American Journal of Biomedical Engineering 2016, 6(1): 32-41 DOI: 10.5923/j.ajbe.20160601.05 Statistical Analysis of EEG Signals in Wavelet Domain for Efficient Seizure Prediction Mostafa I. El Sayeid 1,* , Entessar Gemeay 1 , Salah Khames 1 , Turkey Alotaiby 2 , Saleh A. Alshebeili 3 , Fathi E. Abd El-Samie 4 1 Faculty of Engineering, Tanta University, Tanta, Egypt 2 KACST, Kingdom of Saudi Arabia 3 Electrical Engineering Department, KACST-TIC in Radio Frequency and Photonics for the e-Society (RFTONICS), King Saud University, Kingdom of Saudi Arabia 4 Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt Abstract Epilepsy is an electrophysiological disorder of the brain, characterized by recurrent seizures. Electroencephalogram (EEG) is a test that measures and records the electrical activity of the brain, and is widely used in the prediction and analysis of epileptic seizures. This paper provides a statistical analysis associated with EEG signals assuming that these signals can be categorized into inter-ictal, pre-ictal, and ictal states. We study histograms and cumulative histograms for segments of various signal states, in wavelet domain by utilizing different signal processing tools such as the differentiator and median filtering, as well as the local mean, and local variance estimators. The results show that signal states could be distinguished according to statistics in the wavelet domain. Keywords EEG, Wavelet domain, Seizure prediction, Statistical analysis 1. Introduction Epilepsy is actually the most common neurological ailment impacting on 50 million people world-wide, 85% of which belong to the developing nations. About 2.4 million new cases happen annually around the world. At least, 50% of the epileptic cases start at childhood or adolescence [1]. There were many trials for human brain discovery utilizing EEG signals. These kinds of signals are obtained through a brain computer interface (BCI) constructed with electrodes. If theses electrodes are placed touching the brain surface, intracranial Electrocorticography (iEEG) signals are acquired. In contrast, for outside electrodes [2], we obtain Stereo Electrocorticography (sEEG) signals. iEEG signals are obtained using an invasive techniques, while sEEG signals are obtained via non-invasive techniques [2]. EEG signals are, in general, of multi-band nature. Using the signal power in each sub-band or signal features, an EEG signal classification procedure can be carried out for various applications such as seizure detection and prediction. Seizure detection can be a popular medical application for EEG signal processing. The goal of this technique is to execute an off-line information acquisition through recorded EEG * Corresponding author: eng.mustafa@ymail.com (Mostafa I. El sayeid) Published online at http://journal.sapub.org/ajbe Copyright © 2016 Scientific & Academic Publishing. All Rights Reserved signals for patients [3]. A vast majority of signal processing tools, especially transform domain tools, have been investigated for seizure detection. One of these transforms is the wavelet transform [4, 5], which will be used in this paper .Seizure prediction is a more challenging task to be performed online. For correct prediction, EEG signals need to be divided into three different classes. These classes are inter-ictal (normal state), pre-ictal (pre-state), and ictal (seizure state). The objective of this paper is to study the statistical characteristics of different EEG signal batches in the wavelet domain in order to classify the signal batches into each of these states. Section II of the paper discusses the seizure detection algorithms. Section III discusses the seizure prediction process with its limitations. Section IV presents the statistical tools that are utilized for EEG signal batch analysis. Section V presents the experimental results. Finally, the concluding remarks are given in section VI. 2. Wavelet-Domaim Seisure Detection Seizure detection is a sort of anomaly detection in EEG signals to identify the occurrence of a seizure as shown in Fig. (1). Feature extraction and signal modeling techniques have been used for wavelet-domain seizure detection, leading to detection results below the required levels. To enhance detection results, Rana et al. [9] adopted a frequency-domain technique for seizure detection using