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