Patient prognosis based on feature extraction, selection and classification of EEG periodic activity
Cargando...
Fecha
2015-02-01
Título de la revista
ISSN de la revista
Título del volumen
Editor
IOS Press
Resumen
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.
Palabras clave
Bioinformatics
EEG periodic activity
Feature selection
Medical classification
EEG periodic activity
Feature selection
Medical classification
Descripción
Materias
Cita
Sánchez-González, A., García-Zapirain, B., Maestro Saiz, I., & Santamaría, I. Y. (2015). Patient prognosis based on feature extraction, selection and classification of EEG periodic activity. Bio-Medical Materials and Engineering, 26, S1569-S1578. https://doi.org/10.3233/BME-151456