1. Epileptic Seizure Prediction
By Tom Eithun

2. 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

3. 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

4. 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

5. 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

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