用脑电图和混合神经网络精准识别抑郁状态。
Depression Detection Based on Electroencephalography Using a Hybrid Deep Neural Network CNN-GRU and MRMR Feature Selection
- 结合CNN提取空间特征、GRU捕捉时间动态，融合双模态信息。
- 在公开数据集上达到98.74%的分类准确率，表现优异。
- 适合临床辅助诊断，为抑郁症早期干预提供客观工具。
本研究探讨基于深度学习方法检测与分类抑郁与非抑郁状态。抑郁症是常见精神障碍，严重影响生活质量，早期诊断可显著提升治疗效果与患者照护。传统诊断依赖主观自评，可靠性不足，亟需客观准确的识别技术。本文提出一种基于深度学习的脑电图（EEG）分析框架，用于抑郁症早期检测。EEG信号反映大脑内在活动，不受外部行为干扰，可揭示抑郁相关的细微神经变化。所提方法结合卷积神经网络（CNN）与门控循环单元（GRU），联合提取EEG数据中的空间与时间特征；随后采用最小冗余最大相关（MRMR）算法筛选最具信息量的特征，再通过全连接神经网络进行分类。实验结果表明，该模型在识别抑郁状态方面表现卓越，总体准确率达98.74%。通过有效整合时序与空间信息并优化特征选择，该方法展现出良好的临床应用潜力，不仅实现高精度早期筛查，还可支持更优治疗策略与患者预后改善。
原文摘要 · Abstract (English)
This study investigates the detection and classification of depressive and non-depressive states using deep learning approaches. Depression is a prevalent mental health disorder that substantially affects quality of life, and early diagnosis can greatly enhance treatment effectiveness and patient care. However, conventional diagnostic methods rely heavily on self-reported assessments, which are often subjective and may lack reliability. Consequently, there is a strong need for objective and accurate techniques to identify depressive states. In this work, a deep learning based framework is proposed for the early detection of depression using EEG signals. EEG data, which capture underlying brain activity and are not influenced by external behavioral factors, can reveal subtle neural changes associated with depression. The proposed approach combines convolutional neural networks (CNNs) and gated recurrent units (GRUs) to jointly extract spatial and temporal features from EEG recordings. The minimum redundancy maximum relevance (MRMR) algorithm is then applied to select the most informative features, followed by classification using a fully connected neural network. The results demonstrate that the proposed model achieves high performance in accurately identifying depressive states, with an overall accuracy of 98.74%. By effectively integrating temporal and spatial information and employing optimized feature selection, this method shows strong potential as a reliable tool for clinical applications. Overall, the proposed framework not only enables accurate early detection of depression but also has the potential to support improved treatment strategies and patient outcomes.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。