Detecting Driver Sleepiness From Physiological Indicators Using a CNN-LSTM Self-Attention Model

Sleepiness at the wheel is an important factor contributing to road traffic accidents. Based on the characteristic changes in Electroencephalography (EEG) and Electrooculography (EOG) signals, a dozing state is refined into three sub-states: the onset, duration, and end state. Each state is characterized by different physiological indicators such as the EEG alpha waves, the rising edge, and falling edge waveforms in EOG signals. To enable real-time detection of these physiological indicators, we propose a framework integrating three Convolutional Neural Network-Long Short-Term Memory-Self-Attention (CLSA) models, which combine CNN-based local feature extraction with self-attention mechanism for global context capture. The framework is evaluated for performance on continuous test data from 12 subjects. Our results demonstrate that by detecting alpha waves and the rising edge waveform, the alpha wave epoch (AWE) at the onset of the dozing state can be identified with high accuracy and precision. Thus, the onset sub-state is calculated as the period from the start time of the rising edge waveform to the time when the AWE is valid. Subsequently, the duration sub-state corresponds to the sustained presence of alpha waves. Furthermore, the falling edge waveform is detected with high accuracy, enabling the classification of the end state into two distinct phenomena: alpha blocking phenomenon or alpha wave attenuation-disappearance phenomenon, representing the sleepiness level-relaxed wakefulness or sleep onset, respectively. Utilizing three-channel signal processing, this framework provides a promising approach for real-time sleepiness detection in real-world driving scenarios.