Search for: recurrence-quantification-analysis--rqa
Article IEEE Journal of Biomedical and Health Informatics ; Volume 17, Issue 3 , 2013 , Pages 572-578 ; 21682194 (ISSN) ; Mousavi, S. R ; Vosoughi Vahdat, B ; Sayyah, M ; Sharif University of Technology
This study presents applying recurrence quantification analysis (RQA) on EEG recordings and their subbands: delta, theta, alpha, beta, and gamma for epileptic seizure detection. RQA is adopted since it does not require assumptions about stationarity, length of signal, and noise. The decomposition of the original EEG into its five constituent subbands helps better identification of the dynamical system of EEG signal. This leads to better classification of the database into three groups: Healthy subjects, epileptic subjects during a seizure-free interval (Interictal) and epileptic subjects during a seizure course (Ictal). The proposed algorithm is applied to an epileptic EEG dataset provided...