Model and data-driven representations of the sleep cycle using locally linear embedding

Beth A Lopour,Heidi E Kirsch,James W Sleigh,Andrew J Szeri

doi:10.1186/1471-2202-10-s1-p294

Abstract

Model and data-driven representations of the sleep cycle using locally linear embedding

Highlights

There is a complex relationship between sleep and the onset of epileptic seizures
It accomplishes this by characterizing the local structure around each data point and computing a nonlinear re-mapping of the data that optimally preserves that local structure [1]
We apply the locally linear embedding (LLE) algorithm to both human EEG data recorded during sleep and simulated EEG data from a continuous mathematical model of the sleep cycle [2]

Summary

Introduction

There is a complex relationship between sleep and the onset of epileptic seizures. Current methods of sleep scoring divide data into discrete stages, but a continuous model may provide more insight into this phenomenon. This would allow a more detailed analysis of the sleep leading up to the seizure, but it would give us greater predictive power regarding impending transitions. Continuous models of the human sleep cycle already exist; it is very difficult to connect these models to actual EEG sleep data. We present a possible solution to this problem using a technique called locally linear embedding (LLE)

Method

Results

Summary