Feature Analysis for Discrimination of Motor Unit Action Potentials

Thuy T. Pham, Diep N. Nguyen, Eryk Dutkiewicz, Alistair L. McEwan, Philip H.W. Leong, Andrew J. Fuglevand

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In electrophysiological signal processing for intramuscular electromyography data (nEMG), single motor unit activity is of great interest. The changes of action potential (MUAP) morphology, motor unit (MU) activation, and recruitment provide the most informative part to study the nature causality in neuromuscular disorders. In practice, for a single nEMG recording, more than one motor unit activities (in the surrounding area of a needle electrode) are usually collected. Such a fact makes the MUAP discrimination that separates single unit activities a crucial task. Most neurology laboratories worldwide still recruit specialists who spend hours to manually or semi-automatically sort MUAPs. From a machine learning perspective, this task is analogous to the clustering-based classification problem in which the number of classes and other class information are unfortunately missing. In this paper, we present a feature analysis strategy to help better utilize unsupervised (i.e., totally automated) methods for MUAP discrimination. To that end, we extract a large pool of features from each MUAP. Then we select the top ranked candidates using clusterability scores as selection criteria. We found spectrograms of wavelet decomposition as a top-ranking feature, highly correlated to the motor unit reference and was more separable than existing features. Using a correlation-based clustering technique, we demonstrate the sorting performance with this feature set. Compared with the reference produced by human experts, our method obtained a comparable result (e.g., equivalent number of classes was found, identical MUAP morphology in each pair of corresponding MU class, and similar histograms of MUs). Taking the manual labels as references, our method got a much higher sensitivity and accuracy than the compared unsupervised sorting method. We obtained a similar result in MUAP classification to the reference.

Original languageEnglish (US)
Title of host publication12th International Symposium on Medical Information and Communication Technology, ISMICT 2018
PublisherIEEE Computer Society
ISBN (Electronic)9781538633892
DOIs
StatePublished - Dec 11 2018
Event12th International Symposium on Medical Information and Communication Technology, ISMICT 2018 - Sydney, Australia
Duration: Mar 26 2018Mar 28 2018

Publication series

NameInternational Symposium on Medical Information and Communication Technology, ISMICT
Volume2018-March
ISSN (Print)2326-828X
ISSN (Electronic)2326-8301

Other

Other12th International Symposium on Medical Information and Communication Technology, ISMICT 2018
CountryAustralia
CitySydney
Period3/26/183/28/18

Keywords

  • Spike sorting
  • feature learning

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Health Informatics
  • Health Information Management

Fingerprint Dive into the research topics of 'Feature Analysis for Discrimination of Motor Unit Action Potentials'. Together they form a unique fingerprint.

  • Cite this

    Pham, T. T., Nguyen, D. N., Dutkiewicz, E., McEwan, A. L., Leong, P. H. W., & Fuglevand, A. J. (2018). Feature Analysis for Discrimination of Motor Unit Action Potentials. In 12th International Symposium on Medical Information and Communication Technology, ISMICT 2018 [8573738] (International Symposium on Medical Information and Communication Technology, ISMICT; Vol. 2018-March). IEEE Computer Society. https://doi.org/10.1109/ISMICT.2018.8573738