Time-Frequency Based Methods for Non-Stationary Signal Analysis with Application To EEG Signals

Abstract

The analysis of electroencephalogram or EEG plays an important role in diagnosis and detection of brain related disorders like seizures. In this dissertation, we propose three new seizure detection algorithms that can classify seizure from nonseizure data with high accuracy. The first algorithm is based on time-domain features which are the approximate entropy (ApEn), the maximum singular value (MSV) and the median absolute deviation (MAD). These features were fed into the AdaBoost and the Support Vector Machine (SVM) algorithms, which were used to classify the signal as either seizure or non-seizure. The accuracy of these classifications was summarized and compared to different algorithms in the literature. In the second algorithm, the Rényi entropy was extracted from different spectral components after the EEG signal was decomposed using either Empirical Mode Decomposition (EMD) or the Discrete Dyadic Wavelet Transform (DWT). The knearest neighbor (k-NN) classifier was use to classify the seizure segments based on the extracted features. In the third algorithm, we decompose the EEG signal into subcomponents occupying different spectral sub-bands using the EMD. A decomposition energy measure was used to discard those sub-components estimated to contain mostly noise. Different time-frequency representations (TFRs) were computed of the remaining sub-components. Local energy measures were estimated and fed into a linear classifier to determine whether or not the EEG signal contained a seizure. The three algorithms were tested on noisy EEG signals from roaming rats as well as the relatively noise free human seizure from a well-known public dataset provided on-line I would like to thank my parents, my brother and my sisters; they are always supporting me and encouraging me with their love and best wishes. Finally, I would like to thank my husband, Yacine Boudria, for his love, support, understanding and patience throughout the completion of this research. v