FPGA Implementation of Epileptic Seizure Detection Using ELM Classifier

Abstract

Electroencephalography (EEG) Signals are widely used to determine the brain disorders. The Electrical activity of human brain is recorded in the form of EEG signal. The abnormal Electrical activity of the human brain is called as epileptic seizure. In epilepsy patients, the seizure occurs at unpredictable times and it causes sudden death. Detection and Prediction of Epileptic seizure is performed by analyzing the EEG signal. The EEG signal of human brain is random in nature, hence detection of seizure in EEG signal is challenging task. Hardware implementation of Epileptic seizure detection system is necessary for real time applications. In this work an accurate approach is used to identify the Epileptic seizure and that is implemented in FPGA (Field Programmable Gate Array). The hardware implementation of epileptic seizure detection algorithm is done using Xilinx System generator tool. In the first step the EEG signal is extracted from the human brain and it is filtered by using Finite Impulse response (FIR) band pass filter. The band pass filter separates the EEG signal into delta, theta, alpha, beta and gamma brain rhythms. The band separated brain signal is modeled by linear prediction theory. In the next step features are extracted from the modeled EEG signal and the classification of normal or seizure signal is done by using Extreme Learning Machine (ELM) classifier. The EEG signals used in this paper were obtained from Epilepsy Center at the University of Bonn, Germany. The hardware architecture, Look up tables, resource utilization, Accuracy and power consumption of the algorithm is analyzed using xilinx zynq- 7000 all programmable soc.