The Use of Event-Related Potentials and Machine Learning to Improve Diagnostic Testing and Prediction of Disease Progression in Parkinson's Disease

Robin Vlieger; Elena Daskalaki; Deborah Apthorp; Christian J Lueck; Hanna Suominen

doi:10.3233/SHTI210737

The Use of Event-Related Potentials and Machine Learning to Improve Diagnostic Testing and Prediction of Disease Progression in Parkinson's Disease

Stud Health Technol Inform. 2021 Dec 15:284:333-335. doi: 10.3233/SHTI210737.

Authors

Robin Vlieger¹, Elena Daskalaki¹, Deborah Apthorp^{1

2}, Christian J Lueck^{3

4}, Hanna Suominen^{1

5

6}

Affiliations

¹ School of Computing, Australian National University, ANU, Australia.
² School of Psychology, University of New England, Australia.
³ ANU Medical School, ANU, Australia.
⁴ Department of Neurology, Canberra Hospital, Australia.
⁵ Data61, Commonwealth Scientific and Industrial Research Organisation, Australia.
⁶ Department of Computing, University of Turku, Finland.

PMID: 34920540
DOI: 10.3233/SHTI210737

Abstract

Current tests of disease status in Parkinson's disease suffer from high variability, limiting their ability to determine disease severity and prognosis. Event-related potentials, in conjunction with machine learning, may provide a more objective assessment. In this study, we will use event-related potentials to develop machine learning models, aiming to provide an objective way to assess disease status and predict disease progression in Parkinson's disease.

Keywords: Diagnosis; Parkinson disease; disease status; event-related potentials; machine learning; prediction.

MeSH terms

Diagnostic Techniques and Procedures
Disease Progression
Evoked Potentials
Humans
Machine Learning
Parkinson Disease* / diagnosis