This paper is part of the URM Validation Series (Paper 11) and examines independent signal-based findings from emergency medicine and physiological monitoring through a dynamical systems framework. Recent studies using spectral analysis of physiological waveforms (including heart rate, oxygen saturation, and related signals) have demonstrated that changes in signal structure, variability, and coupling precede clinical deterioration in acutely ill patients. These findings are typically interpreted as improvements in early detection or risk stratification. In this work, these observations are reinterpreted as manifestations of underlying system dynamics. Specifically, alterations in waveform structure and cross-correlation patterns are understood as reflecting changes in coupling between physiological subsystems, corresponding to a progressive loss of system stability prior to transition into a new disease state. Within the Universal Resonance Model (URM), disease is conceptualized as movement through dynamic states rather than transitions between fixed categories. From this perspective, signal-based approaches provide direct empirical access to pre-transition dynamics in biological systems. Importantly, the signal-based findings discussed here emerge independently of the URM framework. Their consistency with a dynamical systems interpretation represents converging evidence across domains, linking signal-level observations to system-level behaviour. This paper contributes to the URM Validation Series by demonstrating how physiological signal analysis may serve as a bridge between theoretical models of disease dynamics and real-time clinical data.
Anita Domargård (Thu,) studied this question.