Wavelet Packet Feature Assessment for High-density Myoelectric Pattern Recognition and Channel Selection toward Stroke Rehabilitation

No clue how your doctor can use this to update your stroke protocols.
http://journal.frontiersin.org/article/10.3389/fneur.2016.00197/abstract

Dongqing Wang¹, Xu Zhang^1*, Xiaoping Gao², Xiang Chen¹ and Ping Zhou^{3, 4}

• ¹Department of Electronic Science and Technology, Unversity of Science and Technology of China, China
• ²Department of Rehabilitation Medicine, First Affiliated Hospital of Anhui Medical University, China
• ³Guangdong Provincial Work Injury Rehabilitation Center, China
• ⁴Department of Physical Medicine and Rehabilitation, University of Texas Health Science Center at Houston, USA

This study presented wavelet packet feature assessment of neural control information in paretic upper-limb muscles of stroke survivors for myoelectric pattern recognition, taking advantage of high-resolution time-frequency representations of surface electromyographic (EMG) signals. On this basis, a novel channel selection method was developed by combining the Fisher's class separability index (FCSI) and the sequential feedforward selection (SFS) analyses, in order to determine a small number of appropriate EMG channels from original high-density EMG electrode array. The advantages of the wavelet packet features and the channel selection analyses were further illustrated by comparing with previous conventional approaches, in terms of classification performance when identifying 20 functional arm/hand movements implemented by 12 stroke survivors. This study offers a practical approach including paretic EMG feature extraction and channel selection that enables active myoelectric control of multiple degrees of freedom with paretic muscles. All these efforts will facilitate upper-limb dexterity restoration and improved stroke rehabilitation.

Keywords: myoelectric control, pattern recognition, Wavelet packet transform, channel selection, stroke rehabilitation
Citation: Wang D, Zhang X, Gao X, Chen X and Zhou P (2016). Wavelet Packet Feature Assessment for High-density Myoelectric Pattern Recognition and Channel Selection toward Stroke Rehabilitation. Front. Neurol. 7:197. doi: 10.3389/fneur.2016.00197