Enhancing Electroencephalogram-Based Prediction of Posttraumatic Stress Disorder Treatment Response Using Data Augmentation

• 발행기관: 대한신경정신의학회
• 저자명: Sangha Kim Chaeyeon Yang Suh-Yeon Dong Seung-Hwan Lee
• 간행물 정보: 『Psychiatry Investigation』제22권 제8호, 914~920쪽, 전체 7쪽
• 주제분류: 의약학 > 정신과학
• 파일형태: PDF
• 발행일자: 2025.08.05

국문 초록

Objective This study aimed to improve the prediction of treatment response in patients with posttraumatic stress disorder (PTSD) by applying a variational autoencoder (VAE)-based data augmentation (DA) approach to electroencephalogram (EEG) data. Methods EEG spectrograms were collected from patients diagnosed with PTSD. A VAE model was pretrained on the original spectrograms and used to generate augmented data samples. These augmented spectrograms were then utilized to train a deep neural network (DNN) classifier. The performance of the model was evaluated by comparing the area under the receiver operating characteristic curve (AUC) between models trained with and without DA. Results The DNN trained with VAE-augmented EEG data achieved an AUC of 0.85 in predicting treatment response, which was 0.11 higher than the model trained without augmentation. This reflects a significant improvement in classification performance and model generalization. Conclusion VAE-based DA effectively addresses the challenge of limited EEG data in clinical settings and enhances the performance of DNN models for treatment response prediction in PTSD. This approach presents a promising direction for future EEG-based neuropsychiatric research involving small datasets.

목차

INTRODUCTION

METHODS

RESULTS

DISCUSSION

REFERENCES

키워드

Electroencephalography Posttraumatic stress disorders Deep learning Autoencoder