2005 · 29 citations · 4 references
EngineeringNeuroscienceElectrophysiologySleep MedicineTime Series AnalysisElectroencephalogram AnalysisElectroencephalogram Signal ProcessingElectroencephalographySleep DisturbanceSleep PhysiologySleep Signal AnalysisAutomatic ClassificationPolysomnography AnalysisSleep EegSleep Stage ClassificationSleep StagesSleep StagingSlow-wave ActivitySpectral Estimation
Sleep is a natural periodic state of rest for the body, in which the eyes usually close and consciousness is completely or partially lost. Consequently, there is a decrease in bodily movements and responsiveness to external stimuli. Slow wave sleep is of immense interest as it is the most restorative sleep stage during which the body recovers from weariness. During this sleep stage, electroencephalographic (EEG) and electro-oculographic (EOG) signals interfere with each other and they share a temporal similarity. In this investigation we used the EEG and EOG signals acquired from 10 patients undergoing overnight polysomnography with their sleep stages determined by certified sleep specialists based on RK rules. In this pilot study, we performed spectral estimation of EEG signals by Autoregressive (AR) modeling, and then used Itakura Distance to measure the degree of similarity between EEG and EOG signals. We finally calculated the statistics of the results and displayed them in an easy to visualize fashion to observe tendencies for each sleep stage. We found that Itakura Distance is the smallest for sleep stages 3 and 4. We intend to deploy this feature as an important element in automatic classification of sleep stages.
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Statistical Digital Signal Processing and Modeling
Marvin H. J. Gruber, M.H. Hayes · Technometrics · 1997 · 2.2K citations
EEG feature extraction for classification of sleep stages
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Prolonged endurance exercise and sleep disruption
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Characterization of EEG signal changes via Itakura distance
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Medicine, Neurology, Neuroscience +16