title: 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.
authors.affiliation: School of Physical Science and Technology , Lanzhou University , Lanzhou , Gansu 730000 , People's Republic of China.
authors.forename: Liang
authors.initials: L
authors.lastname: Huang
authors.affiliation: School of Electrical , Computer and Energy Engineering , Arizona State University , Tempe , AZ 85287 , USA.
authors.forename: Xuan
authors.initials: X
authors.lastname: Ni
authors.affiliation: College of Sciences , North Carolina State University , Raleigh , NC 27695 , USA.
authors.forename: William L
authors.initials: WL
authors.lastname: Ditto
authors.affiliation: School of Biological and Health Systems Engineering , Arizona State University , Tempe , AZ 85287 , USA.
authors.forename: Mark
authors.initials: M
authors.lastname: Spano
authors.affiliation: Pediatric Neurology and Epilepsy , Department of Neurology , University of North Carolina , 170 Manning Drive , Chapel Hill , NC 27599-7025 , USA.
authors.forename: Paul R
authors.initials: PR
authors.lastname: Carney
authors.affiliation: School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ 85287, USA; Department of Physics, Arizona State University, Tempe, AZ 85287, USA.
authors.forename: Ying-Cheng
authors.identifier: 0000-0002-0723-733X
authors.initials: YC
authors.lastname: Lai
keywords: big data analysis; electroencephalogram; empirical modedecomposition; epileptic seizures; high-frequency oscillations; nonlinear dynamics