Simulation Study on CNN-Based Motor Unit Action Potential Classification Method for Activity Tracking of Motor Unit

LI Yixin; ZHENG Yang

doi:10.7652/xjtuxb202306002

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Simulation Study on CNN-Based Motor Unit Action Potential Classification Method for Activity Tracking of Motor Unit

更新时间：2025-05-08

- Simulation Study on CNN-Based Motor Unit Action Potential Classification Method for Activity Tracking of Motor Unit
- Vol. 57, Issue 6, Pages: 10-17(2023)
- 作者机构：
  
  1. 西安交通大学机械工程学院,西安,710049
  2. 西安交通大学机械制造系统工程国家重点实验室,西安,710049
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202306002
  CLC： R318.04
- Online First：10 June 2023，
  
  Published：2023
- 稿件说明：
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LI Yixin, ZHENG Yang. Simulation Study on CNN-Based Motor Unit Action Potential Classification Method for Activity Tracking of Motor Unit[J]. 2023, 57(6): 10-17.
DOI：

LI Yixin, ZHENG Yang. Simulation Study on CNN-Based Motor Unit Action Potential Classification Method for Activity Tracking of Motor Unit[J]. 2023, 57(6): 10-17. DOI： 10.7652/xjtuxb202306002.

摘要

为了解决在真实条件下运动单元动作电位(MUAP)波形变异导致的MUAP分类识别准确率低

进而降低运动单元(MU)追踪性能的问题

提出了一种基于卷积神经网络(CNN)的MUAP分类方法

并利用仿真数据进行了效果验证。该方法以被称为MU指纹的MUAP波形作为模型输入

结合Keras深度学习框架设计的CNN网络结构简单

并采取Early-stop策略训练网络

可实现不同MU的准确分类和匹配

从而持续追踪给定MU放电活动。所提基于CNN的MU分类方法能够在样本量较小的情况下

即每个MU可获取(54.49±29.28)个MUAP波形样本

对平均(30.23±5.37)个MU进行准确分类

分类准确性达到(89.41%±3.72%)

显著高于现有的基于MUAP波形相似度的方法(49.66%±6.12%)。结果表明

所提方法对MUAP波形变异展现出了更好的鲁棒性

为开展真实条件下MU放电活动持续追踪研究提供重要的技术支撑

对运动控制的神经机制研究等具有重要意义。

Abstract

To solve the problem of low accuracy of motor unit action potential(MUAP)waveform similarity classification method caused by MUAP waveform variation under real conditions

which reduces the tracking performance of the motor unit(MU)

a MUAP classification method based on convolutional neural network(CNN)was proposed and tested using the simulated high-density electromyogram(EMG)recordings in this paper. The network was designed with the Keras deep-learning framework and had a simple structure with the MUAP waveform called MU “fingerprints” as the input. The network was trained using the early-stop strategy in order to realize the classification and matching of different MUs

and thus longitudinal tracking of the discharge activities of given MUs. The results showed that the proposed CNN-based method could reach a classification accuracy of(89.41% ± 3.72%)among(30.23 ± 5.37)MUs on average even when only(54.49 ± 29.28)MUAP waveform samples were available for individual MUs. The performance of the proposed method was significantly higher than the conventional MUAP waveform similarity classification method with an accuracy of(49.66%±6.12%). These results demonstrated that the proposed method was more robust to the variation of MUAP waveforms. The proposed method has the potential to promote the studies in which longitudinal tracking of MU discharge activities is needed

and it is of great significance for multiple studies such as the exploration of the neural mechanisms underlying motor control.

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references

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