Accuracy Enhancement of Epileptic Seizure Detection: A Deep Learning Approach with Hardware Realization of SIFT

Electroencephalogram (EEG) signals, generated during the neuron firing, are an effective way of predicting such seizure and it is used widely in recent days for classifying and predicting seizure activity. But EEG signals generated during an epileptic seizure are highly nonstationary and dynamic in nature and contain very crucial information about the state of the brain. Due to this randomness, the accuracy of analysis of EEG data by conventional and visual methods is reduced drastically. This paper aims at enhancing epilepsy seizure detection using deep learning models with an FPGA implementation of the short-time Fourier transform block. Detection of seizure has been achieved in the following stages: (1) time-frequency analysis of EEG segments using STFT; (2) extraction of frequency bands and features of interest; and (3) seizure detection using convolutional neural network (CNN) and bidirectional long short-term memory (Bi-LSTM). For this work, the Bonn EEG dataset has been used. The maximum error of similar to 0.13% was encountered while the comparison of STFT output generated via proposed hardware architecture vs the output generated via simulation was done. The average classification accuracy of 93.9% and 97.2% was achieved by CNN and Bi-LSTM models, respectively, considering all frequency bands for epileptic and non-epileptic patients.