  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.
