A Spiral-Time Operator Framework for Heart–Brain Isostasis: Operator-Based Early Detection of Cardiac Instability

Cardiac and neural systems form a tightly coupled regulatory unit whose dysregulation can precede acute events such as arrhythmia or myocardial infarction. While electroencephalography (EEG) and electrocardiography (ECG) are routinely analyzed independently, a unified early-warning diagnostic that captures joint deviation from physiological baseline remains underdeveloped. We present a deterministic, operator-based framework that transfers a spiral-time operator embedding (previously validated as a time-series diagnostic in neural data) to cardiac dynamics and heart–brain coupling. The method uses a triadic embedding ψ(t) = t + iϕ(t) + jχ(t) together with an interpretable deviation functional ∆Φ built from spectral, informational, and coherence observables. No machine learning is required; thresholds are specified a priori. The framework is presented as a conservative signal-analysis methodology designed for prospective clinical validation and falsification.