Automated CAP Phase Detection in EEG for Sleep Disorder Analysis using Deep Learning

This paper proposed an automatic CAP phases classification system which combines CWT and Light Weight CNN for efficient analysis of sleep instability based on EEG signals. The proposed framework successfully convert non-stationary EEG signals into informative time-frequency scalogram features to differentiate among different CAP subclasses such as A1, A2, A3 phase. From the experimental results, the proposed system achieved significant improvements of Accuracy, Precision, Recall, F1-Score but has decreased complexity. Analysis from the confusion matrix and training analysis indicates that the framework can efficiently classify and have excellent generalization. The lightweight CNN based model is adaptable for on-line clinical diagnosis systems and medical devices that face memory constraint issues. In future, we can investigate the possibility of integrating transformer networks and multimodal PSG signal features into the developed framework and also apply this system for real-time sleep monitoring. 

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