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Automatic detection of drowsiness using in-ear EEG

机译：使用入耳式EEG自动检测睡意

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Sleep monitoring with wearable electroencephalography (EEG) has recently been validated and reported in the research community. One such device is our ultra-wearable, unobtrusive, and inconspicuous in-ear EEG system, which has already been demonstrated to be next-generation solution for out-of-clinic sleep monitoring. We here provide a further proof of concept of the utility of ear-EEG in day time drowsiness monitoring in the real-world. For rigour, hypnograms are obtained from manually scored daytime nap recordings from twentythree subjects, while a complexity science feature-structural complexity extracted from scalp- and ear-EEG recordings - is used in the classification stage, in conjunction with a binary-class support vector machine (SVM). The achieved drowsiness classification accuracies range from 80.0% to 82.9% for ear-EEG, with the corresponding accuracies for scalp-EEG ranging from 86.8 % to 88.8 %. Given the notoriously difficult to classify drowsiness related changes in EEG (similar to the issues with the NREM Stage 1), this conclusively confirms the feasibility of in-ear EEG for automatic light sleep classification. This also promises a key stepping stone towards continuous, discreet, and user-friendly wearable out-of-clinic drowsiness monitoring in the real-world, with numerous applications in the monitoring the state of body and mind of pilots, train drivers, and tele-operators.
机译：最近，在可穿戴式脑电图（EEG）中进行睡眠监测已得到验证，并在研究社区中得到了报道。我们的超耐磨，不显眼且不起眼的入耳式EEG系统就是这样的一种设备，该系统已被证明是用于诊所外睡眠监测的下一代解决方案。在这里，我们提供了耳式心电图在现实世界中的日间睡意监测中的效用的概念的进一步证明。为了严格起见，从二十三名受试者的手动打day日间小睡记录中获得催眠图，而在分类阶段结合二元类支持向量，使用从头皮和耳式EEG记录中提取的复杂性科学特征-结构复杂性机器（SVM）。耳EEG的嗜睡分类精度达到80.0％至82.9％，头皮EEG的相应精度达到86.8％至88.8％。鉴于很难对与嗜睡相关的脑电变化进行分类（类似于NREM第1阶段的问题），因此，这最终证实了入耳式脑电图对于自动轻度睡眠分类的可行性。这也有望成为在现实世界中进行连续，谨慎和用户友好的可穿戴式临床外睡意监测的重要踏脚石，在监控飞行员，火车驾驶员和电话的身心状态方面有许多应用-操作员。

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