2024-03-29T13:24:42Zhttps://keep.lib.asu.edu/oai/requestoai:keep.lib.asu.edu:node-1283902021-10-27T18:41:15Zoai_pmh:all128390
https://hdl.handle.net/2286/R.I.44628
<p>Huang, L., Ni, X., Ditto, W. L., Spano, M., Carney, P. R., & Lai, Y. (2017). Detecting and characterizing high-frequency oscillations in epilepsy: a case study of big data analysis. Royal Society Open Science, 4(1), 160741. doi:10.1098/rsos.160741</p>
10.1098/rsos.160741
2054-5703
http://rightsstatements.org/vocab/InC/1.0/
http://creativecommons.org/licenses/by/4.0
2017-01-18
18 pages
eng
Huang, Liang
Ni, Xuan
Ditto, William L.
Spano, Mark
Carney, Paul R.
Lai, Ying-Cheng
Ira A. Fulton Schools of Engineering
The final version of this article, as published in Royal Society Open Science, can be viewed online at: http://rsos.royalsocietypublishing.org/content/4/1/160741
<p>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.</p>
Text
Detecting and Characterizing High-Frequency Oscillations in Epilepsy: A Case Study of Big Data Analysis