College of Engineering, Zhejiang Normal University, Jinhua 321004, China.
College of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China.
Sensors (Basel). 2023 Oct 23;23(20):8639. doi: 10.3390/s23208639.
Depressive disorder (DD) has become one of the most common mental diseases, seriously endangering both the affected person's psychological and physical health. Nowadays, a DD diagnosis mainly relies on the experience of clinical psychiatrists and subjective scales, lacking objective, accurate, practical, and automatic diagnosis technologies. Recently, electroencephalogram (EEG) signals have been widely applied for DD diagnosis, but mainly with high-density EEG, which can severely limit the efficiency of the EEG data acquisition and reduce the practicability of diagnostic techniques. The current study attempts to achieve accurate and practical DD diagnoses based on combining frontal six-channel electroencephalogram (EEG) signals and deep learning models. To this end, 10 min clinical resting-state EEG signals were collected from 41 DD patients and 34 healthy controls (HCs). Two deep learning models, multi-resolution convolutional neural network (MRCNN) combined with long short-term memory (LSTM) (named MRCNN-LSTM) and MRCNN combined with residual squeeze and excitation (RSE) (named MRCNN-RSE), were proposed for DD recognition. The results of this study showed that the higher EEG frequency band obtained the better classification performance for DD diagnosis. The MRCNN-RSE model achieved the highest classification accuracy of 98.48 ± 0.22% with 8-30 Hz EEG signals. These findings indicated that the proposed analytical framework can provide an accurate and practical strategy for DD diagnosis, as well as essential theoretical and technical support for the treatment and efficacy evaluation of DD.
抑郁障碍（DD）已成为最常见的精神疾病之一，严重危害患者的身心健康。目前，DD 的诊断主要依赖于临床精神科医生的经验和主观量表，缺乏客观、准确、实用和自动化的诊断技术。最近，脑电图（EEG）信号已广泛应用于 DD 诊断，但主要是高密度 EEG，这可能严重限制 EEG 数据采集的效率，并降低诊断技术的实用性。本研究尝试结合额部六通道脑电图（EEG）信号和深度学习模型来实现准确和实用的 DD 诊断。为此，从 41 名 DD 患者和 34 名健康对照者（HCs）中采集了 10 分钟的临床静息态 EEG 信号。提出了两种深度学习模型，多分辨率卷积神经网络（MRCNN）结合长短期记忆（LSTM）（命名为 MRCNN-LSTM）和 MRCNN 结合残差挤压和激励（RSE）（命名为 MRCNN-RSE），用于 DD 识别。研究结果表明，用于 DD 诊断的 EEG 信号的较高频带获得了更好的分类性能。MRCNN-RSE 模型在 8-30 Hz 的 EEG 信号下实现了 98.48±0.22%的最高分类准确率。这些发现表明，所提出的分析框架可以为 DD 诊断提供一种准确实用的策略，为 DD 的治疗和疗效评估提供必要的理论和技术支持。
