{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T17:36:32Z","timestamp":1791826592871,"version":"4.3.4"},"reference-count":30,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T00:00:00Z","timestamp":1782691200000},"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. Cardiovasc. Med."],"abstract":"<jats:p>\n                    Portable reduced-lead ECG devices offer a practical path to pre-hospital emergency care and community cardiovascular screening, yet a foundational question has remained unresolved: which subset of the standard 12 leads should such a device actually record? Prior work evaluates a small number of fixed configurations selected by convention, without systematic comparison across the full combinatorial space or adequate accounting for the true cost of electrode attachment. We present the first exhaustive benchmark evaluating all 4,094 C(12, N) lead subsets for\n                    <jats:italic>N<\/jats:italic>\n                    \u2009=\u20091\u201311 under four reconstruction paradigms\u2014linear regression, ridge regression, a lightweight 1-D convolutional network, and a Transformer encoder\u2013decoder\u2014on the PTB-XL dataset. Performance is assessed along three orthogonal axes: reconstruction fidelity (PCC, RMSE, SNR on withheld leads), downstream diagnostic accuracy (macro-F1 across five cardiac superclasses via a classifier trained on real 12-lead signals), and acquisition efficiency operationalised as electrode contact burden. A Composite Lead Score with five-point \u03b1-sensitivity analysis identifies\n                    <jats:italic>N<\/jats:italic>\n                    \u2009=\u20094 as the consensus efficiency\u2013accuracy knee (mean macro-F1\u2009=\u20090.631, 93.5% of the 12-lead upper bound). External zero-shot validation on CPSC2018 (6,877 records) and Chapman-Shaoxing (45,152 records) confirms that all three deployment-recommended configurations retain\u2009\u2265\u200983% of within-PTB-XL three-class F1 on both cohorts (Chapman 92\u201399%; CPSC 83\u201391%), and the\n                    <jats:italic>N<\/jats:italic>\n                    \u2009=\u20093 knee replicates across all four architectures on Chapman, supporting interpretation of the knee as a property of the lead system's information geometry rather than of any specific architecture or development cohort. A frozen-classifier sensitivity control (\n                    <jats:italic>\u0394<\/jats:italic>\n                    F1\u2009=\u2009\u2009+\u20090.0006 at\n                    <jats:italic>N<\/jats:italic>\n                    \u2009=\u20094 after fine-tuning, an order of magnitude smaller than the cross-N marginal gain) confirms the benchmark's robustness to downstream-model assumptions. Evidence-based configurations are derived for three deployment scenarios: V6 for pre-hospital triage, I\u2009+\u2009II\u2009+\u2009AVR\u2009+\u2009AVF for community screening, and a 7-lead set at 5 contacts for home monitoring; we present these as population-level defaults whose within-tier specific lead choice may benefit from cohort-specific re-derivation. All code and benchmark results are publicly released.\n                  <\/jats:p>","DOI":"10.3389\/fcvm.2026.1856211","type":"journal-article","created":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T05:40:22Z","timestamp":1782711622000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Systematic benchmark of reduced-lead configurations for 12-lead ECG reconstruction: multi-model evaluation across all possible subsets"],"prefix":"10.3389","volume":"13","author":[{"given":"Xinyu","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Biomedical Engineering and Instrument Science, Zhejiang University","place":["Hangzhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hailing","family":"Cai","sequence":"additional","affiliation":[{"name":"College of Biomedical Engineering and Instrument Science, Zhejiang University","place":["Hangzhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huilong","family":"Duan","sequence":"additional","affiliation":[{"name":"College of Biomedical Engineering and Instrument Science, Zhejiang University","place":["Hangzhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xudong","family":"Lu","sequence":"additional","affiliation":[{"name":"College of Biomedical Engineering and Instrument Science, Zhejiang University","place":["Hangzhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,6,29]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1038\/s41569-021-00522-7","article-title":"Smart wearable devices in cardiovascular care: where we are and how to move forward.","volume":"18","author":"Bayoumy","year":"2021","journal-title":"Nat Rev 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