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表面肌电信号在等长爆发性和斜坡收缩中的起始检测:基于计算机的方法比较。

Onset detection in surface electromyographic signals across isometric explosive and ramped contractions: a comparison of computer-based methods.

机构信息

Biomechanics Research Unit, Department of Physical Education and Sport Sciences, University of Limerick, Limerick, Ireland.

School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, United Kingdom.

出版信息

Physiol Meas. 2021 Apr 12;42(3). doi: 10.1088/1361-6579/abef56.

Abstract

. Accurate identification of surface electromyography (EMG) muscle onset is vital when examining short temporal parameters such as electromechanical delay. The visual method is considered the 'gold standard' in onset detection. Automatic detection methods are commonly employed to increase objectivity and reduce analysis time, but it is unclear if they are sensitive enough to accurately detect EMG onset when relating them to short-duration motor events.. This study aimed to determine: (1) if automatic detection methods could be used interchangeably with visual methods in detecting EMG onsets (2) if the Teager-Kaiser energy operator (TKEO) as a conditioning step would improve the accuracy of popular EMG onset detection methods. The accuracy of three automatic onset detection methods: approximated generalized likelihood ratio (AGLR), TKEO, and threshold-based method were examined against the visual method. EMG signals from fast, explosive, and slow, ramped isometric plantarflexor contractions were evaluated using each technique.. For fast, explosive contractions, the TKEO was the best-performing automatic detection method, with a low bias level (4.7 ± 5.6 ms) and excellent intraclass correlation coefficient (ICC) of 0.993, however with wide limits of agreement (LoA) (-6.2 to +15.7 ms). For slow, ramped contractions, the AGLR with TKEO conditioning was the best-performing automatic detection method with the smallest bias (11.3 ± 32.9 ms) and excellent ICC (0.983) but produced wide LoA (-53.2 to +75.8 ms). For visual detection, the inclusion of TKEO conditioning improved inter-rater and intra-rater reliability across contraction types compared with visual detection without TKEO conditioning.. In conclusion, the examined automatic detection methods are not sensitive enough to be applied when relating EMG onset to a motor event of short duration. To attain the accuracy needed, visual detection is recommended. The inclusion of TKEO as a conditioning step before visual detection of EMG onsets is recommended to improve visual detection reliability.

摘要

. 准确识别表面肌电图(EMG)肌肉起始对于检查电机械延迟等短时间参数至关重要。视觉方法被认为是起始检测的“金标准”。自动检测方法通常用于提高客观性和减少分析时间,但尚不清楚在将其与短持续时间运动事件相关联时,它们是否足够敏感以准确检测 EMG 起始。本研究旨在确定:(1)自动检测方法是否可以与视觉方法互换使用以检测 EMG 起始;(2)作为预处理步骤的 Teager-Kaiser 能量算子(TKEO)是否会提高流行的 EMG 起始检测方法的准确性。使用每种技术评估了快速、爆发性和缓慢、斜坡等速足底屈肌收缩的三种自动起始检测方法:近似广义似然比(AGLR)、TKEO 和基于阈值的方法的准确性。. 对于快速、爆发性收缩,TKEO 是表现最好的自动检测方法,具有较低的偏置水平(4.7 ± 5.6 ms)和极好的组内相关系数(ICC)为 0.993,但具有较宽的一致性界限(LoA)(-6.2 至+15.7 ms)。对于缓慢、斜坡收缩,经过 TKEO 预处理的 AGLR 是表现最好的自动检测方法,具有最小的偏置(11.3 ± 32.9 ms)和极好的 ICC(0.983),但产生了较宽的 LoA(-53.2 至+75.8 ms)。对于视觉检测,与没有 TKEO 预处理的视觉检测相比,在包括 TKEO 预处理后,跨收缩类型的观察者间和观察者内可靠性都得到了提高。. 总之,所检查的自动检测方法对于将 EMG 起始与短持续时间的运动事件相关联不够敏感。为了达到所需的准确性,建议进行视觉检测。建议在视觉检测 EMG 起始之前,将 TKEO 作为预处理步骤,以提高视觉检测的可靠性。

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