Automatic sleep stage classification is an important
paradigm in computational intelligence and promises consider-
able advantages to the health care. Most current automated
methods require the multiple electroencephalogram (EEG) chan-
nels and typically cannot distinguish the S1 sleep stage from
EEG. The aim of this study is to revisit automatic sleep stage
classification from EEGs using complexity science methods. The
proposed method applies fuzzy entropy and permutation entropy
as kernels of multi-scale entropy analysis. To account for sleep
transition, the preceding and following 30 seconds of epoch data
were used for analysis as well as the current epoch. Combining
the entropy and spectral edge frequency features extracted from
one EEG channel, a multi-class support vector machine (SVM)
was able to classify 93.8% of 5 sleep stages for the SleepEDF
database [expanded], with the sensitivity of S1 stage was 49.1%.
Also, the Kappa’s coefficient yielded 0.90, which indicates almost
perfect agreement.