Sleep is a natural periodic state of rest for the body, in which the eyes are usually closed and consciousness is completely or partially lost. Consequently, there is a decrease in bodily movements and responsiveness to external stimuli. In this pilot study, we performed power spectral estimation of EEG signals by Autoregressive (AR) modeling, and then used Itakura Distance to measure the degree of similarity between an EEG baseline and EEG epochs for the entire sleep study. Sleep data from twenty-five subjects (21 males and 4 females, age: 50 ± 10 years, range 28–68 years) from Physionet database were used. We found that Itakura Distance was the smallest for sleep stages similar to the baseline. We intend to deploy this feature as an important element in automatic classification of sleep stages. Results show that trends provided by this feature could discern between sleep stages with a very high level of statistical significance p<0.01.

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