EEG Signal Analysis for Automated Epilepsy Seizure Detection Using Wavelet Transform and Artificial Neural Network

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Abstract
Electroencephalogram (EEG) measures electrical activity of the brain and proffers valuable insight of the brain dynamics. Accurate and careful analysis of EEG signal plays a prominent role in the diagnosis of brain diseases like epilepsy, brain tumor. EEG is the most significant method used for epilepsy monitoring, diagnosis and rehabilitation. A patient-specific seizure detection model has been developed using Haar wavelet and Artificial Neural Network. HAAR Wavelet decomposition of multi-channel EEG with five scales is made and three frequency bands of EEG selected for the consequent process. The conventional Haar wavelet transform (HWT) is replaced by a modified Haar wavelet transform whereas the number of multiplications and additions are reduced. The Haar wavelet reduces computational complexity from the existing Haar wavelet structure which consumes only 1–3 ms based on the decomposition level to detect epilepsy.