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FPQA Implementation of Huang Hilbert Transform for Classification of Epileptic Seizures Using Artificial Neural Network

Authors: G.Deepika; K.S.Rao;

FPQA Implementation of Huang Hilbert Transform for Classification of Epileptic Seizures Using Artificial Neural Network

Abstract

The most common brain disorders due to abnormal burst of electrical discharges are termed as Epileptic seizures. This work proposes an efficient approach to extract the features of epileptic seizures by decomposing EEG into band limited signals termed as IMF’s by empirical decomposition EMD. Huang Hilbert Transform is applied on these IMF’s for calculating Instantaneous frequencies and are classified using artificial neural network trained by Back propagation algorithm. The results indicate an accuracy of 97.87%. The algorithm is implemented using Verilog HDL on Zynq 7000 family FPGA evaluation board using Xilinx vivado 2015.2 version.

Keywords

EEG, IMF,EMD

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
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