• Madhavi Devi Kodali

The EEG signal is pre-processed to remove major artifacts before being decomposed into several EEG sub-bands using a discrete-wavelet-transform (DWT). The nonlinear method Shannon entropy, which measure complexity and chronicity in the EEG recording, is used to extract the features. The extracted features are then classified using several classification methods. Different EEG datasets are used to verify the proposed design technique: The University of Bonn dataset, the MIT dataset, the King Abdulaziz University dataset, and many more. The proposed method could potentially be used to assist epilepsy and ASD diagnosis. The combination of DWT, Shannon entropy and k-nearest neighbor (KNN) techniques produces the most promising classification result, with an overall accuracy of up to 94.6% for the three-class (multi-channel) classification problem.

EEG, epilepsy, chronicity, DWT, nearest neighbor

"EEG SIGNAL PROCESSING FOR ABNORMALITY DETECTION USING DWT, SE AND KNN TECHNIQUES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 9, page no.88-99, September-2018, Available :http://www.jetir.org/papers/JETIR1809663.pdf

"EEG SIGNAL PROCESSING FOR ABNORMALITY DETECTION USING DWT, SE AND KNN TECHNIQUES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 9, page no. pp88-99, September-2018, Available at : http://www.jetir.org/papers/JETIR1809663.pdf