Malignant arrhythmia risk assessment based on lead-I mobile ECG measurements using machine learning.

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Bibliographic Details
Title: Malignant arrhythmia risk assessment based on lead-I mobile ECG measurements using machine learning.
Authors: Tuboly G; Department of Electrical Engineering and Information Systems, University of Pannonia, Egyetem u. 10, 8200 Veszprém, Hungary. Electronic address: tuboly.gergely@mik.uni-pannon.hu., Kiss O; Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, Hungary. Electronic address: kiss.orsolya1@med.semmelweis-univ.hu., Babity M; Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, Hungary. Electronic address: babity.mate@semmelweis.hu., Zámodics M; Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, Hungary. Electronic address: zamodics.mark@stud.semmelweis.hu., Merkely B; Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, Hungary. Electronic address: merkely.bela@kardio.sote.hu., Kozmann G; Department of Electrical Engineering and Information Systems, University of Pannonia, Egyetem u. 10, 8200 Veszprém, Hungary., Issa MF; Department of Electrical Engineering and Information Systems, University of Pannonia, Egyetem u. 10, 8200 Veszprém, Hungary; Department of Scientific Computing, Faculty of Computers and Artificial Intelligence, Benha University, Benha 13511, Egypt. Electronic address: mohamed.issa@fci.bu.edu.eg.
Source: Journal of electrocardiology [J Electrocardiol] 2026 Mar-Apr; Vol. 95, pp. 154194. Date of Electronic Publication: 2026 Jan 13.
Publication Type: Journal Article
Journal Info: Publisher: Churchill Livingstone Country of Publication: United States NLM ID: 0153605 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1532-8430 (Electronic) Linking ISSN: 00220736 NLM ISO Abbreviation: J Electrocardiol Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1532-8430
DOI:10.1016/j.jelectrocard.2026.154194