{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T13:20:20Z","timestamp":1768569620031,"version":"3.49.0"},"posted":{"date-parts":[[2025,7,14]]},"group-title":"Cardiovascular Medicine","reference-count":24,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2025,7,14]],"date-time":"2025-07-14T00:00:00Z","timestamp":1752451200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2025,7,14]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>Determination of cardiac output (CO) is essential to the clinical management of cardiovascular compromise. However, the invasiveness, procedural risks, and reliance on specialized infrastructure limit accessibility and scalability of standard-of-care right heart catheterization (RHC). Seismocardiography (SCG), a non-invasive technique which records subtle chest wall vibrations generated by cardiac mechanical activity, may offer a promising alternative for CO determination.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Objectives<\/jats:title>\n                  <jats:p>To develop and evaluate a deep learning model for estimating CO directly from SCG, electrocardiogram (ECG), and body mass index (BMI) in heart failure patients undergoing RHC.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>We trained a deep convolutional neural network for CO estimation using an open-access dataset comprising 73 heart failure patients with simultaneous RHC, SCG, and ECG recordings. Model performance was evaluated using a rotating leave-pair-out cross-validation strategy.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>\n                    When estimating CO, the deep learning model achieved a mean bias of -0.35 L\/min with limits of agreement (LoA) from -2.21 to 1.51 L\/min. When predicting cardiac index in patients with a reference index &lt; 2.2 L\/min\/m\n                    <jats:sup>2<\/jats:sup>\n                    , the model yielded a mean bias of 0.07 L\/min\/m\n                    <jats:sup>2<\/jats:sup>\n                    with LoA from -0.35 to 0.48 L\/min\/m\n                    <jats:sup>2<\/jats:sup>\n                    .\n                  <\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>This study demonstrates the feasibility of using deep learning in combination with wearable SCG sensors to non-invasively estimate CO. Model performance was particularly strong in low-output states. These findings highlight the potential of SCG-based monitoring to augment clinical decision-making in settings where invasive measurements are impractical or unavailable. Prospective multicenter validation is needed to confirm generalizability and assess clinical impact.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Sources of Support<\/jats:title>\n                  <jats:p>This work was supported by NIH grants T32 HL129964 (N.J.K.), K08 ES037420 (N.J.K.), R01 HL124021 (S.Y.C.), R01 HL122596 (S.Y.C.); R01 HL151228 (S.Y.C.); the McKamish Family Foundation, the Hemophilia Center of Western Pennsylvania, and the Institute for Transfusion Medicine (N.J.K.), United Therapeutics Jenesis Innovative Research Award (N.J.K.), and the Pulmonary Hypertension Association (N.J.K.).<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Disclosures<\/jats:title>\n                  <jats:p>S.Y.C. has served as a consultant for Merck, Janssen, and United Therapeutics; S.Y.C. is a director, officer, and shareholder in Synhale Therapeutics and Amlysion Therapeutics; S.Y.C. and N.J.K. hold research grants from United Therapeutics; S.Y.C. holds research grants from Bayer and the WoodNext Foundation. S.Y.C. has filed patent applications regarding the targeting of metabolism in pulmonary hypertension. Other authors: none.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Twitter Summary<\/jats:title>\n                  <jats:p>We developed a deep learning model to non-invasively estimate cardiac output from wearable seismocardiogram (SCG) signals in patients undergoing right heart catheterization. This is the largest study to date using SCG for noninvasive cardiac output monitoring. #HeartFailure, #WearableTech, #AIInCardiology.<\/jats:p>\n                <\/jats:sec>","DOI":"10.1101\/2025.07.11.25331386","type":"posted-content","created":{"date-parts":[[2025,7,14]],"date-time":"2025-07-14T23:20:11Z","timestamp":1752535211000},"source":"Crossref","is-referenced-by-count":0,"title":["Deep learning predicts cardiac output from seismocardiographic signals in heart failure"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8269-1930","authenticated-orcid":false,"given":"Jesse","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7465-0581","authenticated-orcid":false,"given":"Seyed M.","family":"Nouraie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0455-5348","authenticated-orcid":false,"given":"Neil J.","family":"Kelly","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9520-7527","authenticated-orcid":false,"given":"Stephen Y.","family":"Chan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"key":"2025071604350556000_2025.07.11.25331386v1.1","doi-asserted-by":"publisher","DOI":"10.1002\/ccd.28329"},{"key":"2025071604350556000_2025.07.11.25331386v1.2","doi-asserted-by":"publisher","DOI":"10.1016\/j.healun.2022.10.028"},{"key":"2025071604350556000_2025.07.11.25331386v1.3","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2025.02.018"},{"key":"2025071604350556000_2025.07.11.25331386v1.4","doi-asserted-by":"publisher","DOI":"10.1016\/j.cardfail.2023.09.003"},{"key":"2025071604350556000_2025.07.11.25331386v1.5","doi-asserted-by":"publisher","DOI":"10.1016\/j.echo.2014.09.009"},{"key":"2025071604350556000_2025.07.11.25331386v1.6","doi-asserted-by":"publisher","DOI":"10.1186\/s13054-016-1401-7"},{"key":"2025071604350556000_2025.07.11.25331386v1.7","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.26638"},{"key":"2025071604350556000_2025.07.11.25331386v1.8","doi-asserted-by":"publisher","DOI":"10.1007\/s10554-024-03054-6"},{"key":"2025071604350556000_2025.07.11.25331386v1.9","first-page":"87","article-title":"The Method of Seismocardiography","volume":"18","year":"1964","journal-title":"Kardiologiia"},{"key":"2025071604350556000_2025.07.11.25331386v1.10","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2020.3032938"},{"key":"2025071604350556000_2025.07.11.25331386v1.11","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2022.3147066"},{"key":"2025071604350556000_2025.07.11.25331386v1.12","doi-asserted-by":"publisher","DOI":"10.1161\/JAHA.122.026067"},{"key":"2025071604350556000_2025.07.11.25331386v1.13","doi-asserted-by":"publisher","DOI":"10.13026\/133D-PK11"},{"key":"2025071604350556000_2025.07.11.25331386v1.14","doi-asserted-by":"publisher","DOI":"10.1161\/CIR.0000000000000973"},{"key":"2025071604350556000_2025.07.11.25331386v1.15","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1912.01703"},{"key":"2025071604350556000_2025.07.11.25331386v1.16","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-019-0686-2"},{"key":"2025071604350556000_2025.07.11.25331386v1.17","doi-asserted-by":"publisher","DOI":"10.1002\/ccd.26888"},{"key":"2025071604350556000_2025.07.11.25331386v1.18","doi-asserted-by":"publisher","DOI":"10.5405\/jmbe.847"},{"key":"2025071604350556000_2025.07.11.25331386v1.19","doi-asserted-by":"publisher","DOI":"10.1161\/01.CIR.98.17.1762"},{"key":"2025071604350556000_2025.07.11.25331386v1.20","doi-asserted-by":"publisher","DOI":"10.1016\/j.jchf.2025.102527"},{"key":"2025071604350556000_2025.07.11.25331386v1.21","doi-asserted-by":"publisher","DOI":"10.1007\/s42979-021-00815-1"},{"key":"2025071604350556000_2025.07.11.25331386v1.22","doi-asserted-by":"publisher","DOI":"10.1016\/j.jchf.2025.102513"},{"key":"2025071604350556000_2025.07.11.25331386v1.23","doi-asserted-by":"publisher","DOI":"10.1016\/j.cardfail.2023.07.006"},{"key":"2025071604350556000_2025.07.11.25331386v1.24","unstructured":"Investigators GC , Rasmussen M , Prado A , et al. 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