Accurate Hardware Predictor for Epileptic Seizure

Abstract

Epilepsy triggers seizures, which develop before clinical onset in patients, and a timely and accurate prediction can save lives. A research challenge is to design accurate, fast, and energy-efficient hardware predictors. This work advances hardware-based seizure prediction research by proposing a new machine-learning-based predictor. It proposes a novel reconfigurable electroencephalogram (EEG) signal segmentation for increased learning. The proposed reconfigurable segmentation adaptively adjusts the overlap extent between consecutive segments and prepares new segments. Such prepared segments are fed into a Convolutional Auto-Encoder (CAE) using a proposed convolution module. The proposed convolution module uses optimized hyperparameters, including the number of layers, filters, filter size, pooling method, stride value, and padding for high learning and feature extraction. The learned CAE feeds into an Economic Long Short-Term Memory (ELSTM) to attain the final prediction result. The proposed predictor achieves high accuracy by exploiting the temporal dynamics of epileptic activity. It predicts seizures with an accuracy of 99.32%, a sensitivity of 99.29%, and a false alarm rate of 0.003 per hour, yielding high performance across classification thresholds, incurring low costs, and outperforming related hardware solutions. It is implemented in stand-alone VHDL, Altera Arria 10 GX FPGA, and synthesized into 45-nm technology.