Yuchen Liang, Xuelin Gu, Jun Wang, Xiaoou Li, Lihong Pan
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引用次数: 0
Abstract
Major Depressive Disorder (MDD) represents a major global health burden, characterized by high recurrence rates and an increased risk of suicide. Traditional clinical diagnoses rely primarily on subjective scales, which limits their objectivity in accurately distinguishing MDD subtypes.To address these challenges, this study introduces a multimodal assessment framework that integrates EEG functional connectivity with clinical scale data.We developed TSCANet (Transformer Local-Global Graph Neural Network Scales Cross-Attention Network), a deep learning multimodal model designed to address the complexity of psychiatric data. TSCANet achieved classification accuracies of 82.62% and 81.36% in visual and auditory paradigms, respectively, outperforming single-modality models.
期刊介绍：
The primary aims of Computer Methods in Biomechanics and Biomedical Engineering are to provide a means of communicating the advances being made in the areas of biomechanics and biomedical engineering and to stimulate interest in the continually emerging computer based technologies which are being applied in these multidisciplinary subjects. Computer Methods in Biomechanics and Biomedical Engineering will also provide a focus for the importance of integrating the disciplines of engineering with medical technology and clinical expertise. Such integration will have a major impact on health care in the future.
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