Evaluating Effects of Resting-State Electroencephalography Data Pre-Processing on a Machine Learning Task for Parkinson's Disease
: Vlieger, Robin; Daskalaki, Elena; Apthorp, Deborah; Lueck, Christian J.; Suominen, Hanna
: Bichel-Findlay, Jen;Otero, Paula; Scott, Philip; Huesing, Elaine
: World Congress on Medical and Health Informatics
: 2024
: Studies in Health Technology and Informatics
: MEDINFO 2023 - The Future Is Accessible: Proceedings of the 19th World Congress on Medical and Health Informatics
: Studies in health technology and informatics
: Stud Health Technol Inform
: 310
: 1480
: 1481
: 978-1-64368-456-7
: 978-1-64368-457-4
: 0926-9630
: 1879-8365
DOI: https://doi.org/10.3233/SHTI231254
: https://ebooks.iospress.nl/doi/10.3233/SHTI231254
: https://research.utu.fi/converis/portal/detail/Publication/387203582
Resting-state electroencephalography pre-processing methods in machine learning studies into Parkinson's disease classification vary widely. Here three separate data sets were pre-processed to four different stages to investigate the effects on evaluation metrics, using power features from six regions-of-interest, Random Forest Classifiers for feature selection, and Support Vector Machines for classification. This showed muscle artefact inflated evaluation metrics, and alpha and theta band features produced the best results when fully pre-processing data.
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This study was funded by Our Health in Our Hands, an initiative of the Australian National University, which aims to transform health care by developing new personalised health technologies and solutions. We gratefully acknowledge the funding from the ANU School of Computing for the first author’s PhD studies.
