Frontiers in physiology · 2026
Waveband-resolved nonlinear characterization of SERF-magnetocardiography signals: a phase-space reconstruction framework for exploratory clinical cohort analysis.
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Li Keyi, Zhou Xiangyang, Pang Jiaojiao, Shang Rui, Zhang Yadan, Cui Yangyang, Xu Dong, Xiang Min
Аннотация
BACKGROUND: Spin-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.
METHODS: We 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.
RESULTS: Analysis 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.
CONCLUSIONS: This 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.
METHODS: We 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.
RESULTS: Analysis 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.
CONCLUSIONS: This 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.