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A new method for detecting state changes in the EEG: exploratory application to sleep data
Author(s) -
McKeown Martin,
Humphries Colin,
Achermann Peter,
Borbély Alexander,
Sejnowsk Terrence
Publication year - 1998
Publication title -
journal of sleep research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.297
H-Index - 117
eISSN - 1365-2869
pISSN - 0962-1105
DOI - 10.1046/j.1365-2869.7.s1.8.x
Subject(s) - electroencephalography , scalp , non rapid eye movement sleep , sleep (system call) , vigilance (psychology) , audiology , dimensionless quantity , psychology , oscillation (cell signaling) , mathematics , pattern recognition (psychology) , physics , neuroscience , computer science , medicine , chemistry , anatomy , cognitive psychology , biochemistry , mechanics , operating system
A new statistical method is described for detecting state changes in the electroencephalogram (EEG), based on the ongoing relationships between electrode voltages at different scalp locations. An EEG sleep recording from one NREM‐REM sleep cycle from a healthy subject was used for exploratory analysis. A dimensionless function defined at discrete times t i , u(t i ), was calculated by determining the log‐likelihood of observing all scalp electrode voltages under the assumption that the data can be modeled by linear combinations of stationary relationships between derivations. The u(t i ), calculated by using independent component analysis, provided a sensitive, but non‐specific measure of changes in the global pattern of the EEG. In stage 2, abrupt increases in u(t i ) corresponded to sleep spindles. In stages 3 and 4, low frequency (≈ 0.6 Hz) oscillations occurred in u(t i ) which may correspond to slow oscillations described in cellular recordings and the EEG of sleeping cats. In stage 4 sleep, additional irregular very low frequency (≈ 0.05–0.2 Hz) oscillations were observed in u(t i ) consistent with possible cyclic changes in cerebral blood flow or changes of vigilance and muscle tone. These preliminary results suggest that the new method can detect subtle changes in the overall pattern of the EEG without the necessity of making tenuous assumptions about stationarity.