Task-oriented intelligent solution to measure Parkinson’s disease tremor severity

AlMahadin, G ORCID: https://orcid.org/0000-0001-9244-1054, Lotfi, A ORCID: https://orcid.org/0000-0002-5139-6565, McCarthy, M and Breedon, P ORCID: https://orcid.org/0000-0002-1006-0942, 2021. Task-oriented intelligent solution to measure Parkinson’s disease tremor severity. Journal of Healthcare Engineering, 2021: 9624386. ISSN 2040-2295

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Abstract

Tremor is a common symptom of Parkinson’s disease (PD). Currently, tremor is evaluated clinically based on MDS-UPDRS Rating Scale, which is inaccurate, subjective, and unreliable. Precise assessment of tremor severity is the key to effective treatment to alleviate the symptom. Therefore, several objective methods have been proposed for measuring and quantifying PD tremor from data collected while patients performing scripted and unscripted tasks. However, up to now, the literature appears to focus on suggesting tremor severity classification methods without discrimination tasks effect on classification and tremor severity measurement. In this study, a novel approach to identify a recommended system is used to measure tremor severity, including the influence of tasks performed during data collection on classification performance. The recommended system comprises recommended tasks, classifier, classifier hyperparameters, and resampling technique. The proposed approach is based on the above-average rule of five advanced metrics results of four subdatasets, six resampling techniques, six classifiers besides signal processing, and features extraction techniques. The results of this study indicate that tasks that do not involve direct wrist movements are better than tasks that involve direct wrist movements for tremor severity measurements. Furthermore, resampling techniques improve classification performance significantly. The findings of this study suggest that a recommended system consists of support vector machine (SVM) classifier combined with BorderlineSMOTE oversampling technique and data collection while performing set of recommended tasks, which are sitting, stairs up and down, walking straight, walking while counting, and standing.

Item Type:	Journal article
Publication Title:	Journal of Healthcare Engineering
Creators:	AlMahadin, G., Lotfi, A., McCarthy, M. and Breedon, P.
Publisher:	Hindawi Limited
Date:	10 September 2021
Volume:	2021
ISSN:	2040-2295
Identifiers:	Number Type 10.1155/2021/9624386 DOI 1471260 Other
Rights:	Copyright © 2021 Ghayth AlMahadin 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.
Divisions:	Schools > School of Science and Technology Schools > School of Architecture, Design and the Built Environment
Record created by:	Laura Ward
Date Added:	17 Sep 2021 14:43
Last Modified:	17 Sep 2021 15:20
URI:	https://irep.ntu.ac.uk/id/eprint/44204

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