Detecting and characterizing high frequency oscillations in epilepsy - A case study of big data analysis

TL;DR

This paper presents a comprehensive framework for analyzing massive nonlinear time series data, demonstrated through detecting high frequency oscillations in rat EEG recordings, revealing on-off intermittency with algebraic scaling laws.

Contribution

The authors introduce a novel multi-step framework combining preprocessing, empirical mode decomposition, and statistical analysis for big data time series, applied to epilepsy EEG data.

Findings

HFOs exhibit on-off intermittency.

Intermittency can be quantified by algebraic scaling laws.

Framework applicable to other large-scale data analysis problems.

Abstract

We develop a framework to uncover and analyze dynamical anomalies from massive, nonlinear and non-stationary time series data. The framework consists of three steps: preprocessing of massive data sets to eliminate erroneous data segments, application of the empirical mode decomposition and Hilbert transform paradigm to obtain the fundamental components embedded in the time series at distinct time scales, and statistical/scaling analysis of the components. As a case study, we apply our framework to detecting and characterizing high frequency oscillations (HFOs) from a big database of rat EEG recordings. We find a striking phenomenon: HFOs exhibit on-off intermittency that can be quantified by algebraic scaling laws. Our framework can be generalized to big data-related problems in other fields such as large-scale sensor data and seismic data analysis.