Deterministic Detection of Information-Driven Regime Transitions in Wearable Physiological Signals: A Spiral-Time Operator Framework

Biological systems operate far from equilibrium and exhibit multiscale dynamics shaped by information flow, memory, and environmental coupling. While classical statistical and controltheoretic approaches successfully describe many aspects of physiology, they often struggle to express abrupt regime transitions and structured coherence-like signatures observed across neural, cardiac, and metabolic processes. In this work, we propose a spiral-time operator framework that remains compatible with standard open quantum system theory by introducing a structured control coordinate that encodes phase coverage and memory channels at the level of effective generators. The framework does not assume long-lived biological quantum coherence, nor does it modify quantum dynamics; rather, it provides a conservative, platform-agnostic mathematical language for modeling information-driven regime transitions in biological systems treated as effective open systems. We define an operational instability functional ∆Φ that quantifies regime divergence in a model-agnostic manner, propose matched-resource null tests, and connect the formalism to existing opensystem tools including non-Markovian kernels and filter-function stability bounds.