of the data analysis unit.

[0050] The data analysis unit is based on The measurement result of the number of motion units obtained by combining the first valid data with the second valid data includes: obtaining the area of ​​the maximum waveform of the second valid data and power ; Get The waveform area of ​​the first valid data and power ; Get The number of motion units corresponding to valid data ;according to The plurality of groups of fitting coefficients are obtained based on the number of motion units; and the measurement results of the number of motion units are obtained based on the plurality of groups of fitting coefficients.

[0051] Optionally, the basis Valid data acquisition The number of motion units includes: In the detection position The maximum value of , collect the first valid data of n rounds, then , ; Determine n and any detection gear Value size, if ,at this time The total number of first valid data of the detection gear ;like , No. The detection gear skips the acquisition of passive induced signals. The total number of first valid data of the detection gear , then the The number of motion units in the detection gear is supplemented to That is, to supplement the Optionally, the additional number of motion units may be the number of motion units from the 1st to the nth in the detection gear. The mean number of motor units.

[0052] Optional, according to The number of motion units to obtain multiple sets of fitting coefficients includes: The number of motion units of each detection gear is set to the same group to obtain n groups of data; and n groups of fitting coefficients are obtained based on the n groups of data.

[0053] Optionally, obtaining N groups of fitting coefficients based on N groups of data includes: calculating the ICMUCs of N groups of M gears according to the area and power of the maximum CMAP wave, the SIP area and power of the N groups of M gears; drawing a (SIP area-ICMUC) graph with the SIP area on the horizontal axis and the ICMUC on the vertical axis according to the sample points of SIP area-ICMUC, and drawing one graph for each group, for a total of N groups; fitting the IMUC and SIP area of ​​the M gears of each group, to obtain a total of N groups of fitting coefficients.

[0054] Optionally, the method of obtaining the measurement result of the number of motor units based on multiple sets of fitting coefficients includes: due to ICMUC and Inverse functional relationship, set , the number of motion units , represents the first coefficient in the fitting coefficients, represents the second coefficient in the fitting coefficient; let Get the measurement result of the number of moving units , The set waveform area representing the first valid data, for example According to the empirical value of 20, we get , as the final estimate of the number of motion units.

[0055] In this case, since the first coefficient in the fitting coefficients and the second coefficient Both groups and are corresponding, so when obtaining the measurement results of the number of motion units When The measurement result of the number of motor units is calculated by the mean of the group fitting coefficients, which can also be calculated based on Group( ) The fitting coefficients are used to calculate the measurement results of the number of motor units in each group and then take the average. Taking the average value through multiple calculations can effectively improve the reliability of MUNIX and ensure the accuracy of the results. The difference between the two also leads to differences in the collection process and application scenarios of effective data. The mean of the group fitting coefficients is used to calculate the measurement results of the number of motor units. This method requires first obtaining all The fitting coefficient is usually determined by historical data. The fitting coefficient can be calculated during the acquisition process or after all acquisitions are completed. However, the measurement results of the number of motion units must be equal. All the fitting coefficients of the group are obtained, so the effective data collection method is not much different between the gear rotation collection and the gear collection one by one, which is not suitable for calculating the measurement results while collecting. The group fitting coefficients are used to calculate the measurement results of each group of motion units and then take the average. This method is more suitable for calculating the measurement results while collecting data, and is more compatible with the gear rotation collection. After collecting a round of first valid data, it can be combined with the second valid data to obtain the measurement result. The number of collection rounds of the first valid data (or the number of groups of fitting coefficients or the N value) can be adjusted at any time. For example, the reliability of the measurement result can be directly judged based on the stability of the multiple groups of fitting coefficients that have been obtained, and it is decided whether to continue collecting the first valid data. The solution is flexible and adaptable.

[0056] In addition, the set thresholds involved in this case, for example, the first set threshold for determining the data stability of the first valid data, the second set threshold for determining the gear matching degree of the first valid data, and the data volume of the first valid data in the gear detection are set. The third set threshold for determining the relative increment and the fourth set threshold for determining the relative increment can all be set based on experience or obtained based on historical data, and can also be corrected online through a machine learning model.

[0057] In summary, this embodiment 1 is a measurement of the number of motor units achieved by electromyographic induced electric signals, and two types of electromyographic induced electric signals are used for joint measurement, namely active induced signals and passive induced signals. The active induced signal uses the maximum waveform signal, and the passive induced signal uses the induced signals of different gears. The active induced signal is generated by external stimulation, detected by the second detection unit in the measurement system, and the maximum waveform signal of the active induced signal is obtained according to the relative increment of the peak-to-peak amplitude of the obtained waveform and the stimulation intensity; the passive induced signal is generated by autonomous movement, detected by the first detection unit in the measurement system, and the passive induced signal that meets the requirements is obtained according to the data stability detection module and / or the gear matching verification module in the first detection unit, and the gear required for collection is set according to the gear setting unit, and the collection of passive induced signals under all gears is completed. In the data analysis unit, relevant features are obtained for the collected active induced signals and passive induced signals, and the features are fitted. According to the obtained fitting coefficient, the motor unit number index is calculated to achieve the measurement of the number of motor units.

[0058] Example 2.

[0059] Based on Example 1, this Example 2 provides an electromyographic evoked potential instrument, including: an acquisition system, including multiple acquisition units, respectively used to acquire active evoked signals and passive evoked signals; the measurement system, used to output the measurement results of the number of motor units; and an electrical stimulation device, which outputs electrical stimulation for active evoked signals.

[0060] Example 3.

[0061] Based on Example 1, this Example 3 provides a method for measuring the number of motion units. The measurement result of the number of motion units is based on The first valid data and the second valid data are jointly obtained. .

[0062] Example 4.

[0063] On the basis of Example 3, this Example 4 provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the motion unit number measurement method are implemented.

[0064] In the description of the embodiments of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0065] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0066] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0067] Based on the above ideal embodiments of the present invention, the relevant staff can make various changes and modifications without departing from the technical concept of the present invention through the above description. The technical scope of the present invention is not limited to the contents of the specification, and its technical scope must be determined according to the scope of the claims.

Claims

1. A system for measuring the number of motion units, comprising: Gear setting unit, set in the gear of the measuring system a detection gear; a processor, on which a first detection unit, a second detection unit, and a data analysis unit are arranged; wherein the first detection unit is in the Detected in the detection gear The second detection unit detects valid data of a passive induced signal, namely, the first valid data; the second detection unit detects valid data of an active induced signal, namely, the second valid data; the data analysis unit is based on The first valid data and the second valid data are combined to obtain the measurement result of the number of motion units, including: obtaining the area of ​​the maximum waveform of the second valid data and power ; Get The waveform area of ​​the first valid data and power ; Get Number of motor units; The method further comprises: obtaining a plurality of sets of fitting coefficients based on the number of motion units; obtaining the measurement results of the number of motion units based on the plurality of sets of fitting coefficients; and further comprising: Limit setting unit, set in the In the detection gear The maximum limit of is not a uniform value; Parameter setting interface, set The order of collecting the first valid data is to collect the gears in turn, that is, take M detection gears as one round of valid data collection, and collect at most one first valid data for each detection gear in each round of valid data collection, and give priority to forming a single group of first valid data; The acquisition The number of motion units includes: Pick In the detection gear The maximum value of , collect the first valid data of n rounds, then , ; Determine n and any detection gear Value size, if ,at this time The total number of first valid data of the detection gear ;like , No. The detection gear skips the acquisition of passive induced signals. The total number of first valid data of the detection gear , then the The number of motion units in the detection gear is supplemented to indivual; according to The number of motion units to obtain multiple sets of fitting coefficients includes: Will cover The number of motion units for each detection gear is set to the same group, and n groups of data are obtained; Obtain n groups of fitting coefficients based on n groups of data; The limit setting unit comprises: According to The waveform area of ​​the passively induced signal in each detection gear; The number of passive induced signals is increased successively; Determine whether the stability of the waveform area meets the third set threshold; if so, The first valid data in the detection gear The value is set to the number of current passive induced signals; if not, the number of passive induced signals continues to increase until the stability of the mean meets the third set threshold.

2. The measuring system according to claim 1, characterized in that The first detection unit comprises: A data stability detection module is configured to detect data stability, that is, whether the characteristic value of the passive induced signal meets a first set threshold value of the current detection gear; The gear matching verification module is configured to detect the gear matching degree, that is, whether the waveform area of ​​the passive induced signal meets the second set threshold of the current detection gear.

3. The measuring system according to claim 2, characterized in that The data stability detection module comprises: The passive induced signal of a certain detection gear is divided to obtain multiple segments of data; Determine whether the data characteristics of the current segment of data meet the first set threshold; if so, the current data is counted as the first valid data and the next data is collected according to the set collection method; if not, it is not counted and the passive induced signal is continued to be collected in the current detection gear.

4. The measuring system according to claim 2, characterized in that The gear matching verification module includes: Set the gear value threshold of each detection gear; Obtain the waveform area of ​​the passive induced signal in the current detection gear; Determine whether the waveform area meets the gear value threshold of the current detection gear; if so, the current data is counted as the first valid data and the next data is collected according to the set collection method; if not, it is not counted and the passive induced signal is continued to be collected in the current detection gear.

5. The measuring system according to claim 1, characterized in that The measurement results of calculating the number of motion units in each group according to the n groups of fitting coefficients include: set up , the number of motion units , represents the first coefficient in the fitting coefficients, represents the second coefficient in the fitting coefficient; let Get the measurement result of the number of moving units , Indicates the set waveform area of ​​the first valid data.

6. The measuring system according to claim 1, characterized in that The second detection unit comprises: Obtain the peak-to-peak amplitude of the waveform and the relative increment of the stimulus intensity; Determine whether the relative increment meets the fourth set threshold; if so, take the current waveform as the maximum waveform of the second valid data; if not, continue to increase the stimulation intensity until the maximum waveform is obtained; if the maximum waveform is still not obtained by continuing to increase the stimulation intensity to the stimulation intensity threshold, take the waveform corresponding to the stimulation intensity threshold as the maximum waveform of the second valid data.

7. The measuring system according to claim 1, characterized in that when When the measurement result of the number of motion units is configured to calculate the measurement results of the number of motion units of each group according to the n groups of fitting coefficients and then take the average.

8. A myoelectric evoked potential instrument, characterized in that: include: An acquisition system, including a plurality of acquisition units, respectively used for acquiring active induced signals and passive induced signals; The measurement system of claim 1, configured to output a measurement result of the number of motion units; An electrical stimulation device outputs electrical stimulation for actively inducing signals.

9. A method for measuring the number of motion units using the measuring system according to claim 1, characterized in that: The number of motor units is measured based on The first valid data and the second valid data are jointly obtained. .

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for measuring the number of motion units as claimed in claim 1 are implemented.

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