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

Liang Huang, Xuan Ni, William L. Ditto, Mark Spano, Paul R. Carney, Ying-Cheng Lai

Research output: Contribution to journalArticle

5 Scopus citations


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.

Original languageEnglish (US)
Article number160741
JournalRoyal Society Open Science
Issue number1
StatePublished - Jan 18 2017



  • Big data analysis
  • Electroencephalogram
  • Empirical mode decomposition
  • Epileptic seizures
  • High-frequency oscillations
  • Nonlinear dynamics

ASJC Scopus subject areas

  • General

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