Epileptic seizure detection using EEG signals by means of stationary wavelet transforms
نویسندگان
چکیده
Wavelet transform provides a fine means of classifying seizure EEG signals from the normal EEG signals. Stationary wavelet transform (SWT) is used to further improve the performance of discrete wavelet transform. EEG signal prediction and classification can be bolstered up by applying SWT. In this work the residues obtained from denoising the signal using SWT is considered. Its arithmetical factors like standard deviation mean and median absolute deviation, maximum norm and histogram are considered and analyzed. It can be vividly seen that the seizure EEG signal parameters are higher than the normal EEG wave pattern. The original wavelet used here is a novel wavelet named as eegwav which has a resemblance with the EEG wave pattern.
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