ECG-based automatic sleep staging using hidden Markov model

Abstract

In this paper, an automatic sleep stages recognition system based on the electrocardiogram (ECG) signals is developed. The reason that ECG signals are used for sleep staging is that the device for measuring ECG signals is cheap and is portable. So, the sleep staging can then be performed at home. In this study, some ECG sleep features used in other research are adopted. These features are used to train the Hidden Markov Model (HMM) model and then fed into the trained HMM for recognition. Unlike the existing research on sleep staging by HMM, in which the modeling of HMM is independent of the special properties of the sleep stage transition, the HMM in this study is adjusted to meet these properties. With this method, the accuracy of sleep staging can be improved. The experimental results show that the proposed method enhances the recognition rate compared with other existing research.