Following the development of a new class of self-sustained oscillators with a time-varying but stable frequency, the inverse approach to these systems is now formulated. We show how observed data arranged in a single-variable time series can be used to recognise such systems. This approach makes use of time-frequency domain information using the wavelet transform as well as the recently-developed method of Bayesian-based inference. In addition, a new set of methods, named phase fluctuation analysis, is introduced to detect the defining properties of the new class of systems by directly analysing the statistics of the observed perturbations. We apply these methods to numerical examples but also elaborate further on the cardiac system.
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