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More importantly, theta band SL on the first EEG proved to be a significant predictor of the diagnosis 'epilepsy'epilepsy
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Synchronization between the SR-BIS and SR-EEG data proved to be a challenge.
The paper is based on the fact that features extracted from electroencephalogram (EEG) recordings have proved to be unique enough across subjects for biometric applications.
Electroencephalograms (EEGs) have proven to be a useful tool for monitoring the brain activity of adults undergoing anesthesia, but studies have found EEG-based monitoring to be unreliable in infants under anesthesia.
EEG has been proven to be a kind of nonlinear, nonstationary, and chaotic time series [ 5], providing information about spatiotemporal patterns of brain electrical activity [ 6].
In several studies spectral analysis of EEG has been proven to be a valuable, sensitive and observer –independent method to detect minimal HE [ 19, 26], therefore it can be used as a reference tool and we applied it in our study as well.
Cloostermans et al. [ 27] proved that continuous EEG patterns within 12 hrs predicted good outcome while an isoelectric or low-voltage electroencephalograms after 24 hrs predicted poor outcome in patients treated with hypothermia.
The singular values and vectors have proved useful in characterizing EEG abnormalities [13, 74].
Scientific data have proved utility of continuous EEG monitoring in intensive care [ 1, 2].
Simultaneous EEG and NIR methods have also proved to be of considerable interest, not least because these techniques are relatively inexpensive, mutually compatible and applicable at the bedside.
The quantitative evaluation using simulated EEGs with outliers proves that the proposed Lp (p ≤ 1) AR can estimate the AR parameters more robustly than the Yule Walker, Burg and LS methods, under various simulated outlier conditions.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com