EEG Sleep Stages Analysis and Classification Based on Weighed Complex Network Features

Manual staging of sleep data from EEG signals is time consuming, error prone, and resource intensive. These limitations lead to continuous interests of researchers and clinicians toward the automatic sleep stages classification of EEG signals. This paper proposes a scheme for automatic classification of sleep stages using “edge strength of visibility graph technique from single-channel EEG signals. To discover the validity of the proposed study with noise, we perform simulation analysis with two different time series named Lorenz and Rossler time series. The proposed technique accomplishes better performance than other related work for the two standard groups of sleep stages: Rechtschaffen and Kales standard and American Academy of Sleep Medicine.