Classification of the myoelectric signal using time-frequency based representations
Classification of the myoelectric signal using time-frequency based representations
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DOI：
10.1016/s1350-4533(99)00066-1
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发表时间：
1999-07-01
影响因子：
2.2
通讯作者：
Stevenson, M
中科院分区：
文献类型：
--
作者：
Engelhart, K;Hudgins, B;Stevenson, M
近年来，一种准确且计算高效的表面肌电信号模式分类方法一直是大量研究工作的主题。有效的特征提取对于可靠分类至关重要，并且在寻求提高瞬态肌电信号模式分类准确性的过程中，提出了一组基于时频的表示方法。研究表明，基于短时傅里叶变换、小波变换和小波包变换的特征集为分类提供了一种有效的表示形式，前提是它们要经过适当形式的降维处理。（C）1999年IPEM。由爱思唯尔科学有限公司出版。保留所有权利。
An accurate and computationally efficient means of classifying surface myoelectric signal patterns has been the subject of considerable research effort in recent years. Effective feature extraction is crucial to reliable classification and, in the quest to improve the accuracy of transient myoelectric signal pattern classification, an ensemble of time-frequency based representations are proposed. It is shown that feature sets based upon the short-time Fourier transform, the wavelet transform, and the wavelet packet transform provide an effective representation for classification, provided that they are subject to an appropriate form of dimensionality reduction. (C) 1999 IPEM. Published by Elsevier Science Ltd. All rights reserved.