Waveband-resolved nonlinear characterization of SERF-magnetocardiography signals: a phase-space reconstruction framework for exploratory clinical cohort analysis

BackgroundSpin-exchange relaxation-free magnetocardiography (SERF–MCG) offers femtotesla-level sensitivity for noninvasive cardiac monitoring. Conventional analysis pipelines, largely adapted from linear electrocardiography, may not fully capture nonlinear dynamical patterns embedded in cardiac magnetic-field signals. In this retrospective and de-identified cohort study, we aimed to develop and evaluate a waveband-resolved nonlinear analysis framework for exploratory signal-level characterization of SERF–MCG recordings from stringently screened healthy adults and a heterogeneous clinical cardiovascular patient cohort enriched for clinically diagnosed ischemia.MethodsWe developed a waveband-resolved framework that decomposes each SERF–MCG beat into P, QRS, ST, and T segments and reconstructs segment-specific attractors via delay-coordinate embedding. From these reconstructed trajectories, we extracted a 32-dimensional feature set capturing phase-space geometric descriptors, entropy-related descriptors, Lyapunov-related descriptors, and spatial energy/eigenvalue-ratio descriptors. Cross-phase signal associations were assessed using a dual-perspective approach: (i) statistical association matrices derived from feature aggregates and (ii) a four-node self-attention encoder that estimates a 4 × 4 matrix of model-derived association weights between cardiac phases.ResultsAnalysis of 1,848 statistically independent median beats, including 906 stringently screened healthy controls and 942 recordings from a heterogeneous clinical cardiovascular patient cohort enriched for clinically diagnosed ischemia, showed statistically significant but partially overlapping nonlinear feature differences between groups. Repolarization-related ST/T features showed lower sample entropy and altered Lyapunov-related descriptors in the clinical patient cohort, consistent with group-level differences in signal complexity and local trajectory instability. Statistical and attention-based analyses further suggested altered ST–T association patterns at the signal-feature level. These findings should be interpreted as exploratory cohort-level signal differences rather than ischemia-specific causal or diagnostic evidence.ConclusionsThis study presents a waveband-resolved nonlinear SERF–MCG analysis framework for exploratory characterization of cardiac magnetic-field signals. Within the present retrospective and de-identified dataset, the framework identified group-level differences in repolarization-related nonlinear features and cross-phase association patterns between stringently screened healthy controls and a heterogeneous clinical patient cohort. The findings support the methodological feasibility of phase-space reconstruction and attention-assisted association analysis for SERF–MCG signal characterization, while prospective studies with complete clinical covariates and uniform diagnostic verification are required before ischemia-specific or diagnostic claims can be made.