A Smart Device System to Identify New Phenotypical Characteristics in Movement Disorders

Varghese J, Niewöhner S, Soto-Rey I, Schipmann-Mileti S, Warneke N, Warnecke T, Dugas M

Research article (journal) | Peer reviewed

Abstract

Parkinson's disease and Essential Tremor are two of the most common movement disorders and are still associated with high rates of misdiagnosis. Collected data by technology-based objective measures (TOMs) has the potential to provide new promising and highly accurate movement data for a better understanding of phenotypical characteristics and diagnostic support. A technology-based system called Smart Device System (SDS) is going to be implemented for multi-modal high-resolution acceleration measurement of patients with PD or ET within a clinical setting. The 2-year prospective observational study is conducted to identify new phenotypical biomarkers and train an Artificial Intelligence System. The SDS is going to be integrated and tested within a 20-min assessment including smartphone-based questionnaires, two smartwatches at both wrists and tablet-based Archimedean spirals drawing for deeper tremor-analyses. The electronic questionnaires will cover data on medication, family history and non-motor symptoms. In this paper, we describe the steps for this novel technology-utilizing examination, the principal steps for data analyses and the targeted performances of the system. Future work considers integration with Deep Brain Stimulation, dissemination into further sites and patient's home setting as well as integration with further data sources as neuroimaging and biobanks. Study Registration ID on ClinicalTrials.gov: NCT03638479.

Details about the publication

JournalFrontiers in Neurology
Volume10
StatusPublished
Release year2019
Language in which the publication is writtenEnglish
DOI10.3389/fneur.2019.00048
Link to the full textPM:30761078; ISI:000457250700002
Keywordsartificial; CLASSIFICATION; COMMON; DIFFERENTIAL-DIAGNOSIS; ESSENTIAL TREMOR; intelligence; MOTOR; neural networks; Parkinson's Disease; smart wearables

Authors from the University of Münster

Dugas, Martin
Institute of Medical Informatics
Soto Rey, Inaki
Institute of Medical Informatics
Varghese, Julian
Institute of Medical Informatics