Epilepsy identification based on EEG signal using RQA method

Abstract

PurposeEpilepsy is one of the most common neurological diseases and its cause is not unequivocal. Thus, additional methods and searches that may help to diagnose the disease are used in the clinical practice. In this study, we tested the possibility of using the Recurrence Quantification Analysis (RQA) method to identify epilepsy and present the analysis of EEG signals of healthy patients and epileptic patients by the RQA method. Materials/methodsThe recordings of signals belong to 13 patients, which were divided into 2 groups: Group A (5 epileptic patients) and Group B (8 healthy patients). In this study Fp1, Fp2, T3 and T4 electrodes were considered in the analysis using the RQA method. ResultsIt is difficult to explore the dynamics of signals by linear methods. In this study, another way of analyzing the dynamics of signals by the RQA method is presented. The RQA method revealed differences in the dynamics between the epileptic and normal signals, which seemed important in an organoleptic way. It was found that the dynamics of epileptic signals is more periodic than normal signals. To confirm the correctness of the statements issued for the RQA data the Principal Component Analysis mapping was applied. This method showed more clearly the differences in the dynamics of both signals. ConclusionsThe RQA method can be used to identify nonlinear biomedical signals such as EEG signals.