New Perspectives for Computer-Aided Discrimination of Parkinson’s Disease and Essential Tremor.
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2017
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Metrics
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 fromhigh-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 individualmetrics, which increased the accuracy of tremor type discrimination to 94%. In summary, the proposed
neural drive-basedmetrics were themost accurate at discriminating and characterizing the two most common pathological tremor
types.