{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T11:47:26Z","timestamp":1782992846154,"version":"3.54.5"},"reference-count":54,"publisher":"World Scientific Pub Co Pte Lt","issue":"03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Neur. Syst."],"published-print":{"date-parts":[[2011,6]]},"abstract":"<jats:p> Epilepsy is a common neurological disorder that is characterized by the recurrence of seizures. Electroencephalogram (EEG) signals are widely used to diagnose seizures. Because of the non-linear and dynamic nature of the EEG signals, it is difficult to effectively decipher the subtle changes in these signals by visual inspection and by using linear techniques. Therefore, non-linear methods are being researched to analyze the EEG signals. In this work, we use the recorded EEG signals in Recurrence Plots (RP), and extract Recurrence Quantification Analysis (RQA) parameters from the RP in order to classify the EEG signals into normal, ictal, and interictal classes. Recurrence Plot (RP) is a graph that shows all the times at which a state of the dynamical system recurs. Studies have reported significantly different RQA parameters for the three classes. However, more studies are needed to develop classifiers that use these promising features and present good classification accuracy in differentiating the three types of EEG segments. Therefore, in this work, we have used ten RQA parameters to quantify the important features in the EEG signals.These features were fed to seven different classifiers: Support vector machine (SVM), Gaussian Mixture Model (GMM), Fuzzy Sugeno Classifier, K-Nearest Neighbor (KNN), Naive Bayes Classifier (NBC), Decision Tree (DT), and Radial Basis Probabilistic Neural Network (RBPNN). Our results show that the SVM classifier was able to identify the EEG class with an average efficiency of 95.6%, sensitivity and specificity of 98.9% and 97.8%, respectively. <\/jats:p>","DOI":"10.1142\/s0129065711002808","type":"journal-article","created":{"date-parts":[[2011,6,7]],"date-time":"2011-06-07T07:28:30Z","timestamp":1307431710000},"page":"199-211","source":"Crossref","is-referenced-by-count":281,"title":["APPLICATION OF RECURRENCE QUANTIFICATION ANALYSIS FOR THE AUTOMATED IDENTIFICATION OF EPILEPTIC EEG SIGNALS"],"prefix":"10.1142","volume":"21","author":[{"given":"U. RAJENDRA","family":"ACHARYA","sequence":"first","affiliation":[{"name":"Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S. VINITHA","family":"SREE","sequence":"additional","affiliation":[{"name":"School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"SUBHAGATA","family":"CHATTOPADHYAY","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, National Institute of Science and Technology, Berhampur 761008, Orissa, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"WENWEI","family":"YU","sequence":"additional","affiliation":[{"name":"Chiba University, Chiba, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"PENG CHUAN ALVIN","family":"ANG","sequence":"additional","affiliation":[{"name":"Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"219","published-online":{"date-parts":[[2011,11,21]]},"reference":[{"key":"rf1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2005.06.011"},{"key":"rf2","doi-asserted-by":"publisher","DOI":"10.1142\/S0219519409003152"},{"key":"rf3","doi-asserted-by":"publisher","DOI":"10.1016\/S0165-0270(02)00340-0"},{"key":"rf4","doi-asserted-by":"publisher","DOI":"10.3233\/JAD-2005-7301"},{"key":"rf5","doi-asserted-by":"publisher","DOI":"10.1177\/155005940503600303"},{"key":"rf6","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2006.886855"},{"key":"rf7","doi-asserted-by":"publisher","DOI":"10.1016\/j.neulet.2008.08.008"},{"key":"rf8","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2009.05.003"},{"key":"rf9","doi-asserted-by":"publisher","DOI":"10.1201\/9781439815328"},{"key":"rf10","doi-asserted-by":"publisher","DOI":"10.1177\/155005941004100103"},{"key":"rf11","doi-asserted-by":"crossref","first-page":"197","DOI":"10.3233\/ICA-2010-0345","volume":"17","author":"Ahmadlou M.","journal-title":"Integrated Computer-Aided Engineering"},{"key":"rf12","doi-asserted-by":"publisher","DOI":"10.1007\/s00702-010-0450-3"},{"key":"rf13","doi-asserted-by":"publisher","DOI":"10.1097\/WNP.0b013e3181f40dc8"},{"key":"rf14","doi-asserted-by":"publisher","DOI":"10.1097\/WAD.0b013e3181ed1160"},{"key":"rf15","doi-asserted-by":"publisher","DOI":"10.1177\/155005941104200105"},{"key":"rf16","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065709001896"},{"key":"rf17","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.64.061907"},{"key":"rf18","first-page":"200","author":"Bai D.","journal-title":"Journal of Biomedical Engineering"},{"key":"rf19","doi-asserted-by":"publisher","DOI":"10.1080\/03091900701559408"},{"key":"rf20","first-page":"485","volume":"223","author":"Chua K. 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