NDLI: Electromyogram decomposition via unsupervised dynamic multi-layer neural network

Content Provider	IEEE Xplore Digital Library
Author	Hassoun, M.H. Wang, C. Spitzer, A.R.
Copyright Year	1992
Description	Author affiliation: Wayne State Univ., Detroit, MI, USA (Hassoun, M.H.; Wang, C.; Spitzer, A.R.)
Abstract	A signal decomposition method which utilizes a multi-layer dynamic network to automatically decompose a clinical electromyogram (EMG), without supervision, is proposed. Due to the lack of a priori knowledge of motor unit potential (MUP) morphology, the EMG decomposition must be performed in an unsupervised manner. A neural network classifier, consisting of a multi-layer neural net of perceptrons and using an unsupervised training strategy, is proposed. The neural network learns repetitive appearances of MUP waveforms from their suspected occurrence in a given filtered EMLG signal by using an unsupervised clustering strategy. Upon training, the network creates stable attractors which correspond to nominal representations of MUP clusters hidden in the data. The decomposition/clustering capabilities of the proposed method are validated on a real EMG signal and on an unlabeled signal set.
Starting Page	405
Ending Page	412
File Size	566546
Page Count	8
File Format	PDF
ISBN	0780305590
DOI	10.1109/IJCNN.1992.226954
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1992-06-07
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Multi-layer neural network Electromyography Neural networks Synchronous motors Muscles Diseases Morphology Nervous system Signal resolution Signal processing
Content Type	Text
Resource Type	Article

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