Automatic Micro-sleep Detection under Car-driving Simulation Environment using Night-sleep EEG
Young-Seok Kweon, Gi-Hwan Shin, Heon-Gyu Kwak, Minji Lee

TL;DR
This study proposes a deep learning method using night-sleep EEG data to improve micro-sleep detection during driving, achieving about 30% better performance by leveraging sleep stage classification.
Contribution
The paper introduces a novel approach of pre-training on night-sleep EEG to enhance micro-sleep detection during driving, addressing data collection challenges.
Findings
Micro-sleep resembles early NREM sleep stages.
Pre-trained U-Net improves detection accuracy by 30%.
Night-sleep EEG can be used to infer micro-sleep during driving.
Abstract
A micro-sleep is a short sleep that lasts from 1 to 30 secs. Its detection during driving is crucial to prevent accidents that could claim a lot of people's lives. Electroencephalogram (EEG) is suitable to detect micro-sleep because EEG was associated with consciousness and sleep. Deep learning showed great performance in recognizing brain states, but sufficient data should be needed. However, collecting micro-sleep data during driving is inefficient and has a high risk of obtaining poor data quality due to noisy driving situations. Night-sleep data at home is easier to collect than micro-sleep data during driving. Therefore, we proposed a deep learning approach using night-sleep EEG to improve the performance of micro-sleep detection. We pre-trained the U-Net to classify the 5-class sleep stages using night-sleep EEG and used the sleep stages estimated by the U-Net to detect…
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Taxonomy
MethodsConcatenated Skip Connection · Max Pooling · *Communicated@Fast*How Do I Communicate to Expedia? · Convolution · U-Net
