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.
Highlights
Big data analysis [1,2,3,4,5,6], a frontier field in science and engineering, has broad applications ranging from biomedicine and smart health [7,8] to social behaviour quantification and energy optimization in civil infrastructures
As a concrete example to illustrate the general principle of our big data analysis framework, we address the detection of high-frequency oscillations (HFOs), which are local oscillatory field potentials of frequencies greater than 100 Hz and usually have a duration less than 1 s [26,27,28,29,30,31,32,33,34,35,36,37]
empirical mode decomposition (EMD) decomposes it into a small number of modes, the intrinsic mode functions (IMFs), each having a distinct time or frequency scale and preserving the amplitude of the oscillations in the frequency range
Summary
Big data analysis [1,2,3,4,5,6], a frontier field in science and engineering, has broad applications ranging from biomedicine and smart health [7,8] to social behaviour quantification and energy optimization in civil infrastructures. In a modern infrastructure viewed as a complex dynamical system, large-scale sensor networks can be deployed to measure a number of physical signals to monitor the behaviours of the system in continuous time [17,18,19]. Big datasets are ubiquitous [21,22] In all these cases, monitoring, sensing or measurements typically result in big datasets, and it is of considerable interest to detect behaviours that deviate from the norm or the expected
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