Detection of Epileptic Seizures and Efficient DeNoising In Speech-Auditory Brain Waves
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2016, Vol 4, Issue 2
Abstract
EEG measures the brain activity. EEG signals are combination of the signals i.e, pure EEG and artifacts. The presence of these noises introduces spikes and results in signal distortion. The Electroencephalogram (EEG) signal is very important in the diagnosis of epilepsy. The detection of epileptic activity is, therefore, a very demanding process that requires a detailed analysis of the entire length of the EEG data. This paper describes an automated classification of EEG signals for the detection of epileptic seizures using wavelet transform and statistical pattern recognition. On comparing with other techniques, the Conventional Wavelet transform algorithms are best auditory artifact removal technique and can be highly useful in auditory EEG data analysis .The decision making process is comprised of three main stages: (a) feature extraction based on wavelet transform, (b) feature space dimension reduction using scatter matrices, and (c) classification by quadratic classifiers (disorder such as normal, ictal and interictal). An overall classification accuracy of 99% was achieved. The proposed algorithm has a potential in the classification of EEG signals and detection of epileptic seizures, and could thus further improve the diagnosis of epilepsy.
Authors and Affiliations
M. Manimekalai , C. Manjula
Keywords
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- EP ID EP21645
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