Conditional Unlocking Fields as a Cardiac Identity Measure: Necessity Burden Detects Arrhythmia Onset in a Stochastic Leaky Integrate-and-Fire Pacemaker Model

We apply the Conditional Unlocking Fields (CUF) framework, a formal identity maintenance measurement apparatus, to the problem of cardiac arrhythmia detection using a stochastic leaky integrate-and-fire (LIF) neuron as a first principles model of pacemaker cell electrophysiology. The CUF normalised burden (Mb = Mb/k*) provides a dimensionless, self-normalised identity criterion that does not require a population reference database. Because the viability threshold (epsilon) is set at the 35th percentile of baseline ISI deviations, the clinically meaningful results are the relative ordering of Mb across regimes and the presence of a persistence cliff, not absolute threshold crossing. Across a four-regime protocol (normal sinus rhythm, stress ramp, arrhythmia onset, and recovery), Mb rises from 1.077 at baseline to 1.947 at arrhythmia onset, where a persistence cliff (S = 0) is detected. The clinical comparator SDNN moves in the opposite direction (16.3 ms at baseline to 9.8 ms at arrhythmia), confirming that Mb and SDNN capture orthogonal properties of rhythm identity. The result is robust to the choice of viability threshold epsilon across percentiles 20–55 of the baseline inter spike interval (ISI) deviation distribution (Regime III dominant throughout). Four extensions are reported: (1) closed-loop ledger feedback revealing an ISI variance resonance at g ≈ 0.31; (2) mutual information I(ISI; |ISI−ISItarget|) = 4.69 nats at arrhythmia onset; (3) cumulative burden minimised at sigma* = 0.44 mV (CUF stochastic resonance); and (4) positive Lyapunov exponents across all regimes, consistent with chaotic burden dynamics. All code, raw data, and scalars are provided with SHA-256 integrity verification.