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DTCWT Based EEG Classification Using ANN and Statistical Feature Analysis for Brain Diseases Diagnosis


Madhulika Pandey , SSTC, Bhilai, Chhattisgarh; Ravi Mishra, SSTC, BHILAI, CHHATTISGARH


Dual Tree Complex Wavelet Transform(DTCWT), Artificial Neural Network(ANN), Back Propagation Network (BPN)


This paper is presented to propose an automatic support system for EEG signal classification to brain diseases diagnosis or epilepsy seizure detection. The signal classification is a challenging problem due to its complexity. The Artificial Neural Network is used for classification of EEG signal. If we go through the manual analysis of EEG signals it may become time consuming or inaccurate and also it will require a trained person for the diagnosis. Decision making is done in two steps that are feature extraction in DTCWT domain and classification using artificial neural network. The artificial neural network has implemented the multilayer perceptron neural network and back propagation network. By using BPN we get the fast and accurate classification. The performance of BPN has evaluated in terms of training performance and classification accuracy.

Other Details

Paper ID: IJSRDV4I80327
Published in: Volume : 4, Issue : 8
Publication Date: 01/11/2016
Page(s): 465-467

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