Suppr超能文献

[运用自回归模型通过针极肌电图信号分析损伤神经]

[Using AR model to analyze injured nerve with needle EMG signal].

作者信息

Qin Chuan, Wang Zhizhong, Wang Gang, Ma Bo

机构信息

Department of Biomedical Engineering, Shanghai Jiaotong University, Shanghai 200030, China.

出版信息

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2004 Aug;21(4):636-9.

Abstract

The two main factors to affect the style of the recruitment are temporal recruitment and spatial recruitment. This study sought a new way to analyze the recruitment with the modern spectrum method. The abnormal spatial recruitment and temporal recruitment of varied injury degrees of intramuscular neuron were compared through the AR model. At last, AR coefficients were extracted and passed through BP artificial neuron network to classify different NEMG signals and good result was gained.

摘要

影响募集方式的两个主要因素是时间募集和空间募集。本研究寻求一种用现代频谱方法分析募集的新途径。通过自回归(AR)模型比较了不同损伤程度的肌内神经运动单位的异常空间募集和时间募集。最后,提取AR系数并通过BP人工神经网络对不同的神经肌电图(NEMG)信号进行分类,取得了良好的效果。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验