.png){kind=icon}
返回
A two-stage method for MUAP classification based on EMG decomposition
.png){kind=icon}
PRE
AI
DOI：10.1016/j.compbiomed.2006.11.010.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
A method for the extraction and classification of individual motor unit action potentials (MUAPs) from needle electromyographic signals is presented. The proposed method automatically decomposes MUAPs and classifies them into normal, neuropathic or myopathic using a two-stage feature-based classifier. The method consists of four steps: (i) preprocessing of EMG recordings, (ii) MUAP clustering and detection of superimposed MUAPs, (iii) feature extraction and (iv) MUAP classification using a two-stage classifier. The proposed method employs Radial Basis Function Artificial Neural Networks and decision trees. It requires minimal use of tuned parameters and is able to provide interpretation for the classification decisions. The approach has been validated on real EMG recordings and an annotated collection of MUAPs. The success rate for MUAP clustering is 96%, while the accuracy for MUAP classification is about 89%. (C) 2006 Elsevier Ltd. All rights reserved.
Keyword:
quantitative electromyography
electromyogram decomposition
MUAP detection and classification
radial basis function network
decision trees
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.3
论文数:
8.3K
被引数:
3.3W
机构
暂无机构信息
引用论文
.png){kind=icon}
查看更多内容