Development of a Machine Learning Based Algorithm To Accurately Detect Schizophrenia based on One-minute EEG Recordings
Development of a Machine Learning Based Algorithm To Accurately Detect Schizophrenia based on One-minute EEG Recordings
dc.contributor.author | Buettner, Ricardo | |
dc.contributor.author | Beil, David | |
dc.contributor.author | Scholtz, Stefanie | |
dc.contributor.author | Djemai, Aadel | |
dc.date.accessioned | 2020-01-04T07:49:09Z | |
dc.date.available | 2020-01-04T07:49:09Z | |
dc.date.issued | 2020-01-07 | |
dc.description.abstract | While 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.extent | 10 pages | |
dc.identifier.doi | 10.24251/HICSS.2020.393 | |
dc.identifier.isbn | 978-0-9981331-3-3 | |
dc.identifier.uri | http://hdl.handle.net/10125/64135 | |
dc.language.iso | eng | |
dc.relation.ispartof | Proceedings of the 53rd Hawaii International Conference on System Sciences | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.subject | Big Data on Healthcare Application | |
dc.subject | electroencephalography | |
dc.subject | machine learning | |
dc.subject | random forest | |
dc.subject | schizophrenia | |
dc.subject | spectral analysis | |
dc.title | Development of a Machine Learning Based Algorithm To Accurately Detect Schizophrenia based on One-minute EEG Recordings | |
dc.type | Conference Paper | |
dc.type.dcmi | Text |
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