An attention-based multi-resolution deep learning model for automatic A-phase detection of cyclic alternating pattern in sleep using single-channel EEG

Abstract

Sleep is a crucial part of human well-being. Many people suffer from various sleep disorders and insufficient sleep. The detection of the cyclic alternating pattern (CAP) of electroencephalogram (EEG) activity during sleep is essential for identifying and monitoring these problems. In this paper, we present a multi-resolution deep neural network model with temporal and channel attention for detecting A-phase and its subtypes. A multi-branch one-dimensional convolutional neural network (1D-CNN) is employed where each branch has different kernel sizes to extract features of different frequency resolutions automatically. An attention-based transformer network exploits the dynamic and temporal relationship between CAP event features extracted from the single-channel EEG data. Our model achieves 90.31% accuracy, 95.30% specificity, and 65.73% F1-Score in A-phase detection and 86.72% accuracy, 89.53% specificity, and 59.59% F1-Score in the detection of its subtypes, superior performance as compared to those of the recent approaches.