Intramuscular EMG decomposition basing on motor unit action potentials detection and superposition resolution

Xiaomei Ren, Chuan Zhang, Xuhong Li, Gang Yang, Thomas Potter, Yingchun Zhang

Research output: Contribution to journalArticle

6 Scopus citations

Abstract

A novel electromyography (EMG) signal decomposition framework is presented for the thorough and precise analysis of intramuscular EMG signals. This framework first detects all of the active motor unit action potentials (MUAPs) and assigns single MUAP segments to their corresponding motor units. MUAP waveforms that are found to be superimposed are then resolved into their constituent single MUAPs using a peel-offapproach and similarly assigned. The method is composed of six stages of analytical procedures: preprocessing, segmentation, alignment and feature extraction, clustering and refinement, supervised classification, and superimposed waveform resolution. The performance of the proposed decomposition framework was evaluated using both synthetic EMG signals and real recordings obtained from healthy and stroke participants. The overall detection rate of MUAPs was 100% for both synthetic and real signals. The average accuracy for synthetic EMG signals was 87.23%. Average assignment accuracies of 88.63 and 94.45% were achieved for the real EMG signals obtained from healthy and stroke participants, respectively. Results demonstrated the ability of the developed framework to decompose intramuscular EMG signals with improved accuracy and efficiency, which we believe will greatly benefit the clinical utility of EMG for the diagnosis and rehabilitation of motor impairments in stroke patients.

Original languageEnglish (US)
Article number2
JournalFrontiers in Neurology
Volume9
Issue numberJAN
DOIs
StatePublished - Jan 23 2018

Keywords

  • EMG decomposition
  • Minimum spanning tree
  • Pseudo-correlation measure
  • Segments detection
  • Superposition waveform resolution

ASJC Scopus subject areas

  • Neurology
  • Clinical Neurology

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