The use of permutation entropy to characterize sleep electroencephalograms

This work proposes the use of Permutation Entropy (PE), a measure of time-series complexity, to characterize electroencephalogram (EEG) signals recorded during sleep. Such a measure could provide information concerning the different sleep stages and, thus, be utilized as an additional aid to obtain sleep staging information. PE has been estimated for artifact-free 30s segments from more than 80 hours of EEG records obtained from 16 subjects during all-night recordings, from which the mean PE for each sleep stage was obtained. It was found that different sleep stages are characterized by significantly different PE values, which track the physiological changes in the complexity of the EEG signals observed at the different sleep stages. This finding encourages the use of PE as an additional aide to either visual or automated sleep staging.