Automatic detection of drowsiness using in-ear EEG
Automatic detection of drowsiness using in-ear EEG
复制标题
使用入耳式脑电图自动检测睡意
DOI：
10.1109/ijcnn.2018.8489723
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
D. Mandic
中科院分区：
文献类型：
--
作者：
Takashi Nakamura;Y. Alqurashi;M. Morrell;D. Mandic
可穿戴式脑电图（EEG）睡眠监测最近在研究界得到了验证和报道。其中一个设备是我们的超可穿戴、不显眼、不显眼的耳内脑电图系统，它已经被证明是诊所外睡眠监测的下一代解决方案。我们在这里进一步证明了耳-脑电图在现实世界白天嗜睡监测中的实用性。严格地说，催眠图是从23个受试者的白天午睡记录中手动获得的，而从头皮和耳朵的eeg记录中提取的复杂性科学特征-结构复杂性-在分类阶段被使用，并与二分类支持向量机（SVM）结合使用。耳- eeg分类准确率为80.0% ~ 82.9%，头皮- eeg分类准确率为86.8% ~ 88.8%。考虑到众所周知的困倦相关脑电图变化难以分类（类似于NREM阶段1的问题），这最终证实了耳内脑电图自动轻度睡眠分类的可行性。这也有望成为现实世界中连续、谨慎、用户友好的可穿戴式门诊外睡意监测的关键基石，在监测飞行员、火车司机和远程操作员的身心状态方面有许多应用。
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.
影响因子：
2.7
作者：
Ahmed, Mosabber U.;Chanwimalueang, Theerasak;Mandic, Danilo P.
通讯作者：
Mandic, Danilo P.
影响因子：
2.9
作者：
Bleichner MG;Debener S
通讯作者：
Debener S
影响因子：
8.6
作者：
Costa, M;Goldberger, AL;Peng, CK
通讯作者：
Peng, CK