Machine-learning pipelines for classification of pathological tremor patients
A proof-of-concept
A. Assis de Souza (TU Delft - Mechanical Engineering)
W. Mugge – Mentor (TU Delft - Mechanical Engineering)
M. Kok – Mentor (TU Delft - Mechanical Engineering)
F.C.T. van der Helm – Graduation committee member (TU Delft - Mechanical Engineering)
O.E. Scharenborg – Graduation committee member (TU Delft - Electrical Engineering, Mathematics and Computer Science)
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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.