Epilepsy Identification System with Neural Network Hardware Implementation

This paper presents a real-time identification system for epilepsy detection with a neural network (NN) classifier. The identification flow of the proposed system in animal testing is described as follows: 1. Two channel signals are collected from mouse brain. 2. Original signals are filtered in the appropriate bandwidth. 3. Six feature values are calculated. 4. Normal and epilepsy are classified by the classifier. The electroencephalography signal is measured from C57BL/6 mice in animal testing with a sampling rate of 400 Hz. The proposed system is verified on software design and hardware implementation. The software is designed in Matlab, and the hardware is implemented by the field programmable gate array (FPGA) platform. The chip is fabricated with TSMC 0.18 μm CMOS technology. The feature extraction function is realized in FPGA, and the NN architecture is implemented with a chip. The chosen feature sets from the previous measured animal testing data are amplitude, frequency bins, approximate entropy, and standard deviation. The accuracies of the proposed system are approximately 98.76% and 89.88% on software verification and hardware implementation, respectively. Results reveal that the proposed architecture is effective for epilepsy recognition.