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Electroencephalographic arousals are defined as abrupt shifts in electroencephalogram (EEG) frequency during sleep. Occurrence of arousals results in fragmented sleep, being one of the most important causes of daytime sleepiness among sleep disorders. Detection of arousals requires a polysomnographic (PSG) recording to be made during the patient's sleep. The resulting PSG is then analyzed offline...
The problem of patient disorder classification and prediction from biological signals is addressed. We approach the problem from the perspective of nonlinear dynamical systems. Explored signals are ECG and EEG. We propose a combination of linear and nonlinear features for classification of four different types of heart rhythms through heart rate variability analysis. Classification accuracy is evaluated...
The daily life of epilepsy patients is constrained by the possibility of occurrence of seizures. Until now, seizures cannot be predicted with sufficient sensitivity and specificity. Most of the seizure prediction studies have been focused on a small number of patients, and frequently assuming unrealistic hypothesis. This paper adopts the view that for an appropriate development of reliable predictors...
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