Sangha Kim, Chaeyeon Yang, Suh-Yeon Dong, Seung-Hwan Lee
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引用次数: 0
Abstract
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.
期刊介绍:
The Psychiatry Investigation is published on the 25th day of every month in English by the Korean Neuropsychiatric Association (KNPA). The Journal covers the whole range of psychiatry and neuroscience. Both basic and clinical contributions are encouraged from all disciplines and research areas relevant to the pathophysiology and management of neuropsychiatric disorders and symptoms, as well as researches related to cross cultural psychiatry and ethnic issues in psychiatry. The Journal publishes editorials, review articles, original articles, brief reports, viewpoints and correspondences. All research articles are peer reviewed. Contributions are accepted for publication on the condition that their substance has not been published or submitted for publication elsewhere. Authors submitting papers to the Journal (serially or otherwise) with a common theme or using data derived from the same sample (or a subset thereof) must send details of all relevant previous publications and simultaneous submissions. The Journal is not responsible for statements made by contributors. Material in the Journal does not necessarily reflect the views of the Editor or of the KNPA. Manuscripts accepted for publication are copy-edited to improve readability and to ensure conformity with house style.