A New Evolutionary Algorithm-Based Home Monitoring Device for Parkinson's Dyskinesia

Michael Adam Lones, Jane E. Alty, Jeremy Cosgrove, Philippa Duggan-Carter, Stuart Jamieson, Rebecca F. Naylor, Andrew J. Turner, Stephen L. Smith

Research output: Contribution to journalArticle

4 Citations (Scopus)
18 Downloads (Pure)

Abstract

Parkinson’s disease (PD) is a neurodegenerative movement disorder. Although there is no cure, symptomatic treatments are available and can significantly improve quality of life. The motor, or movement, features of PD are caused by reduced production of the neurotransmitter dopamine. Dopamine deficiency is most often treated using dopamine replacement therapy. However, this therapy can itself lead to further motor abnormalities referred to as dyskinesia. Dyskinesia consists of involuntary jerking movements and muscle spasms, which can often be violent. To minimise dyskinesia, it is necessary to accurately titrate the amount of medication given and monitor a patient’s movements. In this paper, we describe a new home monitoring device that allows dyskinesia to be measured as a patient goes about their daily activities, providing information that can assist clinicians when making changes to medication regimens. The device uses a predictive model of dyskinesia that was trained by an evolutionary algorithm, and achieves AUC>0.9 when discriminating clinically significant dyskinesia.
Original languageEnglish
Article number176
Number of pages8
JournalJournal of Medical Systems
Volume41
Issue number11
Early online date25 Sep 2017
DOIs
Publication statusPublished - Nov 2017

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