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Decreased electrocortical temporal complexity distinguishes sleep from wakefulness
Joaquín González1, Matias Cavelli1, Alejandra Mondino1
1Universidad de la República, Departamento de Fisiología de Facultad de Medicina, Av. Gral. Flores 2125, 11800, Montevideo, Uruguay.
Scientific Reports
|December 6, 2019
Summary
Permutation Entropy (PeEn) analysis of electrocorticogram (ECoG) signals reveals distinct patterns during wakefulness and sleep. This complexity measure offers a simple yet effective method for sleep monitoring.
Area of Science:
- Neuroscience
- Sleep Science
- Computational Biology
Background:
- Mammalian sleep-wake cycles involve distinct states: wakefulness (W), non-REM (NREM), and REM sleep.
- Thalamo-cortical system activity underlies cognitive changes during these states.
- Electrocorticogram (ECoG) is crucial for measuring thalamo-cortical activity during W and sleep.
Purpose of the Study:
- To analyze rat ECoG recordings using Permutation Entropy (PeEn).
- To investigate the utility of PeEn as a time-series complexity measure for differentiating sleep-wake states.
- To assess PeEn's potential for efficient sleep monitoring.
Main Methods:
- Analysis of broad-band ECoG recordings from rats.
- Application of Permutation Entropy (PeEn), a robust time-series complexity measure.
- Comparison of PeEn values across wakefulness, NREM, and REM sleep states.
Main Results:
- PeEn was found to be maximal during wakefulness and decreased during sleep.
- PeEn analysis revealed distinct thalamo-cortical dynamics associated with different sleep-wake states.
- PeEn determined behavioral states independently of electrode location, indicating a global signal pattern.
Conclusions:
- PeEn analysis effectively differentiates between sleep and wakefulness based on ECoG signals.
- The findings highlight unique thalamo-cortical dynamics during sleep-wake cycles.
- PeEn analysis of a single EEG channel shows promise for cost-effective and efficient sleep monitoring.
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