Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 임창환 | - |
dc.date.accessioned | 2022-12-06T06:45:36Z | - |
dc.date.available | 2022-12-06T06:45:36Z | - |
dc.date.issued | 2022-04 | - |
dc.identifier.citation | FRONTIERS IN NEUROINFORMATICS, v. 16, article no. 811756, Page. 1-11 | en_US |
dc.identifier.issn | 1662-5196 | en_US |
dc.identifier.uri | https://www.frontiersin.org/articles/10.3389/fninf.2022.811756/full | en_US |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/178043 | - |
dc.description.abstract | Electroencephalography (EEG)-based diagnosis of psychiatric diseases using machine-learning approaches has made possible the objective diagnosis of various psychiatric diseases. The objective of this study was to improve the performance of a resting-state EEG-based computer-aided diagnosis (CAD) system to diagnose post-traumatic stress disorder (PTSD), by optimizing the frequency bands used to extract EEG features. We used eyes-closed resting-state EEG data recorded from 77 PTSD patients and 58 healthy controls (HC). Source-level power spectrum densities (PSDs) of the resting-state EEG data were extracted from 6 frequency bands (delta, theta, alpha, low-beta, high-beta, and gamma), and the PSD features of each frequency band and their combinations were independently used to discriminate PTSD and HC. The classification performance was evaluated using support vector machine with leave-one-out cross validation. The PSD features extracted from slower-frequency bands (delta and theta) showed significantly higher classification performance than those of relatively higher-frequency bands. The best classification performance was achieved when using delta PSD features (86.61%), which was significantly higher than that reported in a recent study by about 13%. The PSD features selected to obtain better classification performances could be explained from a neurophysiological point of view, demonstrating the promising potential to develop a clinically reliable EEG-based CAD system for PTSD diagnosis. | en_US |
dc.description.sponsorship | This work was supported by the Institute for Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (No. 2017-0-00451; Development of BCI based Brain and Cognitive Computing Technology for Recognizing Users Intentions using Deep Learning), and by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the MSIT (2020R1A4A1017775) and by the Ministry of Education (NRF-2019R1I1A1A01063313), and the Korea Science and Engineering Foundation (KOSEF), funded by the Korean government (NRF-2018R1A2A2A05018505). | en_US |
dc.language | en | en_US |
dc.publisher | FRONTIERS MEDIA SA | en_US |
dc.source | 85043_임창환.pdf | - |
dc.subject | machine-learning technique | en_US |
dc.subject | classification | en_US |
dc.subject | computer-aided diagnosis | en_US |
dc.subject | resting-state electroencephalogram (EEG) | en_US |
dc.subject | slow-frequency EEG oscillation | en_US |
dc.subject | post-traumatic stress disorder (PTSD) | en_US |
dc.title | Enhanced Performance by Interpretable Low-Frequency Electroencephalogram Oscillations in the Machine Learning-Based Diagnosis of Post-traumatic Stress Disorder | en_US |
dc.type | Article | en_US |
dc.relation.volume | 16 | - |
dc.identifier.doi | 10.3389/fninf.2022.811756 | en_US |
dc.relation.page | 1-11 | - |
dc.relation.journal | FRONTIERS IN NEUROINFORMATICS | - |
dc.contributor.googleauthor | Shim, Miseon | - |
dc.contributor.googleauthor | Im, Chang-Hwan | - |
dc.contributor.googleauthor | Lee, Seung-Hwan | - |
dc.contributor.googleauthor | Hwang, Han-Jeong | - |
dc.sector.campus | S | - |
dc.sector.daehak | 공과대학 | - |
dc.sector.department | 바이오메디컬공학전공 | - |
dc.identifier.pid | ich | - |
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