Machine-learning pipelines for classification of pathological tremor patients

A proof-of-concept

Master thesis (2021)

Authors

A. Assis de Souza Mechanical Engineering

Contributors

W. Mugge Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering (supervisor 1)
M. Kok Team Manon Kok - Mechanical, Maritime and Materials Engineering (supervisor 1)
F.C.T. van der Helm Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering (supervisor 2)
O.E. Scharenborg (supervisor 2)
Faculty
Mechanical Engineering
Copyright: © 2021 Alvaro Assis de Souza
Machine-learning Parkinson's disease Essential tremor Proof-of-concept Tremor classification Dystonic tremor
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Published Date

21-09-2021

Language

English

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Faculty

Mechanical Engineering

Abstract

Parkinson’s Disease (PD), Essential tremor (ET), and dystonia are movement disorders often misdiagnosed as one another and commonly present tremor as one of their motor symptoms. Rates of misdiagnosis between 30 and 50% of ET patients have been reported, where dystonia and PD are the most common missed diagnoses. Additionally, up to 50% of dystonia cases are misdiagnosed/under-diagnosed at their first encounter. Misdiagnosis rates up to 34% are reported for PD. Although similar tremor behaviors between the mentioned disorders lead to substantial misdiagnosis rates and, consequently, subpar care, tremorous signal acquired via wearable sensors can be used to discriminate between PD, ET, and dystonia patients. This study aims to develop three proofs-of-concept, accelerometer-based algorithms to assist medical doctors with decision-making in unclear diagnostic scenarios.