动态收缩时表面肌电信号多重分形的来源分析

K. Marri, R. Swaminathan
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引用次数: 9

摘要

本研究的目的是分析非疲劳和疲劳状态下动态收缩过程中肌表电(sEMG)信号多重分形的来源。记录22例正常健康受试者肱三头肌肌电信号。将信号在时间尺度上分成六个相等的片段进行归一化。将第一段和第六段分别视为非疲劳状态和疲劳状态。多重分形的来源可能是由于相关性和概率分布。将原表面肌电信号序列变换为洗牌序列和替代序列。对非疲劳和疲劳状态下的原始序列、洗牌序列和替代序列进行多重分形去趋势波动分析(MFDFA),提取特征。结果表明,表面肌电信号具有多重分形特征。进一步的研究表明,多重分形的起源主要是由于相关性。在非疲劳条件下,多重分形的成因为80%,在疲劳条件下,多重分形的成因为86%。这种多重分形分析方法可用于分析各种神经肌肉研究中肌肉收缩的进行性变化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analyzing Origin of Multifractality of Surface Electromyography Signals in Dynamic Contractions
The aim of this study is analyze the origin of multifractality of surface electromyography (sEMG) signals during dynamic contraction in nonfatigue and fatigue conditions. sEMG signals are recorded from triceps brachii muscles of twenty two normal healthy subjects. The signals are divided into six equal segments on time scale for normalization. The first and sixth segments are considered as nonfatigue and fatigue condition respectively. The source of multifractality can be due to correlation and probability distribution. The original sEMG series are transformed into shuffled and surrogate series. These three series namely, original, shuffled and surrogate series in nonfatigue and fatigue conditions are subjected to multifractal detrended fluctuation analysis (MFDFA) and features are extracted. The results indicate that sEMG signals exhibit multifractal behavior. Further investigation revealed that origin of multifractality is primarily due to correlation. The origin of multifractality due to correlation is quantified as 80% in nonfatigue and 86% in fatigue conditions. This method of multifractal analysis may be useful for analyzing progressive changes in muscle contraction in varied neuromuscular studies.
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