title: Patient prognosis based on feature extraction, selection and classification of EEG periodic activity.
abstract: Periodic activity in electroencephalography (PA-EEG) is shown as comprising a series of repetitive wave patterns that may appear in different cerebral regions and are due to many different pathologies. The diagnosis based on PA-EEG is an arduous task for experts in Clinical Neurophysiology, being mainly based on other clinical features of patients. Considering this difficulty in the diagnosis it is also very complicated to establish the prognosis of patients who present PA-EEG. The goal of this paper is to propose a method capable of determining patient prognosis based on characteristics of the PA-EEG activity. The approach, based on a parallel classification architecture and a majority vote system has proven successful by obtaining a success rate of 81.94% in the classification of patient prognosis of our database.
authors.affiliation: DeustoTech-Life, University of Deusto, Av de las Universidades, 24, 48007, Biscay, Spain.
authors.forename: Alain
authors.initials: A
authors.lastname: Sánchez-González
authors.affiliation: DeustoTech-Life, University of Deusto, Av de las Universidades, 24, 48007, Biscay, Spain.
authors.forename: Begoña
authors.initials: B
authors.lastname: García-Zapirain
authors.affiliation: Cruces University Hospital and Bio-Cruces Health Research Institute, Biscay, Basque Country, Spain.
authors.forename: Iratxe
authors.initials: I
authors.lastname: Maestro Saiz
authors.affiliation: Cruces University Hospital and Bio-Cruces Health Research Institute, Biscay, Basque Country, Spain.
authors.forename: Izaskun
authors.initials: I
authors.lastname: Yurrebaso Santamaría
mesh_terms: D000465:Algorithms; D001927:Brain Diseases / Q000175:diagnosis*; D020000:Decision Support Systems, Clinical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D009991:Oscillometry / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D011379:Prognosis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D060388:Support Vector Machine
keywords: EEG periodic activity; Medical classification; bioinformatics; feature selection