Scoring Transitions to REM Sleep in Rats Based on the EEG Phenomena of Pre-REM Sleep: An Improved Analysis of Sleep Structure | Zendy

Joel H. Benington | Zendy; Susheel Kodali | Zendy; H. Craig Heller | Zendy

Open Access

Scoring Transitions to REM Sleep in Rats Based on the EEG Phenomena of Pre-REM Sleep: An Improved Analysis of Sleep Structure

Author(s) -

Joel H. Benington,

Susheel Kodali,

H. Craig Heller

Publication year - 1994

Publication title -

sleep

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 2.222

H-Index - 207

eISSN - 1550-9109

pISSN - 0161-8105

DOI - 10.1093/sleep/17.1.28

Subject(s) - non rapid eye movement sleep , electroencephalography , sleep (system call) , sleep stages , eye movement , psychology , k complex , audiology , sleep spindle , rapid eye movement sleep , delta rhythm , slow wave sleep , sleep onset , polysomnography , neuroscience , medicine , insomnia , theta rhythm , psychiatry , computer science , operating system

Algorithms for scoring sleep/waking states and transitions to REM sleep (NRTs) in rats are presented and validated. Both algorithms are based on electroencephalographic (EEG) power in delta (0.5-4.0 Hz), theta (6-9 Hz) and sigma (10-14 Hz) frequency bands, and electromyogram (EMG) intensity. Waking is scored when EMG intensity is high or (sigma power).(theta power) is low. Nonrapid eye movement (NREM) sleep is scored in nonwaking epochs having high (delta power)/(theta power). Rapid eye movement (REM) sleep is scored in nonwaking epochs having low (delta power)/(theta power). NRTs are identified by the EEG phenomena of the pre-REM sleep phase of NREM sleep. Algorithms are validated by comparison with records scored independently by two investigators based on visual examination of EEGs and EMGs. The sleep/waking-state scoring algorithm produces greater than 90% agreement with visual scoring. The NRT-scoring algorithm produces 88-92% agreement with visual scoring. Scoring NRTs based on the phenomena of the pre-REM sleep phase of NREM sleep, instead of relying solely on REM sleep expression for identification of REM sleep onset, reveals a significant population of brief REM sleep episodes that are ignored by most sleep cycle analyses and allows independent quantification of REM sleep timing and maintenance.

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