Change Detection in Complex Dynamical Systems Using Intrinsic Phase and Amplitude Synchronization

Ashif Sikandar Iquebal, Satish Bukkapatnam, Arun Srinivasa

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


We present an approach for the detection of sharp change points (short-lived and persistent) in nonlinear and nonstationary dynamic systems under high levels of noise by tracking the local phase and amplitude synchronization among the components of a univariate time series signal. The signal components are derived via Intrinsic Time scale Decomposition (ITD)-a nonlinear, non-parametric analysis method. We show that the signatures of sharp change points are retained across multiple ITD components with a significantly higher probability as compared to random signal fluctuations. Theoretical results are presented to show that combining the change point information retained across a specific set of ITD components offers the possibility of detecting sharp transitions with high specificity and sensitivity. Subsequently, we introduce a concept of mutual agreement to identify the set of ITD components that are most likely to capture the information about dynamical changes of interest and define an InSync statistic to capture this local information. Extensive numerical, as well as real-world case studies involving benchmark neurophysiological processes and industrial machine sensor data, suggest that the present method can detect sharp change points, on an average 62% earlier (in terms of average run length) as compared to other contemporary methods tested.

Original languageEnglish (US)
Article number9162536
Pages (from-to)4743-4756
Number of pages14
JournalIEEE Transactions on Signal Processing
StatePublished - 2020
Externally publishedYes


  • Change detection
  • nonlinear and nonstationary systems
  • phase synchronization
  • signal decomposition
  • time series

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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