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Machine Learning in Tremor Analysis: Critique and Directions

De, A; Bhatia, KP; Volkmann, J; Peach, R; Schreglmann, SR; (2023) Machine Learning in Tremor Analysis: Critique and Directions. Movement Disorders 10.1002/mds.29376. (In press). Green open access

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Abstract

Tremor is the most frequent human movement disorder, and its diagnosis is based on clinical assessment. Yet finding the accurate clinical diagnosis is not always straightforward. Fine-tuning of clinical diagnostic criteria over the past few decades, as well as device-based qualitative analysis, has resulted in incremental improvements to diagnostic accuracy. Accelerometric assessments are commonplace, enabling clinicians to capture high-resolution oscillatory properties of tremor, which recently have been the focus of various machine-learning (ML) studies. In this context, the application of ML models to accelerometric recordings provides the potential for less-biased classification and quantification of tremor disorders. However, if implemented incorrectly, ML can result in spurious or nongeneralizable results and misguided conclusions. This work summarizes and highlights recent developments in ML tools for tremor research, with a focus on supervised ML. We aim to highlight the opportunities and limitations of such approaches and provide future directions while simultaneously guiding the reader through the process of applying ML to analyze tremor data. We identify the need for the movement disorder community to take a more proactive role in the application of these novel analytical technologies, which so far have been predominantly pursued by the engineering and data analysis field. Ultimately, big-data approaches offer the possibility to identify generalizable patterns but warrant meaningful translation into clinical practice. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Type: Article
Title: Machine Learning in Tremor Analysis: Critique and Directions
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1002/mds.29376
Publisher version: https://doi.org/10.1002/mds.29376
Language: English
Additional information: Copyright © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
Keywords: accelerometer, artificial intelligence, classification, feature based
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Clinical and Movement Neurosciences
URI: https://discovery.ucl.ac.uk/id/eprint/10168067
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