Hardware implementation of deep neural network for seizure prediction

Abstract

Epilepsy is a neurological disorder characterized by seizures, which are caused by a sudden, uncontrollable electrical disturbance in the brain. Recently, machine learning and deep learning techniques have been used in seizure prediction algorithms, which greatly aids epilepsy patients. Moreover, the usage of hardware that is available for both medical practitioners and epilepsy patients would help enhance the quality of life for epilepsy patients. For this purpose, a Field-programmable Gate Array (FPGA) is used in this work to implement a hardware model of a neural network that is able to predict seizures. In this research, the main aim is to implement an FPGA-based general and patient-specific seizure prediction algorithm that detects seizures for epilepsy patients using Multilayer Perceptron (MLP) neural network models. Moreover, the work will also tackle ways to optimize the FPGA resources and the computational time of the seizure prediction models. The data available for training and testing are raw electroencephalogram (EEG) signal samples provided by the Melbourne-NeuroVista seizure trial and the Melbourne-University AES-MathWorks-NIH Seizure Prediction Challenge. The results show that the general model had an Area under curve (AUC) score of 0.72 while the patient-specific model had an average AUC score of 0.8.