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

Purpose Sleep arousal detection is an important factor to monitor the sleep disorder. Design/methodology/approach Thus, a unique n th layer one-dimensional (1D) convolutional neural network-based U-Net model for automatic sleep arousal identification has been proposed. Findings The 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/value No other researchers have suggested U-Net-based detection of sleep arousal. Research limitations/implications From the experimental results, it has been found that U-Net performs better accuracy as compared to the state-of-the-art methods. Practical implications Sleep 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 implications It will help in improving mental health by monitoring a person's sleep.