Classification of Epileptic EEG Signals by Wavelet based CFC Abstract— Electroencephalogram, an influential equipment for analyzing human’s activities and recognition of seizure attacks can play a crucial role in designing accurate systems which can distinguish ictal seizures from regular brain alertness, since it is the first step towards accomplishing a high accuracy computer aided diagnosis system (CAD). In this article a novel approach for classification of ictal signals with wavelet based cross frequency coupling (CFC) is suggested. After extracting features by wavelet based CFC, optimal features have been selected by t-test and quadratic discriminant analysis (QDA) have completed the Classification. Keywords—Electroencephalogram; Wavelet Decomposition; Cross Frequency Coupling;Quadratic Discriminant Analysis; T-test Feature Selection I. INTRODUCTION The common belief that seizure is a sign of epileptic brain disorder is not very accurate. Occurrence of seizure may take place regardless of circumstances or host’s attributes [1]. Reports of WHO claims that the second plausible neurological disorder beneath stroke is epilepsy. Monitoring and diagnosing this considerable amount of afflicted patients is achievable by Electroencephalogram signals (EEG) [2,3]. Since this diagnosis requires physician’s direct examination and results are not one and the same. Anticipation of epileptic seizure demands a method for automated computer aided diagnosis [4,5]. Time frequency domain [6,7], frequency domain [8-10] and time domain analysis [11] have been the basis of several feature extraction algorithms to detect seizure. After all, EEG signals are believed to be non-stationary. Between methods based on time frequency for feature extraction wavelet transforms are superior options due to their localization and reflection of time varying qualities of the data. Unprocessed EEG signals in conjunction with certain proper rules could be decompounded to precise subdivisions, consequently, noticeable amount of features are considered suchlike phase synchronization [12] effective correlation dimension [13], short term maximum Lyapunov exponents [14] accumulated energy [15] and dynamical similarity algorithm [16] to declare the existence of an epileptic seizure. A real time low power algorithm for classifying signals to detect seizures in ambulatory EEG was suggested by Patel et al [17]. Quadratic and linear discriminant analysis, support vector machine (SVM) and Mahalanobis discriminant analysis (MDA) classifiers have been examined in the aforementioned study on thirteen subjects. In this article a new approach for ictal signals’ classification based on wavelet is suggested after the extraction of optimal features from wavelet coefficients the signal has been classified by QDA and results claim that all cases have been designated correctly. II. MATERIALS AND METHODS This study denotes a Wavelet based CFC method which firstly segregates the EEG signal into wavelet coefficients subsequently phase and amplitude of wavelet coefficients were computed with Hilbert transform. After ranking the optimal features by t-test, the classification procedure has been performed by QDA (Fig.1). Above mentioned cases are: Case I: Avs.E (Healthy versus Seizure) Case II: B vs. E (Healthy versus Ictal) Case III: C vs. E (Hippocampal Interictal versus Seizure) Case IV: D vs. E (Epileptogenic Interictal versus Seizure) Case V: ABCD vs. E (Seizure-free versus Seizure) A. EEG Database of Epilepsy The EEG database is accumulated from Germany epilepsy center, Bonn university hospital of Freiburg [18]. It consists of five subsets each containing 100 single channel EEG captured in international 10-20 electrode placement montage. In spite of the fact that C to E were captured intracranially, A and B have been recorded as extra cranial signals. B. Stationary Wavelet Transform (SWT) The wavelet coefficients of SWT at all individual decomposition levels import the equal sample numbers same as the original signal. While DWT failed to face robustness and repeatability problems, SWT survived the obstacles [19]. C. Feature Extraction based on Cross Frequency Coupling Hilbert transform can indicate instantaneous phase and amplitude as follows: (M(n) is an analytic signal) () = () +  () =   () .  () (1) Amirmasoud Ahmadi, Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran Mahsa Behroozi, Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology Tehran, Iran Vahid Shalchyan, Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology Tehran, Iran Mohammad Reza Daliri Biomedical Engineering Department School of Electrical Engineering, Iran University of Science and Technology Tehran, Iran