Automatic sleep stage classification

Automated sleep stage classification is essential for alleviating the burden of physicians since a large volume of data have to be analyzed per examination. Most of the existing works in the literature are multichannel based or yield poor classification performance. A single-channel based computerized sleep staging scheme that gives good performance is yet to emerge. In this work, we introduce a novel noise assisted decomposition scheme to perform automatic sleep stage classification from single channel EEG signals. At first, we decompose the EEG signal segments into mode functions using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). Various statistical moment based features are then computed from these mode functions. The effectiveness of statistical moment based features is validated by statistical analysis. In this work, we also introduce Adaptive Boosting for sleep stage classification. Experimental outcomes manifest that the computerized sleep staging scheme propounded herein outperforms the state-of-the-art ones in various cases of interest.

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