SleepPACNet: new convolutional neural network considering phase-amplitude coupling for automatic sleep stage classification using single-channel electroencephalogram

Abstract

Automatic sleep stage classification (ASSC) using single-channel electroencephalogram (EEG) signals is an essential technology for developing wearable EEG devices for sleep monitoring. Deep learning (DL)-based methods, although effective in automatically extracting features, often overlook significant EEG characteristics containing domain-specific knowledge relevant to particular sleep stages, such as cross-frequency phase-amplitude coupling (PAC) phenomena. To address this limitation, we proposed SleepPACNet, a novel convolutional neural network (CNN) architecture specifically designed to effectively extract PAC-related features from single-channel EEG signals to enhance ASSC performance. Central to our proposed model is a PAC feature extraction module, which employs a CNN layer explicitly designed to integrate the phase information from low-frequency EEG components, obtained using the Hilbert transform, with amplitude information from high-frequency EEG components. The performance of SleepPACNet was evaluated using single-channel (Fp1–Fp2) sleep EEG data extracted from the Dreem dataset to simulate conditions of a portable EEG device measuring prefrontal bipolar EEG signals. SleepPACNet outperformed conventional CNN models, including SleepCNN, achieving the highest classification accuracy of 75.7%. It significantly improved REM stage classification, validating PAC’s effectiveness in REM detection. These findings highlight the potential of automated PAC feature extraction in DL for improving the overall performance of ASSC systems utilizing single-channel EEG.