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https://hdl.handle.net/1959.11/54794
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DC Field | Value | Language |
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dc.contributor.author | Vlieger, Robin | en |
dc.contributor.author | Daskalaki, Elena | en |
dc.contributor.author | Apthorp, Deborah | en |
dc.contributor.author | Lueck, Christian J | en |
dc.contributor.author | Suominen, Hanna | en |
dc.date.accessioned | 2023-05-15T04:57:03Z | - |
dc.date.available | 2023-05-15T04:57:03Z | - |
dc.date.issued | 2023-03-06 | - |
dc.identifier.uri | https://hdl.handle.net/1959.11/54794 | - |
dc.description.abstract | <p>Resting-state electroencephalography (RSEEG) is a method under consideration as a potential biomarker that could support early and accurate diagnosis of Parkinson's disease (PD). RSEEG data is often contaminated by signals arising from other electrophysiological sources and the environment, necessitating pre-processing of the data prior to applying machine learning methods for classification. Importantly, using differing degrees of pre-processing will lead to different classification results. This study aimed to examine this by evaluating the difference in experimental results when using re-referenced data, data that had undergone filtering and artefact rejection, and data without muscle artefact. The results demonstrated that, using a Random Forest Classifier for feature selection and a Support Vector Machine for disease classification, different levels of pre-processing led to markedly different classification results. In particular, the presence of muscle artefact was associated with inflated classification accuracy, emphasising the importance of its removal as part of pre-processing.</p> | en |
dc.language | en | en |
dc.publisher | medRxiv | en |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.title | Evaluating Effects of Resting-State Electroencephalography Data Pre-Processing on a Machine Learning Task for Parkinson's Disease | en |
dc.type | Working Paper | en |
dc.identifier.doi | 10.1101/2023.03.06.23286826 | en |
dcterms.accessRights | Green | en |
local.contributor.firstname | Robin | en |
local.contributor.firstname | Elena | en |
local.contributor.firstname | Deborah | en |
local.contributor.firstname | Christian J | en |
local.contributor.firstname | Hanna | en |
local.profile.school | School of Psychology and Behavioural Science | en |
local.profile.school | School of Psychology | en |
local.profile.email | rvlieger@une.edu.au | en |
local.profile.email | dapthorp@une.edu.au | en |
local.output.category | W | en |
local.record.place | au | en |
local.record.institution | University of New England | en |
local.publisher.place | United States of America | en |
local.access.fulltext | Yes | en |
local.contributor.lastname | Vlieger | en |
local.contributor.lastname | Daskalaki | en |
local.contributor.lastname | Apthorp | en |
local.contributor.lastname | Lueck | en |
local.contributor.lastname | Suominen | en |
dc.identifier.staff | une-id:rvlieger | en |
dc.identifier.staff | une-id:dapthorp | en |
local.profile.orcid | 0000-0001-5785-024X | en |
local.profile.role | author | en |
local.profile.role | author | en |
local.profile.role | author | en |
local.profile.role | author | en |
local.profile.role | author | en |
local.identifier.unepublicationid | une:1959.11/54794 | en |
dc.identifier.academiclevel | Student | en |
dc.identifier.academiclevel | Academic | en |
dc.identifier.academiclevel | Academic | en |
dc.identifier.academiclevel | Academic | en |
dc.identifier.academiclevel | Academic | en |
local.title.maintitle | Evaluating Effects of Resting-State Electroencephalography Data Pre-Processing on a Machine Learning Task for Parkinson's Disease | en |
local.relation.fundingsourcenote | This research was funded by and has been delivered in partnership with Our Health in Our Hands (OHIOH), a strategic initiative of the Australian National University, which aims to transform health care by developing new personalized health technologies and solutions in collaboration with patients, clinicians, and health-care providers. We gratefully acknowledge the funding from the ANU School of Computing for the PhD studies of the first author. | en |
local.output.categorydescription | W Working Paper | en |
local.search.author | Vlieger, Robin | en |
local.search.author | Daskalaki, Elena | en |
local.search.author | Apthorp, Deborah | en |
local.search.author | Lueck, Christian J | en |
local.search.author | Suominen, Hanna | en |
local.uneassociation | Yes | en |
local.atsiresearch | No | en |
local.sensitive.cultural | No | en |
local.year.published | 2023 | en |
local.fileurl.closedpublished | https://rune.une.edu.au/web/retrieve/74443ae3-d188-4e09-9eb0-011246120311 | en |
local.subject.for2020 | 320904 Computational neuroscience (incl. mathematical neuroscience and theoretical neuroscience) | en |
local.subject.for2020 | 520105 Psychological methodology, design and analysis | en |
local.subject.seo2020 | 280112 Expanding knowledge in the health sciences | en |
local.profile.affiliationtype | External Affiliation | en |
local.profile.affiliationtype | External Affiliation | en |
local.profile.affiliationtype | UNE Affiliation | en |
local.profile.affiliationtype | External Affiliation | en |
local.profile.affiliationtype | External Affiliation | en |
Appears in Collections: | School of Psychology Working Paper |
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