{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T05:05:08Z","timestamp":1789621508720,"version":"build-2803163510"},"reference-count":21,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T00:00:00Z","timestamp":1789603200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Physiol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Spin-exchange relaxation-free magnetocardiography (SERF\u2013MCG) 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\u2013MCG recordings from stringently screened healthy adults and a heterogeneous clinical cardiovascular patient cohort enriched for clinically diagnosed ischemia.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We developed a waveband-resolved framework that decomposes each SERF\u2013MCG 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 \u00d7 4 matrix of model-derived association weights between cardiac phases.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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\u2013T 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study presents a waveband-resolved nonlinear SERF\u2013MCG 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\u2013MCG signal characterization, while prospective studies with complete clinical covariates and uniform diagnostic verification are required before ischemia-specific or diagnostic claims can be made.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fphys.2026.1770392","type":"journal-article","created":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T04:50:08Z","timestamp":1789620608000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Waveband-resolved nonlinear characterization of SERF-magnetocardiography signals: a phase-space reconstruction framework for exploratory clinical cohort analysis"],"prefix":"10.3389","volume":"17","author":[{"given":"Keyi","family":"Li","sequence":"first","affiliation":[{"name":"Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University","place":["Beijing, China"]},{"name":"Zhejiang Provincial Key Laboratory of Ultra-Weak Magnetic-Field Space and Applied Technology, Hangzhou Innovation Institute, Beihang University","place":["Hangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangyang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University","place":["Beijing, China"]},{"name":"Hefei National Laboratory","place":["Hefei, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaojiao","family":"Pang","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine, Qilu Hospital of Shandong University","place":["Jinan, China"]},{"name":"Shandong Key Laboratory for 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