Development of a Machine Learning Based Algorithm To Accurately Detect Schizophrenia based on One-minute EEG Recordings

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dc.contributor.authorBuettner, Ricardo
dc.contributor.authorBeil, David
dc.contributor.authorScholtz, Stefanie
dc.contributor.authorDjemai, Aadel
dc.date.accessioned2020-01-04T07:49:09Z
dc.date.available2020-01-04T07:49:09Z
dc.date.issued2020-01-07
dc.description.abstractWhile diagnosing schizophrenia by physicians based on patients' history and their overall mental health is inaccurate, we report on promising results using a novel, fast and reliable machine learning approach based on electroencephalography (EEG) recordings. We show that a fine granular division of EEG spectra in combination with the Random Forest classifier allows a distinction to be made between paranoid schizophrenic (ICD-10 F20.0) and non-schizophrenic persons with a very good balanced accuracy of 96.77 percent. We evaluate our approach on EEG data from an open neurological and psychiatric repository containing 499 one-minute recordings of n=28 participants (14 paranoid schizophrenic and 14 healthy controls). Since the fact that neither diagnostic tests nor biomarkers are available yet to diagnose paranoid schizophrenia, our approach paves the way to a quick and reliable diagnosis with a high accuracy. Furthermore, interesting insights about the most predictive subbands were gained by analyzing the electroencephalographic spectrum up to 100 Hz.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2020.393
dc.identifier.isbn978-0-9981331-3-3
dc.identifier.urihttp://hdl.handle.net/10125/64135
dc.language.isoeng
dc.relation.ispartofProceedings of the 53rd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectBig Data on Healthcare Application
dc.subjectelectroencephalography
dc.subjectmachine learning
dc.subjectrandom forest
dc.subjectschizophrenia
dc.subjectspectral analysis
dc.titleDevelopment of a Machine Learning Based Algorithm To Accurately Detect Schizophrenia based on One-minute EEG Recordings
dc.typeConference Paper
dc.type.dcmiText

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