Highly scalable real time epilepsy diagnosis architecture via phase correlation

Abstract

Epilepsy is at current the world’s second most common neurological disorder affecting an estimated 50 million people. While up to 70% of epileptic suffers are treated successfully with epileptic medication some 30% continue to suffer untreated [1]. This gap could be filled by the implementation of implantable neural prostheses which are able to detect when a seizure is coming and eventually actuate in the brain to stop its progression. The change in brain activity during epileptic fits has been leading scientists to investigate neural features such as neural spiking [2], correlation [3] and the most tantalizing, phase synchronization, in order to predict seizures before they happen. As described in [4], a large decrease in synchronization between two neural signals can be seen for an unknown period during the pre-ictal stage. This decrease in synchronization is believed to be a significant bio-marker which could hold the key to prediction and prevention of epileptic seizures via neural prosthesis. The discrete distance approximation (DDA) algorithm proposed in this work can drastically reduce the number of complex operations (multiplications and divisions), relying only on basic addition, comparison and shifting. In terms of logic, the DDA can reduce the amount of hardware needed to detect pre-ictal events by as much as 96.8% when compared to systems with similar functionality. Due to its highly efficient area and power consumption, the proposed approach could lead to a truly functional medical in-vivo application for real time monitoring and or prevention.This work has been funded by Mineco under grant TEC2012-33634, Junta de Andalucía under project TIC 2338, the Office of Naval Research (ONR -USA) under Project N00014-14-1-0355 and the FEDER Program.Peer reviewe