Paper Title
Classification of REM and NREM Sleep Stages with/without Sleep Disorder Breathing using DeepSDBnet

The diagnosis of sleep-related disorders, such as Sleep Disorder Breathing (SDB) disease, highly depends on the classification of sleep stages. Previous studies focus on sleep stages classification as the first step in detecting sleep-related diseases. However, classification of sleep stages segments with or without a specific sleep-related disease is challenging. In this study, we focus on the classification of REM and NREM sleep stages with or without SDB events. We propose in this paper a novel deep learning architecture, named Deep SDBNet which comprises three deep learning networks based on Convolutional Neuron Networks (CNN). All three deep learning networks are trained separately. The first two deep learning networks extract important features from EEG, EMG and EOG signals while the third deep learning network extracts important features from the concatenated features produced by the first two deep learning networks. Deep SDBNet classifies the segments into four classes: Normal NREM, Normal REM, SDB NREM and SDB REM. To our knowledge, this is the first study to propose an architecture for this classification using EEG, EMG and EOG signals. ISRUC-Sleep database is used for evaluating the performance of our proposed model. The average accuracy, sensitivity, specificity and F1 score are 78.16%, 55.87%, 85.23%, and 54%, respectively. Keywords - Obstructive Sleep Apnoea, Hypopnoea, CenteralApnoea, Mixed Apnoea, Sleep Disorder Breathing, EEG, EMG, EOG, Deep Learning, Classification, Convolutional Neural Networks, REM NREM.