Computer-aided sleep staging using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and bootstrap aggregating

Abstract

Abstract Computer-aided sleep staging based on single channel electroencephalogram (EEG) is a prerequisite for a feasible low-power wearable sleep monitoring system. It can also eliminate the burden of the clinicians during analyzing a high volume of data by making sleep scoring less onerous, time-consuming and error-prone. Most of the prior studies focus on multichannel EEG based methods which hinder the aforementioned goals. Among the limited number of single-channel based methods, only a few yield good performance in automatic sleep staging. In this article, a single-channel EEG based method for sleep staging using recently introduced Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Bootstrap Aggregating (Bagging) is proposed. At first, EEG signal segments are decomposed into intrinsic mode functions. Higher order statistical moments computed from these functions are used as features. Bagged decision trees are then employed to classify sleep stages. This is the first time that CEEMDAN is employed for automatic sleep staging. Experiments are carried out using the well-known Sleep-EDF database and the results show that the proposed method is superior as compared to the state-of-the-art methods in terms of accuracy. In addition, the proposed scheme gives high detection accuracy for sleep stages S1 and REM.