Sleep arousal detection for monitoring of sleep disorders using one-dimensional convolutional neural network-based U-Net and bio-signals

Abstract

PurposeSleep arousal detection is an important factor to monitor the sleep disorder.Design/methodology/approachThus, a unique nth layer one-dimensional (1D) convolutional neural network-based U-Net model for automatic sleep arousal identification has been proposed.FindingsThe proposed method has achieved area under the precision–recall curve performance score of 0.498 and area under the receiver operating characteristics performance score of 0.946.Originality/valueNo other researchers have suggested U-Net-based detection of sleep arousal.Research limitations/implicationsFrom the experimental results, it has been found that U-Net performs better accuracy as compared to the state-of-the-art methods.Practical implicationsSleep arousal detection is an important factor to monitor the sleep disorder. Objective of the work is to detect the sleep arousal using different physiological channels of human body.Social implicationsIt will help in improving mental health by monitoring a person's sleep.