Temporal complexity of EEG encodes human alertness

In this study, we propose that balanced dynamics of Electroencephalography (EEG) (so called EEG temporal complexity) is a potentially usef ul feature for identifying human alertness states. Recently, a new signal entropy measure, called range entropy (RangeEn), was proposed to overcome some limitations of two of the most widely used entropy measures, namely approximate entropy (ApEn) and Sample Entropy (SampEn), and showed its relevanc e for the study of time domain EEG complexity. In this paper, we investigated whether the RangeEn holds discriminating information associated with human alertness states, namely awake, drowsy, and sleep and compare its performance against those of SampEn and ApEn. Approach. We used EEG data from 60 healthy subjects of both sexes and different ages acquired during whole night sleeps. Using a 30 s sliding window, we computed the three entropy measures of EEG and performed statistical analyses to evaluate the ability of these entropy measures to discriminate among the different human alertness st ates. Main results. Although the three entropy measures contained useful information about human alertness, RangeEn showed a higher discriminative capability compared to ApEn and SampEn especially when using EEG within the beta frequency band. Significance. Our findings highlight the EEG temporal co mplexity evolution through the human alertness states. This relationship can potentially be exploited for the development of automatic human alertness monitor...
Source: Physiological Measurement - Category: Physiology Authors: Source Type: research