Epilepsy seizure detection in eeg signals using wavelet transforms and support vector machines

In the medical science sector, the major focus for the researchers is the medical diagnosis of the abnormalities in brain .The most common brain disorder "epilepsy" that around 1% of total populace experiences this deformity. The Electroencephalogram "EEG" is an apparatus for measuring cerebrum activities which reflect the brain condition. EEG signal is collecting of brain electrical actions and has many information about brain states, also applied in several epilepsy detection methods. Epileptic seizures are distinguished by abnormal electrical activity happen in the brain. EEG records the seizures pretending changes in signal morphology. All these signal characteristics, however, differ between patients as well as between different seizures in the same patient. Epilepsy is succeed with anti-epileptic medications but in some ultimate cases surgery may be needed. Non-invasive surface electrode EEG measurement gives an estimate of the starting seizure but more invasive intracranial electrocardiogram (ECoG) are wanted at times for accurate localization of the epileptogenic zone. Methodology BCI Brain Computer Interfacing that expands a path for correspondence with the outside environment utilizing the brain thoughts. The achievement of this methodology relies on upon the select of techniques to handle the brain signal in every phase, BCI techniques framework is comprises of basic four phase. These are for the most part Signal Acquisition, Computer Interaction, Signal Classification and Signal Pre - Processing. The securing of brain signals is achieved by utilizing different non- invasive techniques like Electro Encephalograph (EEG). Magneto Encephalography (MEG), Near Infra-Red Spectroscopy NIRS and functional Magnetic Resonance Imaging fMRI. After signal acquisition phase, it was done pre-process the signals; can also be called as Signal Enhancement. For the most part, the gained brain signals are polluted by noise and artifacts. Heart beat ECG, eye blinks and eye movements EOG are artifacts. And also these, muscular movements and power line mediations are also blended with brain signals. After gaining the signals without noise in the signal increase stage, essential features in the brain signals were extracted. The signals are classified into several classes after feature extraction. Classifying electroencephalography, signals is an important step for proceeding EEG to identify the abnormal electrical activity in the brain. In this thesis, we present two BCI systems based on Maximal overlap discrete wavelet analysis which use to filter noise and feature extraction of signals and Support Vector Machine the most generality common Machine Learning techniques it use for classifying the Electroencephalography (EEG) signals and it based on the neuronal activity for the brain. The other system based on Maximal overlap discrete wavelet analysis, principle component analysis, and SVM which are able to identify epilepsy seizures from EEG signals. This work is part of research looking for the normal person and abnormal "epileptic" patients using (EEG), the importance of PCA as reduction of dimensionality of data explained.