睡眠皮层脑电图(EEG)具有检测抑郁症的潜力,不同的睡眠结构和皮质连接已在抑郁症患者中得到证实。然而,多通道睡眠脑电图记录操作繁琐,需要实验室设备和专业睡眠技术人员。这里,我们专注于使用最少睡眠脑电图通道进行抑郁症检测。
在睡眠期间记录30例抑郁症患者和30例年龄匹配的正常对照的16个通道的脑电图数据。计算各单脑电通道的功率谱密度,然后测量不同频段EEG通道对之间的符号传递熵(STE)和加权相位滞后指数(WPLI)。此后,这些特征在双向分类中通过F评分进行评估(抑郁与控制)30秒睡眠脑电图片段。根据F分数,引入熵值法计算权重,进一步评估各脑电通道或通道对的分类能力。最后,通过机器学习来验证抑郁症诊断中重要的脑电图通道或通道对。
表征后叶半球间连通性的特征,尤其是颞叶,显示出较高的分类能力。在颞叶中使用两个和四个EEG通道的分类准确率分别为97.96%和99.61%,分别。
这项研究表明,仅使用少数睡眠脑电图通道进行抑郁症筛查的可能性,这可以大大促进医院外抑郁症的诊断。
Sleeping cortical electroencephalogram (EEG) has the potential for depression detection, for different sleep structure and cortical connection have been proved in depressed patients. However, the operation of multi-channel sleep EEG recording is cumbersome and requires laboratory equipment and professional sleep technician. Here, we focus on the depression detection using minimal sleep EEG channels.
Sixteen channels of EEG data of 30 patients with depression and 30 age-matched normal controls were recorded during sleep. Power spectral density of each single EEG channel was calculated, followed by measuring the symbolic transfer entropy (STE) and weighed phase lag index (WPLI) between EEG channel pairs in various frequency bands. Thereafter, these features were evaluated by F-score in the two-way classification (depression vs. control) of 30-s sleep EEG segments. Based on the F-score, entropy method was introduced to calculate the weight which could further assess the classification ability of various EEG channels or channel pairs. Finally, machine learning was implemented to verify the important EEG channels or channel pairs in depression diagnosis.
The features characterizing the inter-hemispheric connectivity in the posterior lobe, especially in the temporal lobe, showed high classification capacity. The classification accuracy of using two and four EEG channels in the temporal lobe were 97.96% and 99.61%, respectively.
This study showed the possibility of using only a few sleep EEG channels for depression screening, which may greatly facilitate the diagnosis of depression outside the hospital.