Tremor Identification Using Machine Learning in Parkinson's Disease

Amitava Datta, Angana Saikia, Vinayak Majhi, Masaraf Hussain, Sudip Paul

Research output: Chapter in Book/Conference paperChapter

Abstract

Tremor is an involuntary quivering movement or shake. Characteristically occurring at rest, the classic slow, rhythmic tremor of Parkinson's disease (PD) typically starts in one hand, foot, or leg and can eventually affect both sides of the body. The resting tremor of PD can also occur in the jaw, chin, mouth, or tongue. Loss of dopamine leads to the symptoms of Parkinson's disease and may include a tremor. For some people, a tremor might be the first symptom of PD. Various studies have proposed measurable technologies and the analysis of the characteristics of Parkinsonian tremors using different techniques. Various machine-learning algorithms such as a support vector machine (SVM) with three kernels, a discriminant analysis, a random forest, and a kNN algorithm are also used to classify and identify various kinds of tremors. This chapter focuses on an in-depth review on identification and classification of various Parkinsonian tremors using machine learning algorithms.
Original languageEnglish
Title of host publicationEarly Detection of Neurological Disorders Using Machine Learning Systems
PublisherIGI Global
Pages128-151
ISBN (Electronic)9781522585688
ISBN (Print) 9781522585671
DOIs
Publication statusPublished - 2019

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    Datta, A., Saikia, A., Majhi, V., Hussain, M., & Paul, S. (2019). Tremor Identification Using Machine Learning in Parkinson's Disease. In Early Detection of Neurological Disorders Using Machine Learning Systems (pp. 128-151). IGI Global. https://doi.org/10.4018/978-1-5225-8567-1.ch008