{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T05:59:51Z","timestamp":1790315991702,"version":"4.1.0"},"reference-count":136,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T00:00:00Z","timestamp":1790294400000},"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. Artif. Intell."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Deep learning has revolutionized image analysis, yet most clinical biosignals, especially multi-lead electrocardiograms (ECGs), remain one-dimensional and awkward for modern vision models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We introduce an orthogonal-polynomial imaging (OPI) framework that encodes 12-lead ECGs into a single two-dimensional image through an invertible transformation prior to coarse-graining, producing compact image representations that retain sufficient information for accurate classification. We benchmark the proposed representation against established architectures including ResNet, Transformers, and 1D temporal convolutional models on large-scale PhysioNet ECG datasets.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Our results demonstrate that OPI-based image representations achieve highly competitive classification performance while preserving diagnostically relevant temporal and cross-lead information.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>The framework bridges raw multi-channel biosignals and standard image-based learning architectures without representation loss prior to coarse-graining, offering an effective, scalable, and physics-inspired representation for automated cardiac diagnostics.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/frai.2026.1874928","type":"journal-article","created":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T05:31:50Z","timestamp":1790314310000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Higher-dimensional embedding of time-series data for machine learning"],"prefix":"10.3389","volume":"9","author":[{"given":"Karan","family":"Singh","sequence":"first","affiliation":[{"name":"Centre for High Energy Physics, Indian Institute of Science","place":["Bengaluru, Karnataka, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pingal Pratyush","family":"Nath","sequence":"additional","affiliation":[{"name":"Centre for High Energy Physics, Indian Institute of Science","place":["Bengaluru, Karnataka, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Urbasi","family":"Sinha","sequence":"additional","affiliation":[{"name":"Raman Research Institute","place":["Bengaluru, Karnataka, India"]},{"name":"Department of Physics and Astronomy, University of Calgary","place":["Calgary, AB, Canada"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aninda","family":"Sinha","sequence":"additional","affiliation":[{"name":"Centre for High Energy Physics, Indian Institute of Science","place":["Bengaluru, Karnataka, India"]},{"name":"Department of Physics and Astronomy, University of Calgary","place":["Calgary, AB, Canada"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,25]]},"reference":[{"key":"B1","first-page":"99","article-title":"\u201cAutomatic classification of diabetic retinopathy through segmentation using cnn,\u201d","volume-title":"EAI international conference on IoT technologies for HealthCare","author":"Abbood","year":"2021"},{"key":"B2","doi-asserted-by":"publisher","first-page":"73079","DOI":"10.1109\/ACCESS.2022.3189374","article-title":"Hybrid retinal image enhancement algorithm for diabetic retinopathy diagnostic using deep learning model","volume":"10","author":"Abbood","year":"2022","journal-title":"IEEE Access"},{"key":"B3","doi-asserted-by":"publisher","first-page":"562","DOI":"10.3390\/math11030562","article-title":"Classifying cardiac arrhythmia from ecg signal using 1d cnn deep learning model","volume":"11","author":"Ahmed","year":"2023","journal-title":"Mathematics"},{"key":"B4","doi-asserted-by":"publisher","first-page":"124003","DOI":"10.1088\/1361-6579\/abc960","article-title":"Classification of 12-lead ecgs: the physionet\/computing in cardiology challenge 2020","volume":"41","author":"Alday","year":"2020","journal-title":"Physiol. 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