The Prognosis of Epilepsy with Naive Bayes Classifier On FPGA Using HDL Coder

Epilepsy is one of the serious neurological disorder that affects about 50 million individuals globally at all ages and that can be detected from electroencephalogram (EEG) signals. This paper discusses the implementation of the Naive Bayes' classifier for the classification of epileptic events into seizure or non-seizure class. The design is carried out in Simulink environment where as it's implementation is performed on qkintex-7 FPGA hardware using HDL coder. The fixed point tool of the MATLAB is used for the conversion of floating point data into fixed point to make the hardware implementation possible. This classification is performed in real time with approximately 95 % to 100% classification accuracy.