You are viewing a javascript disabled version of the site. Please enable Javascript for this site to function properly.
Go to headerGo to navigationGo to searchGo to contentsGo to footer
In content section. Select this link to jump to navigation

Patient prognosis based on feature extraction, selection and classification of EEG periodic activity

Issue title: Frontiers in Biomedical Engineering and Biotechnology – Proceedings of the 4th International Conference on Biomedical Engineering and Biotechnology, 18–21 August 2015, Shanghai, China

Guest editors: Feng Liu, Dong-Hoon Lee, Ricardo Lagoa and Sandeep Kumar

Article type: Research Article

Authors: Sánchez-González, Alaina; * | García-Zapirain, Begoñaa | Maestro Saiz, Iratxeb | Yurrebaso Santamaría, Izaskunb

Affiliations: [a] DeustoTech-Life, University of Deusto, Av de las Universidades, 24, 48007, Biscay, Spain | [b] Cruces University Hospital and Bio-Cruces Health Research Institute, Biscay, Basque Country, Spain

Correspondence: [*] Address for correspondence: Alain Sánchez-González, DeustoTech-LIFE, University of Deusto, Av de las Universidades, 24, 48007, Biscay, Spain. Tel.: +34 944 13 90 00; Fax: 944 456 817; E-mail: [email protected].

Keywords: Medical classification, bioinformatics, feature selection, EEG periodic activity

DOI: 10.3233/BME-151456

Journal: Bio-Medical Materials and Engineering, vol. 26, no. s1, pp. S1569-S1578, 2015

Published: 2015
Get PDF open access

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.

Show more