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

Abstract

Purpose

Sleep arousal detection is an important factor to monitor the sleep disorder.

Design/methodology/approach

Thus, a unique nth 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.