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This paper is in the following e-collection/theme issue:

Cardiac Arrhythmia, Atrial Fibrillation (145) Clinical Information and Decision Making (3898) Machine Learning (3347) Novel Sensors and Data Acquisition Methods in Cardiology (271) Cardiac Risk and Cardiac Risk Calculators (186) Artificial Intelligence (5034) Computer-Aided Diagnosis (445)

Published on in Vol 10 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/83714, first published .
Doctor and patient review EKG results on a clipboard.

Deep Neural Networks for Automatic Atrial Fibrillation Detection Using Long-Term Ambulatory Electrocardiography: Retrospective Diagnostic Accuracy Study

Deep Neural Networks for Automatic Atrial Fibrillation Detection Using Long-Term Ambulatory Electrocardiography: Retrospective Diagnostic Accuracy Study

Authors of this article:

Jagdeep Sedha1, 2 Author Orcid Image ;   Jukka A Lipponen3 Author Orcid Image ;   Antti Aho1 Author Orcid Image ;   Helena Jäntti4 Author Orcid Image ;   Onni E Santala1, 4 Author Orcid Image ;   Tomi P Laitinen1, 5 Author Orcid Image ;   Tiina M Laitinen3 Author Orcid Image ;   Jari Halonen1, 2 Author Orcid Image ;   Mika P Tarvainen3 Author Orcid Image ;   Eemu-Samuli Seljola1, 6, 7 Author Orcid Image ;   Noora S Naukkarinen1 Author Orcid Image ;   Olli Rantula1, 2 Author Orcid Image ;   Tuomas T Rissanen1, 8 Author Orcid Image ;   Juha E K Hartikainen1, 2 Author Orcid Image ;   Tero J Martikainen9 Author Orcid Image
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Journals

  1. Morikawa N, Yuda E. Motion Artifacts Quantification in Ambulatory Holter ECG Across Age Groups: Insights from Simultaneous 3-Axis Accelerometry. Journal of Experimental and Theoretical Analyses 2026;4(4):35 View