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 - Biomechatronics & Human-Machine Control)
M. Kok – Mentor (TU Delft - Team Manon Kok)
Frans C.T. Van Der Helm – Graduation committee member (TU Delft - Biomechatronics & Human-Machine Control)
O.E. Scharenborg – Graduation committee member (TU Delft - Multimedia Computing)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
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.