Earticle

초록

영어

Post-Traumatic Stress Disorder (PTSD) is a serious psychiatric condition that requires objective and accurate diagnosis for effective early intervention. In this study, we propose a deep learning-based computer-aided diagnosis (CAD) system using event-related potentials (ERP) to distinguish individuals with PTSD from healthy controls. We introduce a novel model, TIME-CNN (Temporal Integration with Multi-scale dEcoding CNN), designed to extract temporal features through multi-scale depthwise convolutions and residual connections. EEG data were collected during an auditory oddball task from 51 PTSD patients and 39 matched healthy controls. As a result, the proposed TIME-CNN outperformed its shallower counterpart (Shallow TIME-CNN) in both classification accuracy (86.05% vs. 76.40%) and training time (7.5 vs. 12.5 hours). These findings demonstrate the effectiveness and practicality of the TIME-CNN model for ERP-based PTSD diagnosis.

목차

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

저자

• 황시훈 [ 한국공학대학교 인공지능학과 ]
• 김상규 [ 한국공학대학교 바이오헬스융합공학과 ]
• 조은상 [ 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