PDF(Pubmed)
Abstract：
UNASSIGNED: To explore the effectiveness of using deep learning network combined Vision Transformer (ViT) and Transformer to identify patients with depressive disorder on the basis of their sleep electroencephalogram (EEG) signals.
UNASSIGNED: The sleep EEG signals of 28 patients with depressive disorder and 37 normal controls were preprocessed. Then, the signals were converted into image format and the feature information on frequency domain and spatial domain was retained. After that, the images were transmitted to the ViT-Transformer coding network for deep learning of the EEG signal characteristics of the rapid eye movement (REM) sleep and non-rapid eye movement (NREM) sleep in patients with depressive disorder and those in normal controls, respectively, and to identify patients with depressive disorder.
UNASSIGNED: Based on the ViT-Transformer network, after examining different EEG frequencies, we found that the combination of delta, theta, and beta waves produced better results in identifying depressive disorder. Among the different EEG frequencies, EEG signal features of delta-theta-beta combination waves in REM sleep achieved 92.8% accuracy and 93.8% precision for identifying depression, with the recall rate of patients with depression being 84.7%, and the F0.5 value being 0.917±0.074. When using the delta-theta-beta combination EEG signal features in NREM sleep to identify depressive disorder, the accuracy was 91.7%, the precision was 90.8%, the recall rate was 85.2%, and the F0.5 value was 0.914±0.062. In addition, through visualization of the sleep EEG of different sleep stages for the whole night, it was found that classification errors usually occurred during transition to a different sleep stage.
UNASSIGNED: Using the deep learning ViT-Transformer network, we found that the EEG signal features in REM sleep based on delta-theta-beta combination waves showed better effect in identifying depressive disorder.
UNASSIGNED: The sleep EEG signals of 28 patients with depressive disorder and 37 normal controls were preprocessed. Then, the signals were converted into image format and the feature information on frequency domain and spatial domain was retained. After that, the images were transmitted to the ViT-Transformer coding network for deep learning of the EEG signal characteristics of the rapid eye movement (REM) sleep and non-rapid eye movement (NREM) sleep in patients with depressive disorder and those in normal controls, respectively, and to identify patients with depressive disorder.
UNASSIGNED: Based on the ViT-Transformer network, after examining different EEG frequencies, we found that the combination of delta, theta, and beta waves produced better results in identifying depressive disorder. Among the different EEG frequencies, EEG signal features of delta-theta-beta combination waves in REM sleep achieved 92.8% accuracy and 93.8% precision for identifying depression, with the recall rate of patients with depression being 84.7%, and the F0.5 value being 0.917±0.074. When using the delta-theta-beta combination EEG signal features in NREM sleep to identify depressive disorder, the accuracy was 91.7%, the precision was 90.8%, the recall rate was 85.2%, and the F0.5 value was 0.914±0.062. In addition, through visualization of the sleep EEG of different sleep stages for the whole night, it was found that classification errors usually occurred during transition to a different sleep stage.
UNASSIGNED: Using the deep learning ViT-Transformer network, we found that the EEG signal features in REM sleep based on delta-theta-beta combination waves showed better effect in identifying depressive disorder.
摘要：
■探索使用深度学习网络结合VisionTransformer（ViT）和Transformer根据其睡眠脑电图（EEG）信号识别抑郁症患者的有效性。
■对28例抑郁症患者和37例正常对照者的睡眠EEG信号进行了预处理。然后，将信号转换为图像格式，并保留频域和空间域的特征信息。之后,将图像传输到ViT-Transformer编码网络，以深度学习抑郁症患者和正常对照组的快速眼动（REM）睡眠和非快速眼动（NREM）睡眠的EEG信号特征。分别,并识别患有抑郁症的患者。
■基于ViT-Transformer网络，在检查不同的脑电图频率后，我们发现三角洲的组合，theta,β波在识别抑郁症方面产生了更好的结果。在不同的脑电图频率中，REM睡眠中δ-θ-β组合波的EEG信号特征在识别抑郁症方面达到了92.8％的准确率和93.8％的准确率，抑郁症患者的召回率为84.7%，F0.5值为0.917±0.074。当使用NREM睡眠中的delta-theta-beta组合EEG信号特征来识别抑郁症时，准确率为91.7%，精度为90.8%，召回率为85.2%，F0.5值为0.914±0.062。此外,通过可视化整个晚上不同睡眠阶段的睡眠脑电图，发现分类错误通常发生在过渡到不同的睡眠阶段。
■使用深度学习ViT-Transformer网络，我们发现,基于δ-θ-β组合波的REM睡眠中的EEG信号特征在识别抑郁症方面表现出更好的效果.
■对28例抑郁症患者和37例正常对照者的睡眠EEG信号进行了预处理。然后，将信号转换为图像格式，并保留频域和空间域的特征信息。之后,将图像传输到ViT-Transformer编码网络，以深度学习抑郁症患者和正常对照组的快速眼动（REM）睡眠和非快速眼动（NREM）睡眠的EEG信号特征。分别,并识别患有抑郁症的患者。
■基于ViT-Transformer网络，在检查不同的脑电图频率后，我们发现三角洲的组合，theta,β波在识别抑郁症方面产生了更好的结果。在不同的脑电图频率中，REM睡眠中δ-θ-β组合波的EEG信号特征在识别抑郁症方面达到了92.8％的准确率和93.8％的准确率，抑郁症患者的召回率为84.7%，F0.5值为0.917±0.074。当使用NREM睡眠中的delta-theta-beta组合EEG信号特征来识别抑郁症时，准确率为91.7%，精度为90.8%，召回率为85.2%，F0.5值为0.914±0.062。此外,通过可视化整个晚上不同睡眠阶段的睡眠脑电图，发现分类错误通常发生在过渡到不同的睡眠阶段。
■使用深度学习ViT-Transformer网络，我们发现,基于δ-θ-β组合波的REM睡眠中的EEG信号特征在识别抑郁症方面表现出更好的效果.
