Myoelectric Pattern Identification of Stroke Survivors Using Multivariate Empirical Mode Decomposition

Zhang, Xu; Zhou, Ping

doi:https://doi.org/10.1260/2040-2295.5.3.261

Journal of Healthcare Engineering

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Research Article | Open Access

Volume 5 | Article ID 209017 | https://doi.org/10.1260/2040-2295.5.3.261

Myoelectric Pattern Identification of Stroke Survivors Using Multivariate Empirical Mode Decomposition

Xu Zhang¹and Ping Zhou^1,2

Received01 Aug 2013

Accepted01 Apr 2014

Abstract

This study presents a novel feature extraction method for myoelectric pattern recognition using a multivariate extension of empirical mode decomposition (EMD), namely multivariate EMD (MEMD). The method processes multiple surface electromyogram (EMG) channels simultaneously rather than in a channel-by-channel manner. From mode-aligned intrinsic mode functions (IMFs, representing signal components over multiple scales) derived from the MEMD analysis, normalized amplitude distributions of the same-mode/scale IMFs across different channels were calculated as features, which serve to reveal the underlying relationship in the aligned intrinsic scales across multiple muscles. The proposed method was assessed for identification of 18 different functional movement patterns via 27-channel surface EMG signals recorded from the paretic forearm muscles of 12 subjects with hemiparetic stroke. With a linear discriminant classifier, the proposed MEMD based feature set resulted in an average error rate of 4.61 ± 4.70% for classification of all the different movements, significantly lower than that of the conventional time-domain feature set (7.14 ± 6.15%, p < 0.05). The results indicate that the MEMD based feature extraction of multi-channel surface EMG data provides a promising approach to modeling of muscle couplings and identification of different myoelectric patterns.

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Copyright © 2014 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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