Enhanced Parkinson's disease tremor severity classification by combining signal processing with resampling techniques

AlMahadin, G ORCID: https://orcid.org/0000-0001-9244-1054, Lotfi, A ORCID: https://orcid.org/0000-0002-5139-6565, Carthy, MM and Breedon, P ORCID: https://orcid.org/0000-0002-1006-0942, 2021. Enhanced Parkinson's disease tremor severity classification by combining signal processing with resampling techniques. SN Computer Science, 3 (1): 63. ISSN 2661-8907

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Abstract

Tremor is an indicative symptom of Parkinson's disease (PD). Healthcare professionals have clinically evaluated the tremor as part of the Unified Parkinson’s disease rating scale (UPDRS) which is inaccurate, subjective and unreliable. In this study, a novel approach to enhance the tremor severity classification is proposed. The proposed approach is a combination of signal processing and resampling techniques; over-sampling, under-sampling and a hybrid combination. Resampling techniques are integrated with well-known classifiers, such as artificial neural network based on multi-layer perceptron (ANN-MLP) and random forest (RF). Advanced metrics are calculated to evaluate the proposed approaches such as area under the curve (AUC), geometric mean (Gmean) and index of balanced accuracy (IBA). The results show that over-sampling techniques performed better than other resampling techniques, also hybrid techniques performed better than under-sampling techniques. The proposed approach improved tremor severity classification significantly and show that the best approach to classify tremor severity is the combination of ANN-MLP with Borderline SMOTE which has obtained 93.81% overall accuracy, 96% Gmean, 91% IBA and 99% AUC. Besides, it is found that different resampling techniques performed differently with different classifiers.

Item Type:	Journal article
Publication Title:	SN Computer Science
Creators:	AlMahadin, G., Lotfi, A., Carthy, M.M. and Breedon, P.
Publisher:	Springer Science and Business Media LLC
Date:	12 November 2021
Volume:	3
Number:	1
ISSN:	2661-8907
Identifiers:	Number Type 10.1007/s42979-021-00953-6 DOI 1495797 Other
Rights:	Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Divisions:	Schools > School of Science and Technology
Record created by:	Linda Sullivan
Date Added:	16 Nov 2021 11:08
Last Modified:	16 Nov 2021 11:08
URI:	https://irep.ntu.ac.uk/id/eprint/44811

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