Sleep stage prediction using respiration and body-movement based on probabilistic classifier

Abstract

In this paper, a sleep stage prediction method using respiration and body-movement based on probabilistic classifier is proposed. A pressure sensor is employed to capture respiratory signal. We propose to use least-squares probabilistic classifier (LSPC), a computationally effective probabilistic classifier, for four-class sleep stage classification (wakefulness, rapid-eye movement sleep, light sleep, deep sleep). Thanks to output of posterior probability of each class by LSPC, we can directly handle the confidence of predicted sleep stages. In addition, we introduce a method to handle imbalanced data problem which arises in sleep data collection. The experimental results demonstrate the effectiveness of sleep stage prediction by LSPC.