Cargando…

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

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Varghese, Julian, Niewöhner, Stephan, Soto-Rey, Iñaki, Schipmann-Miletić, Stephanie, Warneke, Nils, Warnecke, Tobias, Dugas, Martin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6363699/
https://www.ncbi.nlm.nih.gov/pubmed/30761078
http://dx.doi.org/10.3389/fneur.2019.00048
_version_ 1783393150727880704
author Varghese, Julian
Niewöhner, Stephan
Soto-Rey, Iñaki
Schipmann-Miletić, Stephanie
Warneke, Nils
Warnecke, Tobias
Dugas, Martin
author_facet Varghese, Julian
Niewöhner, Stephan
Soto-Rey, Iñaki
Schipmann-Miletić, Stephanie
Warneke, Nils
Warnecke, Tobias
Dugas, Martin
author_sort Varghese, Julian
collection PubMed
description 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.
format Online
Article
Text
id pubmed-6363699
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-63636992019-02-13 A Smart Device System to Identify New Phenotypical Characteristics in Movement Disorders Varghese, Julian Niewöhner, Stephan Soto-Rey, Iñaki Schipmann-Miletić, Stephanie Warneke, Nils Warnecke, Tobias Dugas, Martin Front Neurol Neurology 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. Frontiers Media S.A. 2019-01-30 /pmc/articles/PMC6363699/ /pubmed/30761078 http://dx.doi.org/10.3389/fneur.2019.00048 Text en Copyright © 2019 Varghese, Niewöhner, Soto-Rey, Schipmann-Miletić, Warneke, Warnecke and Dugas. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neurology
Varghese, Julian
Niewöhner, Stephan
Soto-Rey, Iñaki
Schipmann-Miletić, Stephanie
Warneke, Nils
Warnecke, Tobias
Dugas, Martin
A Smart Device System to Identify New Phenotypical Characteristics in Movement Disorders
title A Smart Device System to Identify New Phenotypical Characteristics in Movement Disorders
title_full A Smart Device System to Identify New Phenotypical Characteristics in Movement Disorders
title_fullStr A Smart Device System to Identify New Phenotypical Characteristics in Movement Disorders
title_full_unstemmed A Smart Device System to Identify New Phenotypical Characteristics in Movement Disorders
title_short A Smart Device System to Identify New Phenotypical Characteristics in Movement Disorders
title_sort smart device system to identify new phenotypical characteristics in movement disorders
topic Neurology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6363699/
https://www.ncbi.nlm.nih.gov/pubmed/30761078
http://dx.doi.org/10.3389/fneur.2019.00048
work_keys_str_mv AT varghesejulian asmartdevicesystemtoidentifynewphenotypicalcharacteristicsinmovementdisorders
AT niewohnerstephan asmartdevicesystemtoidentifynewphenotypicalcharacteristicsinmovementdisorders
AT sotoreyinaki asmartdevicesystemtoidentifynewphenotypicalcharacteristicsinmovementdisorders
AT schipmannmileticstephanie asmartdevicesystemtoidentifynewphenotypicalcharacteristicsinmovementdisorders
AT warnekenils asmartdevicesystemtoidentifynewphenotypicalcharacteristicsinmovementdisorders
AT warnecketobias asmartdevicesystemtoidentifynewphenotypicalcharacteristicsinmovementdisorders
AT dugasmartin asmartdevicesystemtoidentifynewphenotypicalcharacteristicsinmovementdisorders
AT varghesejulian smartdevicesystemtoidentifynewphenotypicalcharacteristicsinmovementdisorders
AT niewohnerstephan smartdevicesystemtoidentifynewphenotypicalcharacteristicsinmovementdisorders
AT sotoreyinaki smartdevicesystemtoidentifynewphenotypicalcharacteristicsinmovementdisorders
AT schipmannmileticstephanie smartdevicesystemtoidentifynewphenotypicalcharacteristicsinmovementdisorders
AT warnekenils smartdevicesystemtoidentifynewphenotypicalcharacteristicsinmovementdisorders
AT warnecketobias smartdevicesystemtoidentifynewphenotypicalcharacteristicsinmovementdisorders
AT dugasmartin smartdevicesystemtoidentifynewphenotypicalcharacteristicsinmovementdisorders