Electromyogram decomposition via unsupervised dynamic multi-layer neural network

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.<>