Earticle

초록

영어

Recently, deep learning-based computer-aided diagnosis (CAD) systems using electroencephalography (EEG) have shown promising potential in the diagnosis of Major Depressive Disorder (MDD). However, most existing models rely on single-branch convolutional neural networks (CNNs), which are limited in their ability to capture spatiotemporal interactions within EEG signals, thereby restricting diagnostic performance. To address this limitation, we propose a residual multi-branch CNN designed to effectively learn both temporal and spatial features from EEG data for the diagnosis MDD patients. To this end, resting-state EEG recordings were collected from 90 drug-naïve MDD patients and 90 matched healthy controls. Frequency-domain features were extracted, and explainable AI techniques (Grad- CAM), along with statistical analysis, were used for informative channel selection. Our model achieved a classification accuracy of 96.11% using all 62 EEG channels and maintained high performance (89.44%) with only 10 channels. These findings demonstrate the potential of our approach for accurate and efficient early-stage MDD diagnosis in clinical applications.

목차

Abstract
I. 연구 배경
II. 연구 방법
III. 연구 결과
감사의 글
참고문헌

저자

• 김상규 [ 한국공학대학교 바이오헬스융합공학과 ]
• 정성택 [ 한국공학대학교 컴퓨터공학과 ]
• 황한정 [ 고려대학교 세종캠퍼스 전자및정보공학과 ]
• 심미선 [ 한국공학대학교 인공지능학과 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국정보통신설비학회 학술대회

• 간기

  반년간

• 수록기간

  2002~2025

• 십진분류

  KDC 567 DDC 621