A-phase Occurrence During Sleep in the Deep Brain Recordings: Multiscale-Entropy and Multiscale-DFA Analysis

Area of Science:

• Neuroscience
• Sleep Science
• Signal Processing

Background:

• The temporal occurrence of A-phases (TOAP) is a key characteristic of sleep dynamics.
• Understanding TOAP across different brain depths is crucial for elucidating sleep regulation mechanisms.
• Electroencephalogram (EEG) signals provide insights into brain activity during sleep.

Purpose of the Study:

• To evaluate the temporal occurrence of A-phases (TOAP) in superficial and deep EEG recordings.
• To investigate the self-similarity and regulation dynamics of TOAP across different brain depths during sleep.
• To characterize TOAP in scalp, neocortex (NC), and hippocampus (HPC) signals.

Main Methods:

• Analysis of sleep EEG recordings from 10 epileptic patients.
• Representation of TOAP as a binary series (1 for presence, 0 for absence).
• Application of Detrended Fluctuation Analysis (DFA) and entropy metrics (Shannon entropy) for scale-free and pattern analysis.

Main Results:

• Consistent correlation properties in TOAP were found across scalp and deep brain recordings via DFA.
• Monoscale DFA showed persistent long-range correlations; multiscale DFA revealed scale-dependent changes in the scaling exponent.
• Entropy analysis indicated that TOAP pattern distribution varies with brain region and scale, with higher diversity at the scalp and lower in NC and HPC. Shannon entropy correlated positively with cyclic alternating pattern rate.

Conclusions:

• TOAP exhibits consistent scaling across brain regions, suggesting a unified temporal structure and a potential global EEG modulation mechanism during sleep.
• Entropy variations highlight the influence of anatomical location and analysis scale on A-phase occurrence.
• A potential relationship exists between the symbolic entropy of TOAP patterns and sleep instability.