A Hierarchical Approach for the Diagnosis of Sleep Disorders Using Convolutional Recurrent Neural Network

Abstract

Sleep is an essential criterion for health. However, sleep disorders degrade the sleep quality. Hence, to diagnose sleep disorders, sleep monitoring is crucial. The cyclic alternating patterns (CAP) phases describe the sleep quality. However, CAP detection is a time-consuming, hectic, and uncertain process. Therefore, an automatic detection of CAP phases is necessary. This study proposes a hierarchical approach to identify sleep disorders and classify CAP phases. Single-channel EEG recording provided by the CAP sleep database has been utilized in this study. The proposed approach classifies CAP sequence into healthy or unhealthy. Further, it identifies sleep disorder of unhealthy sequence among periodic leg movement (PLM), rapid eye movement behaviour disorder (RBD), nocturnal frontal lobe epilepsy (NFLE), narcolepsy (NARCO), and insomnia (INS). Further using our prior work, the CAP phase of the sequence can be identified. The best model was obtained by long short-term memory (LSTM) along with convolutional neural network (CNN) for healthy-unhealthy, and disease classification with an accuracy of 91.45% and 90.55%, respectively. The same models gave an accuracy of 92.79% for healthy-unhealthy and 93.31% for disease classification when evaluated using dataset of only phase B, highlighting the importance of phase B for identifying sleep disorders.

Keywords

• Convolutional neural network (CNN)
• cyclic alternating patterns (CAP)
• deep learning
• electroencephalogram (EEG)
• long short-term memory (LSTM)
• sleep disorders classification