Detection of abnormal left ventricular geometry in patients without cardiovascular disease through machine learning: An ECG-based approach.
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| Title: | Detection of abnormal left ventricular geometry in patients without cardiovascular disease through machine learning: An ECG-based approach. |
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| Authors: | Angelaki E; Institute of Theoretical and Computational Physics and Department of Physics, University of Crete, Heraklion, Greece.; Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA., Marketou ME; Department of Cardiology, Heraklion University Hospital, Heraklion, Greece., Barmparis GD; Institute of Theoretical and Computational Physics and Department of Physics, University of Crete, Heraklion, Greece., Patrianakos A; Department of Cardiology, Heraklion University Hospital, Heraklion, Greece., Vardas PE; Department of Cardiology, Heraklion University Hospital, Heraklion, Greece.; Heart Sector, Hygeia Hospitals Group, Athens, Greece., Parthenakis F; Department of Cardiology, Heraklion University Hospital, Heraklion, Greece., Tsironis GP; Institute of Theoretical and Computational Physics and Department of Physics, University of Crete, Heraklion, Greece.; Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA. |
| Source: | Journal of clinical hypertension (Greenwich, Conn.) [J Clin Hypertens (Greenwich)] 2021 May; Vol. 23 (5), pp. 935-945. Date of Electronic Publication: 2021 Jan 28. |
| Publication Type: | Journal Article; Research Support, Non-U.S. Gov't |
| Journal Info: | Publisher: Wiley Periodicals Inc Country of Publication: United States NLM ID: 100888554 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1751-7176 (Electronic) Linking ISSN: 15246175 NLM ISO Abbreviation: J Clin Hypertens (Greenwich) Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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