Temporal-scale dependent dynamical characteristics of EEG reflecting circadian rhythms

Abstract

Estimating circadian rhythm disturbance is important for differentiating between mental illness and healthy states. Electroencephalogram (EEG) allows brain activity detection directly; however, the recorded signal combines neural activity across multiple time scales, which has been previously quantified using the multiscale entropy (MSE) analysis. We investigated whether MSE analysis of EEG data can detect circadian rhythms. Our results demonstrated increased brain activity complexity in the temporal scale in the daytime; moreover, these changes were more accurately detected by MSE than conventional power analysis. Our method can be applied for EEG-based analysis of circadian rhythms in clinical and healthcare fields.