5 Citations (Scopus)

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.

Original languageEnglish (US)
Article number160741
JournalRoyal Society Open Science
Volume4
Issue number1
DOIs
StatePublished - Jan 18 2017

Fingerprint

oscillation
time series
seismic data
statistical analysis
transform
decomposition
sensor
timescale
anomaly
data analysis

Keywords

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

ASJC Scopus subject areas

  • General

Cite this

Detecting and characterizing high-frequency oscillations in epilepsy : A case study of big data analysis. / Huang, Liang; Ni, Xuan; Ditto, William L.; Spano, Mark; Carney, Paul R.; Lai, Ying-Cheng.

In: Royal Society Open Science, Vol. 4, No. 1, 160741, 18.01.2017.

Research output: Contribution to journalArticle

Huang, Liang ; Ni, Xuan ; Ditto, William L. ; Spano, Mark ; Carney, Paul R. ; Lai, Ying-Cheng. / Detecting and characterizing high-frequency oscillations in epilepsy : A case study of big data analysis. In: Royal Society Open Science. 2017 ; Vol. 4, No. 1.
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T1 - Detecting and characterizing high-frequency oscillations in epilepsy

T2 - A case study of big data analysis

AU - Huang, Liang

AU - Ni, Xuan

AU - Ditto, William L.

AU - Spano, Mark

AU - Carney, Paul R.

AU - Lai, Ying-Cheng

PY - 2017/1/18

Y1 - 2017/1/18

N2 - 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.

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KW - Big data analysis

KW - Electroencephalogram

KW - Empirical mode decomposition

KW - Epileptic seizures

KW - High-frequency oscillations

KW - Nonlinear dynamics

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