Automated classification of cyclic alternating pattern sleep phases in healthy and sleep-disordered subjects using convolutional neural network.

Sleep contributes to more than a third of a person's life, making sleep monitoring essential for overall well-being. Cyclic alternating patterns (CAP) are crucial in monitoring sleep quality and associated illnesses such as insomnia, nocturnal frontal lobe epilepsy (NFLE), narcolepsy, etc. However, traditionally medical specialists practice manual division techniques of CAP phases which are sensitive to human weariness and inaccuracies. This might result in a false sleep stage diagnosis. This study proposes an automated approach using a deep learning model based on a 1-dimensional convolutional neural network for classifying CAP phases (A and B). The proposed model uses single-channel standardized electroencephalogram (EEG) recordings provided by the CAP sleep database. The model was created with the help of healthy participants and patients suffering from five distinct sleep disorders, which includes narcolepsy, rapid eye movement behaviour disorder (RBD), periodic leg movement disorder (PLM), NFLE, and insomnia. The developed model has achieved the highest automated classification accuracy of 78.84% for the healthy dataset and 82.21%, 79.48%, 78.73%, 76.68%, and 70.88% for narcolepsy, RBD, PLM, NFLE, and insomnia subjects, respectively in categorizing phases A and B. The proposed approach can help medical professionals monitor sleep and examine a person's brain stability.