Wavelet analysis in biomedical research: A versatile tool for multiscale signal characterization and functional connectivity analysis.

Biological systems exhibit oscillatory dynamics across spatial and temporal scales. From intracellular signaling to organ-level physiology, these rhythms support coordinated regulation and collective behavior. The resulting signals are nonstationary and reflect the superposition of multiple physiological processes, posing challenges for conventional analytical approaches. By providing time-frequency localization, wavelet analysis offers a powerful framework for characterizing transient oscillations and their scale-dependent organization. Recently, its integration with functional connectivity enabled quantification of time- and frequency-resolved interactions among biological units and the construction of frequency-specific functional networks. In turn, multilayer representations provide a foundation for examining network organization across frequency bands and their interrelations. Motivated by these developments, this review provides an integrated overview of the principles of wavelet analysis and its applications in biomedical research. Using examples from cardiovascular physiology, neuroscience, and pancreatic islet calcium dynamics, we illustrate how wavelet-based methods extend from the time-frequency characterization of individual signals to the analysis of functional interactions and collective network dynamics. Overall, wavelet analysis emerges as a versatile framework for linking nonstationary multiscale dynamics with physiological coordination and network organization. Advances in experimental resolution, computational methodology, and data-driven analysis are likely to extend its value, particularly when wavelet-derived features are integrated with functional connectivity, multilayer network models, and machine learning approaches. Such combinations may transform complex physiological recordings into interpretable representations of coordinated function across scales. Wavelet-based analysis thus has the potential to become a key methodological tool for studying complex biological systems, from intracellular processes to organs, in health and disease.