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

Machine Learning (1501) Novel Sensors and Data Acquisition Methods in Cardiology (200) Artificial Intelligence (1421)

Published on in Vol 9, No 4 (2021): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/25347, first published .
Reliable Deep Learning–Based Detection of Misplaced Chest Electrodes During Electrocardiogram Recording: Algorithm Development and Validation

Reliable Deep Learning–Based Detection of Misplaced Chest Electrodes During Electrocardiogram Recording: Algorithm Development and Validation

Reliable Deep Learning–Based Detection of Misplaced Chest Electrodes During Electrocardiogram Recording: Algorithm Development and Validation

Authors of this article:

Khaled Rjoob1 Author Orcid Image ;   Raymond Bond1 Author Orcid Image ;   Dewar Finlay1 Author Orcid Image ;   Victoria McGilligan2 Author Orcid Image ;   Stephen J Leslie3 Author Orcid Image ;   Ali Rababah1 Author Orcid Image ;   Aleeha Iftikhar1 Author Orcid Image ;   Daniel Guldenring4 Author Orcid Image ;   Charles Knoery3 Author Orcid Image ;   Anne McShane5 Author Orcid Image ;   Aaron Peace6 Author Orcid Image
Article Authors Cited by (6) Tweetations (1) Metrics

Journals

  1. Bond R, Finlay D, Al-Zaiti S, Macfarlane P. Machine learning with electrocardiograms: A call for guidelines and best practices for ‘stress testing’ algorithms. Journal of Electrocardiology 2021;69:1 View
  2. Petmezas G, Stefanopoulos L, Kilintzis V, Tzavelis A, Rogers J, Katsaggelos A, Maglaveras N. State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review. JMIR Medical Informatics 2022;10(8):e38454 View
  3. Kim S, Park Y, Kim J, Kim E, Lee D, Lee J, Cheon J, Park J. Magnetic Manipulation of Locomotive Liquid Electrodes for Wireless Active Cardiac Monitoring. ACS Applied Materials & Interfaces 2023;15(24):28954 View
  4. Moreno-Sánchez P, García-Isla G, Corino V, Vehkaoja A, Brukamp K, van Gils M, Mainardi L. ECG-based data-driven solutions for diagnosis and prognosis of cardiovascular diseases: A systematic review. Computers in Biology and Medicine 2024;172:108235 View
  5. Huang Y, Wang M, Li Y, Cai W. A lightweight deep learning approach for detecting electrocardiographic lead misplacement. Physiological Measurement 2024;45(5):055006 View