da Silva Souto Carlos F, Pätzold Wiebke, Wolf Karen Insa, Paul Marina, Matthiesen Ida, Bleichner Martin G, Debener Stefan
Branch for Hearing, Speech and Audio Technology HSA, Fraunhofer Institute for Digital Media Technology IDMT, Oldenburg, Germany.
PSG-Auswertungs-Service, Stadtlohn, Germany.
Front Digit Health. 2021 Jun 30;3:688122. doi: 10.3389/fdgth.2021.688122. eCollection 2021.
A comfortable, discrete and robust recording of the sleep EEG signal at home is a desirable goal but has been difficult to achieve. We investigate how well flex-printed electrodes are suitable for sleep monitoring tasks in a smartphone-based home environment. The cEEGrid ear-EEG sensor has already been tested in the laboratory for measuring night sleep. Here, 10 participants slept at home and were equipped with a cEEGrid and a portable amplifier (mBrainTrain, Serbia). In addition, the EEG of Fpz, EOG_L and EOG_R was recorded. All signals were recorded wirelessly with a smartphone. On average, each participant provided data for = 7.48 h. An expert sleep scorer created hypnograms and annotated grapho-elements according to AASM based on the EEG of Fpz, EOG_L and EOG_R twice, which served as the baseline agreement for further comparisons. The expert scorer also created hypnograms using bipolar channels based on combinations of cEEGrid channels only, and bipolar cEEGrid channels complemented by EOG channels. A comparison of the hypnograms based on frontal electrodes with the ones based on cEEGrid electrodes (κ = 0.67) and the ones based on cEEGrid complemented by EOG channels (κ = 0.75) both showed a substantial agreement, with the combination including EOG channels showing a significantly better outcome than the one without ( = 0.006). Moreover, signal excerpts of the conventional channels containing grapho-elements were correlated with those of the cEEGrid in order to determine the cEEGrid channel combination that optimally represents the annotated grapho-elements. The results show that the grapho-elements were well-represented by the front-facing electrode combinations. The correlation analysis of the grapho-elements resulted in an average correlation coefficient of 0.65 for the most suitable electrode configuration of the cEEGrid. The results confirm that sleep stages can be identified with electrodes placement around the ear. This opens up opportunities for miniaturized ear-EEG systems that may be self-applied by users.
在家中舒适、隐秘且稳定地记录睡眠脑电图信号是一个理想目标，但一直难以实现。我们研究了柔性印刷电极在基于智能手机的家庭环境中对睡眠监测任务的适配程度。cEEGrid耳脑电图传感器已在实验室中进行过夜睡眠测量测试。在此，10名参与者在家中睡眠，并配备了cEEGrid和便携式放大器（mBrainTrain，塞尔维亚）。此外，还记录了Fpz、EOG_L和EOG_R的脑电图。所有信号均通过智能手机无线记录。平均而言，每位参与者提供了7.48小时的数据。一位专业睡眠评分员根据Fpz、EOG_L和EOG_R的脑电图，按照美国睡眠医学学会（AASM）标准创建了睡眠图并标注了图形元素，进行了两次，作为进一步比较的基线一致性标准。该专业评分员还仅基于cEEGrid通道的组合以及由EOG通道补充的双极cEEGrid通道创建了睡眠图。基于额电极的睡眠图与基于cEEGrid电极的睡眠图（κ = 0.67）以及基于由EOG通道补充的cEEGrid的睡眠图（κ = 0.75）的比较均显示出高度一致性，其中包含EOG通道的组合显示出比不包含EOG通道的组合显著更好的结果（P = 0.006）。此外，对包含图形元素的传统通道的信号片段与cEEGrid的信号片段进行相关性分析，以确定能最佳代表标注图形元素的cEEGrid通道组合。结果表明，图形元素在前向电极组合中得到了很好的呈现。对于cEEGrid最合适的电极配置，图形元素的相关性分析得出平均相关系数为0.65。结果证实，通过耳部周围的电极放置可以识别睡眠阶段。这为用户可能自行应用的小型化耳脑电图系统开辟了机会。
