Wavelet-analysis of multimode dynamics in living systems

摘要

Biological systems typically exhibit multimode oscillations and generate signals demonstrating a coexistence of rhythmic components. The coexistence of independent modes often leads to various forms of their interaction and entrainment. Difficulties of experimental studies of interaction phenomena are often caused by the nonstationarity of available biological data and by the resolution abilities of a numerical technique being used. Thus, time-varying methods have some limitations in resolving the modes whose frequencies are quite close. In our work we investigate opportunities of the wavelet-analysis in the study of coexisting rhythmic processes. Using chirp-signals we demonstrate how the possibility to estimate the instantaneous frequency depends on the rate of its change. Further, we show how the wavelet-based approach provide information about interaction phenomena in kidney autoregulation. We report clear distinctions in the autoregulation mechanisms for normotensive and hypertensive rats.