Epileptic Seizure Prediction By Tom Eithun

What we know about epilepsy? Not much! Causes, outward symptoms, and underlying physiology are patient specific Even patients that present similarly may respond completely differently to the same treatment Luckily, the effects can be indirectly observed with EEG Imagine diagnosing a complex, usually reliable circuit that spontaneously generates random, dangerously high electrical activity at every node – and you can only observe the space around the circuit at a handful of locations in a room full of other noisy signals An EEG system gathers a discrete representation of the minute electrical field surrounding the brain

Epilepsy Patient Care – Dealing with Chronic Seizures Data Processing Research Focus Patient Needs High accuracy post-seizure Detection Computationally intensive big data prediction studies Search for correlations indicating physiological causality Continuous monitoring system for ever day life Real-time warning system to help plan for or avoid the scene below Time-Domain Prediction

Translating Patient Requirements to Technical Specifications Goals High true negative / low false positive predictive precision Low true positives and high false negative probabilities are okay! Lightweight processing to feature space Universal pattern classification training system

Prediction with Time-Domain Dependence Prediction Approach Extract waveform characteristics from small segments of “observation” window Label each sample with time before seizure-onset and electrode of origin Perform many pattern classification tests on each time group and electrode label Compile results from all electrodes and time domains into real-time, non- causal neural network

Prediction Case Studies Typical Results Ideal – Prediction w/ accurate time grouping Acceptable - Prediction