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Updated: Jan 9, 2026

Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography

Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography

Published on: January 24, 2025

在没有休息试验的情况下,基于机器学习的EMG基线噪声标准偏差估计.

Naisargi Mehta¹, Bashima Islam¹, Edward A Clancy¹

¹Department of Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, MA, USA.

Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology

|December 9, 2025

概括

此摘要是机器生成的。

在表面电肌图 (EMG) 中估计静止噪声至关重要. 一种新的机器学习 (ML) 方法和固定值为准确的EMG分析提供了传统静态测量的可行替代方案.

关键词:

基线噪声估计的基线噪声估计.估计EMG振幅的估计.电肌图 (EMG) 是一种电子肌图.机器学习机器学习噪声纠正噪声纠正信号处理信号处理表面电肌图 (sEMG) 是一种表面电肌图.

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Last Updated: Jan 9, 2026

Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography

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Published on: January 24, 2025

Determining the Functional Status of the Corticospinal Tract Within One Week of Stroke

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Published on: February 22, 2020

Determining The Electromyographic Fatigue Threshold Following a Single Visit Exercise Test

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Published on: July 27, 2015

科学领域:

生物医学工程生物医学工程
信号处理信号处理
神经科学是一个神经科学.

背景情况:

在表面电肌图 (EMG) 中精确估计静止噪声标准偏差 (σnoise) 对于可靠的振幅估计至关重要,特别是在低级收缩中.
传统方法需要明确的静态EMG记录,这在现实应用中并不总是可行的.

研究的目的:

为了比较一种新型机器学习 (ML) 方法的准确性和固定噪声值与直接的静态测量来估计噪声.
评估这些方法在降低基线EMG噪声方面的有效性.

主要方法:

比较了直接的静态噪声测量与一个在模拟和真实EMG数据上训练的ML模型,来自62个受试者的三个系统.
作为第三种比较方法,使用了3%的最大自愿EMG (MVE) 固定的snoise值.
在单独的评估中评估了降噪性能.

主要成果:

基于ML的方法显示,与静止式噪声的MVE中位数绝对差异为1.4%.
固定噪声方法的中位数绝对差异为1.71%的MVE,与ML方法没有显著差异.
无论是ML方法还是固定噪声方法都实现了45%的噪声降低,而休息校准实现了75%的降低.

结论:

机器学习和固定的snoise值是用于EMG噪声估计的直接静态测量的可行的替代方案.
这些方法在缺乏明确休息数据的环境中提高了EMG分析的可靠性.
进一步的研究可能会优化这些替代噪声估计技术.