Detecting and characterizing high-frequency oscillations in epilepsy: a case study of big data analysis
Abstract
We develop a framework to uncover and analyse dynamical anomalies from massive, nonlinear and non-stationary time series data. The framework consists of three steps: preprocessing of massive datasets 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 electroencephalogram 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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 01, 2017
- Source ID
- 10.1098/rsos.160741
Entities
People
- Liang Huang
- Mark Spano
- Paul R. Carney
- William L. Ditto
- Xuan Ni
- Ying-Cheng Lai
Organizations
- Arizona State University
- Army Research Office
- Lanzhou University
- National Natural Science Foundation of China
- North Carolina State University
- Office of Naval Research
- University of North Carolina