Abstract
A resting-state EEG-based computer-aided diagnosis (CAD) system could assist the accurate diagnosis of major depressive disorder (MDD) patients. The purpose of this study is to develop a resting-state EEG-based CAD system for diagnosis of drug-naive female MDD patients. To this end, eyes-closed resting-state EEG data were recorded from 30 female MDD patients and sex-matched 30 healthy controls. Three types of features were extracted, i.e., power spectral density (PSD), phase locking values (PLV) and network indices (strength, clustering coefficient, and path length). The classification performances of each feature set were evaluated using a support vector machine with leave-one-out cross-validation. The best classification performance was achieved when using PLV features (accuracy – 85.00%). In our future studies, we will attempt to develop a practically useful CAD system for MDD patients by applying channel selection approach.
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Acknowledgements
This work was supported by the Institute for Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (No. 2017-0-00451; Development of BCI based Brain and Cognitive Computing Technology for Recognizing User’s Intentions using Deep Learning), by the Basic Research Program through the National Research Foundation of Korea (NRF) funded by the MSIT (NRF-2020R1A4A1017775 and NRF-2019R1I1A1A01063313), by the Korea Medical Device Development Fund grant funded by the Korea government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health &Welfare, the Ministry of Food and Drug Safety) (1711138348, KMDF_PR_20200901_0169), and by the Brain Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2015M3C7A1028252).
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Shim, M., Lee, SH., Hwang, HJ. (2022). Resting-State Electroencephalography (EEG)-Based Diagnosis System for Drug-Naive Female Major Depressive Disorder Patients. In: Wallraven, C., Liu, Q., Nagahara, H. (eds) Pattern Recognition. ACPR 2021. Lecture Notes in Computer Science, vol 13189. Springer, Cham. https://doi.org/10.1007/978-3-031-02444-3_18
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