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Related Concept Videos

Stages of Sleep01:22

Stages of Sleep

Sleep progresses through distinct stages, each characterized by specific brain wave patterns and physiological responses ranging from wakefulness to stages of non-rapid eye movement, known as non-REM, to rapid eye movement, referred to as REM. Understanding these stages helps in recognizing how sleep supports various bodily and cognitive functions.
Before sleep begins, in wakefulness, the brain exhibits primarily beta waves, which are high in frequency and low in amplitude, indicating alertness...
Brain Waves01:23

Brain Waves

Brain waves are electrical signals generated by the neurons in the brain, which are regularly monitored to measure mental activities. Brain waves and their frequency ranges can be measured using an electroencephalogram or EEG. There are four main types of brain waves, each with distinct characteristics:

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Related Experiment Video

Updated: Jun 17, 2026

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

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

Raquel Delgado-Aranda1,2, Juergen Fell3, David Ibarra-Medina4

  • 1Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy. r.delgadoaranda@gmail.com.

Brain Topography
|June 16, 2026
PubMed
Summary
This summary is machine-generated.

Temporal occurrence of A-phases (TOAP) shows consistent scaling across brain regions during sleep, suggesting a unified temporal structure. Variations in entropy indicate that anatomical location and scale influence A-phase occurrence, potentially linking to sleep instability.

Keywords:
A-phaseCyclic alternating patternDetrended fluctuation analysisEntropyMultiscale analysisSleep

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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.