Unsupervised Learning of Spike Patterns for Seizure Detection and Wavefront Estimation of High Resolution Micro Electrocorticographic ({\mu}ECoG) Data

TL;DR

This paper introduces an unsupervised learning framework for analyzing high-resolution micro ECoG data to identify spike patterns, aiding in seizure prediction and wavefront estimation, with promising in-vivo feline results.

Contribution

It presents a novel unsupervised approach combining video processing and clustering to analyze micro ECoG spike patterns for seizure and wavefront prediction.

Findings

Successful identification of spike motion patterns

Effective application to feline seizure data

Potential for improved seizure prediction

Abstract

For the past few years, we have developed flexible, active, multiplexed recording devices for high resolution recording over large, clinically relevant areas in the brain. While this technology has enabled a much higher-resolution view of the electrical activity of the brain, the analytical methods to process, categorize and respond to the huge volumes of seizure data produced by these devices have not yet been developed. In this work we proposed an unsupervised learning framework for spike analysis, which by itself reveals spike pattern. By applying advanced video processing techniques for separating a multi-channel recording into individual spike segments, unfolding the spike segments manifold and identifying natural clusters for spike patterns, we are able to find the common spike motion patterns. And we further explored using these patterns for more interesting and practical problems as seizure prediction and spike wavefront prediction. These methods have been applied to in-vivo feline seizure recordings and yielded promising results.