TY  - JOUR
PY  - 2019//
TI  - Sigmoid wake probability model for high-resolution detection of drowsiness using electroencephalogram
JO  - Conference proceedings - IEEE engineering in medicine and biology society
A1  - Hassan, Ahnaf Rashik
A1  - Kabir, Muammar
A1  - Keshavarz, Behrang
A1  - Taati, Babak
A1  - Yadollahi, Azadeh
SP  - 7080
EP  - 7083
VL  - 2019
IS  - 
N2  - 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.<p />  <p>Language: en</p>
LA  - en
SN  - 1557-170X
UR  - http://dx.doi.org/10.1109/EMBC.2019.8857801
ID  - ref1
ER  -