Deans' stroke musings: Wavelet Packet Feature Assessment for High-density Myoelectric Pattern Recognition and Channel Selection toward Stroke Rehabilitation

Changing stroke rehab and research worldwide now.Time is Brain! trillions and trillions of neurons that DIE each day because there are NO effective hyperacute therapies besides tPA(only 12% effective). I have 523 posts on hyperacute therapy, enough for researchers to spend decades proving them out. These are my personal ideas and blog on stroke rehabilitation and stroke research. Do not attempt any of these without checking with your medical provider. Unless you join me in agitating, when you need these therapies they won't be there.

What this blog is for:

My blog is not to help survivors recover, it is to have the 10 million yearly stroke survivors light fires underneath their doctors, stroke hospitals and stroke researchers to get stroke solved. 100% recovery. The stroke medical world is completely failing at that goal, they don't even have it as a goal. Shortly after getting out of the hospital and getting NO information on the process or protocols of stroke rehabilitation and recovery I started searching on the internet and found that no other survivor received useful information. This is an attempt to cover all stroke rehabilitation information that should be readily available to survivors so they can talk with informed knowledge to their medical staff. It lays out what needs to be done to get stroke survivors closer to 100% recovery. It's quite disgusting that this information is not available from every stroke association and doctors group.

Sunday, November 20, 2016

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