Nonlinear Feature Extraction of Sleeping EEG Signals

This study calculated the spectrum entropy (SE), approximate entropy (ApEn), and Lem-Ziv complexity (LZC) of sleeping EEG signals of eight healthy adults. The statistical results show that all the three nonlinear features can clearly reflect sleeping stage. Among them, the SE is easy to calculate and traces varying sleeping periods fairly and consistently, while the ApEn performs even better but is relatively complicated. The LZC is also simple but loses information details in its preprocessing of original measurement data, which consequently down grades its consistency. Based on a tradeoff of efficiency and efficacy, we consider the SE would be a good feature for real-time tracing sleep stages. Some conclusions are reported based on this study