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Sigmoid Wake Probability Model for High-Resolution Detection of Drowsiness Using Electroencephalogram

文献类型： 会议论文

第一作者： Ahnaf Rashik Hassan

作者： Ahnaf Rashik Hassan 1 ; Muammar Kabir 2 ; Behrang Keshavarz 3 ; Babak Taati 2 ; Azadeh Yadollahi 2 ;

作者机构： 1.the KITE, Toronto Rehabilitation Institute-University Health Network, and Institute of Biomaterials & Biomedical Engineering (IBBME), University of Toronto, ON M5S 3G9, Canada

2.IBBME, University of Toronto and KITE, Toronto Rehabilitation Institute-University Health Network, 550 University Ave, Toronto, ON M5G 2A2, Canada

3.the Ryerson University and KITE, Toronto Rehabilitation Institute-University Health Network

关键词： EEG;driving study;low-resolution

会议名称： International Conference of the IEEE Engineering in Medicine and Biology Society

主办单位：

页码： 7080-7083

摘要： An efficient and reliable method to detect drowsiness can reduce accidents and injuries related to drowsy driving. However, existing systems for detecting drowsiness are often of low-resolution, expensive, and dependent on external parameters. Therefore, the goal of this study is to develop a high-resolution and efficient drowsiness detection algorithm using relatively less noisy sleep study data. To this end, we recorded electroencephalogram (EEG) from 53 subjects during a sleep study and leveraged the EEG frequency band changes at sleep onset to develop a model for drowsiness detection. The model devised herein provided a likelihood of wakefulness for 3-s signal segments. By choosing appropriate thresholds of the model output, we have identified three clusters that represent wakefulness, drowsiness, and, sleep. The proposed scheme has been validated using arousals which are cases of alertness and deep sleep segments, cluster quality evaluation metrics, graphical, and statistical analyses. The results presented in this work suggest that spectral properties of EEG can be utilized for high-resolution drowsiness detection in sleep study. Upon its successful validation in a driving study, the proposed model can lead to the development of an efficient drowsy driving monitoring system.

分类号： r318-532

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