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 language | English (US) |
|---|---|
| Article number | 160741 |
| Journal | Royal Society Open Science |
| Volume | 4 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 18 2017 |
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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 journal › Article
}
TY - JOUR
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.
AB - 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.
KW - Big data analysis
KW - Electroencephalogram
KW - Empirical mode decomposition
KW - Epileptic seizures
KW - High-frequency oscillations
KW - Nonlinear dynamics
UR - http://www.scopus.com/inward/record.url?scp=85010378520&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85010378520&partnerID=8YFLogxK
U2 - 10.1098/rsos.160741
DO - 10.1098/rsos.160741
M3 - Article
AN - SCOPUS:85010378520
VL - 4
JO - Royal Society Open Science
JF - Royal Society Open Science
SN - 2054-5703
IS - 1
M1 - 160741
ER -