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Complexity
Volume 2017, Article ID 4327175, 17 pages
https://doi.org/10.1155/2017/4327175
Research Article

New Perspectives for Computer-Aided Discrimination of Parkinson’s Disease and Essential Tremor

1Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia
2Faculty of Health Sciences, University of Maribor, Maribor, Slovenia
3Centre for Automation and Robotics, CSIC-UPM, 28500 Madrid, Spain
4Neurorehabilitation and Brain Damage Research Group, Experimental Sciences School, Universidad Francisco de Vitoria, Madrid, Spain
5Brain Damage Unit, Hospital Beata María Ana, Madrid, Spain
6Department of Neurology, University Hospital 12 de Octubre, Madrid, Spain
7Center of Biomedical Network Research on Neurodegenerative Diseases (CIBERNED), Madrid, Spain
8Department of Medicine, Faculty of Medicine, Complutense University, Madrid, Spain
9Clinical Research Unit, University Hospital 12 de Octubre, Madrid, Spain

Correspondence should be addressed to A. Holobar; is.mu@raboloh.sela

Received 12 May 2017; Revised 31 July 2017; Accepted 12 September 2017; Published 19 October 2017

Academic Editor: Valeri Mladenov

Copyright © 2017 P. Povalej Bržan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Pathological tremor is a common but highly complex movement disorder, affecting ~5% of population older than 65 years. Different methodologies have been proposed for its quantification. Nevertheless, the discrimination between Parkinson’s disease tremor and essential tremor remains a daunting clinical challenge, greatly impacting patient treatment and basic research. Here, we propose and compare several movement-based and electromyography-based tremor quantification metrics. For the latter, we identified individual motor unit discharge patterns from high-density surface electromyograms and characterized the neural drive to a single muscle and how it relates to other affected muscles in 27 Parkinson’s disease and 27 essential tremor patients. We also computed several metrics from the literature. The most discriminative metrics were the symmetry of the neural drive to muscles, motor unit synchronization, and the mean log power of the tremor harmonics in movement recordings. Noteworthily, the first two most discriminative metrics were proposed in this study. We then used decision tree modelling to find the most discriminative combinations of individual metrics, which increased the accuracy of tremor type discrimination to 94%. In summary, the proposed neural drive-based metrics were the most accurate at discriminating and characterizing the two most common pathological tremor types.