title: Identification of electroencephalographic arousals in multichannel sleep recordings.
abstract: 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 by the physician. This is a time-consuming task, hence, automation of this process is pursued. The analysis, which involves the correlation of various events in time occurring among the different channels, in conjunction with the complexity of the related biomedical signals, makes this task also difficult to achieve in the computer algorithm. In this paper, we present a method for the detection of EEG arousals working on multichannel PSGs. The algorithm detects arousals using the information available through two EEG channels and the electromyography. A signal-processing technique is first proposed for the analysis of biomedical signals and extraction of relevant information. Individual events are detected from the signals and subsequently are related in time. Finally, a classification phase carries out the final decision on the presence of the event. Classifiers based on Fisher's linear and quadratic discriminants, support vector machines and artificial neural networks are compared at this phase. Experiments conducted on 20 patients reported a sensitivity and specificity respectively of 0.86 and 0.76 in the detection of the arousal events.
authors.affiliation: Laboratory for the Research and Development of Artificial Intelligence, University of A Coruña, Galicia 15071, Spain. diego.alvareze@udc.es
authors.forename: D
authors.initials: D
authors.lastname: Alvarez-Estévez
authors.forename: V
authors.initials: V
authors.lastname: Moret-Bonillo
mesh_terms: D000465:Algorithms; D001143:Arousal / Q000502:physiology*; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D017286:Polysomnography / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted