Detecting Sleep Disorders from NREM Using DeepSDBPLM

Sleep disorders have negative effects on human health. Sleep Disorder Breathing (SDB) and Periodic Leg Movement (PLM) are common sleep disorders that happen during sleep. Early detection of SDB and PLM from Non-Rapid Eye Movement (NREM) can protect patients from hypertension and cardiovascular diseases. In this study, we propose a novel deep learning architecture DeepSDBPLM for classifying Normal, SDB and PLM from NREM using Electroencephalogram (EEG) and Electromyogram (EMG) signals. Our proposed model is tested in three different classification problems using ISRUC-Sleep database. The results show that our proposed model achieves the best result of F1 score as compared to the state-of-the-art techniques.