TY - JOUR
T1 - Next move in movement disorders (NEMO)
T2 - Developing a computer-aided classification tool for hyperkinetic movement disorders
AU - van der Stouwe, A. M. Madelein
AU - Tuitert, Inge
AU - Giotis, Ioannis
AU - Calon, Joost
AU - Gannamani, Rahul
AU - Dalenberg, Jelle R.
AU - van der Veen, Sterre
AU - Klamer, Marrit R.
AU - Telea, Alex C.
AU - Tijssen, Marina A.J.
N1 - Funding Information:
Development of the European Union in collaboration with the province of Fryslan (grant number 01492947), and a ZonMW TOP Grant (grant number 91218013).
Funding Information:
Funding This work was supported by the European Fund for Regional
Funding Information:
This work was supported by the European Fund for Regional Development of the European Union in collaboration with the province of Fryslan (grant number 01492947), and a ZonMW TOP Grant (grant number 91218013).
Publisher Copyright:
© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.
PY - 2021/10/11
Y1 - 2021/10/11
N2 - Introduction: Our aim is to develop a novel approach to hyperkinetic movement disorder classification, that combines clinical information, electromyography, accelerometry and video in a computer-aided classification tool. We see this as the next step towards rapid and accurate phenotype classification, the cornerstone of both the diagnostic and treatment process.Methods and analysis: The Next Move in Movement Disorders (NEMO) study is a cross-sectional study at Expertise Centre Movement Disorders Groningen, University Medical Centre Groningen. It comprises patients with single and mixed phenotype movement disorders. Single phenotype groups will first include dystonia, myoclonus and tremor, and then chorea, tics, ataxia and spasticity. Mixed phenotypes are myoclonus-dystonia, dystonic tremor, myoclonus ataxia and jerky/tremulous functional movement disorders. Groups will contain 20 patients, or 40 healthy participants. The gold standard for inclusion consists of interobserver agreement on the phenotype among three independent clinical experts. Electromyography, accelerometry and three-dimensional video data will be recorded during performance of a set of movement tasks, chosen by a team of specialists to elicit movement disorders. These data will serve as input for the machine learning algorithm. Labels for supervised learning are provided by the expert-based classification, allowing the algorithm to learn to predict what the output label should be when given new input data. Methods using manually engineered features based on existing clinical knowledge will be used, as well as deep learning methods which can detect relevant and possibly new features. Finally, we will employ visual analytics to visualise how the classification algorithm arrives at its decision.Ethics and dissemination: Ethical approval has been obtained from the relevant local ethics committee. The NEMO study is designed to pioneer the application of machine learning of movement disorders. We expect to publish articles in multiple related fields of research and patients will be informed of important results via patient associations and press releases.
AB - Introduction: Our aim is to develop a novel approach to hyperkinetic movement disorder classification, that combines clinical information, electromyography, accelerometry and video in a computer-aided classification tool. We see this as the next step towards rapid and accurate phenotype classification, the cornerstone of both the diagnostic and treatment process.Methods and analysis: The Next Move in Movement Disorders (NEMO) study is a cross-sectional study at Expertise Centre Movement Disorders Groningen, University Medical Centre Groningen. It comprises patients with single and mixed phenotype movement disorders. Single phenotype groups will first include dystonia, myoclonus and tremor, and then chorea, tics, ataxia and spasticity. Mixed phenotypes are myoclonus-dystonia, dystonic tremor, myoclonus ataxia and jerky/tremulous functional movement disorders. Groups will contain 20 patients, or 40 healthy participants. The gold standard for inclusion consists of interobserver agreement on the phenotype among three independent clinical experts. Electromyography, accelerometry and three-dimensional video data will be recorded during performance of a set of movement tasks, chosen by a team of specialists to elicit movement disorders. These data will serve as input for the machine learning algorithm. Labels for supervised learning are provided by the expert-based classification, allowing the algorithm to learn to predict what the output label should be when given new input data. Methods using manually engineered features based on existing clinical knowledge will be used, as well as deep learning methods which can detect relevant and possibly new features. Finally, we will employ visual analytics to visualise how the classification algorithm arrives at its decision.Ethics and dissemination: Ethical approval has been obtained from the relevant local ethics committee. The NEMO study is designed to pioneer the application of machine learning of movement disorders. We expect to publish articles in multiple related fields of research and patients will be informed of important results via patient associations and press releases.
KW - adult neurology
KW - neurology
KW - neurophysiology
U2 - 10.1136/bmjopen-2021-055068
DO - 10.1136/bmjopen-2021-055068
M3 - Article
C2 - 34635535
AN - SCOPUS:85117142424
SN - 2044-6055
VL - 11
JO - BMJ Open
JF - BMJ Open
IS - 10
M1 - e055068
ER -