Why the study?
Estimating circadian rhythm disturbance is important for differentiating mental illness from healthy states, but whether multiscale entropy analysis of EEG data can detect circadian rhythms remained to be investigated.
Does multiscale entropy (MSE) analysis of EEG data detect circadian rhythm differences in brain activity complexity between day and night in healthy young males?
Does multiscale entropy (MSE) analysis of EEG data detect circadian rhythm differences in brain activity complexity between day and night in healthy young males?
Multiscale entropy analysis of EEG signals at slow temporal scales can accurately detect circadian rhythm changes in brain activity complexity, outperforming conventional power analysis.
Multiscale entropy may detect circadian EEG complexity shifts missed by power analysis; leaves open validation before any clinical use.
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.
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Iinuma et al. (2022) studied this question.
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