Published on in Vol 7, No 3 (2019): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/12284, first published .
Mobile Phone–Based Use of the Photoplethysmography Technique to Detect Atrial Fibrillation in Primary Care: Diagnostic Accuracy Study of the FibriCheck App

Mobile Phone–Based Use of the Photoplethysmography Technique to Detect Atrial Fibrillation in Primary Care: Diagnostic Accuracy Study of the FibriCheck App

Mobile Phone–Based Use of the Photoplethysmography Technique to Detect Atrial Fibrillation in Primary Care: Diagnostic Accuracy Study of the FibriCheck App

Journals

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  83. Isaksen J, Arildsen B, Lind C, Nørregaard M, Vernooy K, Schotten U, Jespersen T, Betz K, Hermans A, Kanters J, Linz D. Raw photoplethysmogram waveforms versus peak-to-peak intervals for machine learning detection of atrial fibrillation: Does waveform matter?. Computer Methods and Programs in Biomedicine 2025;260:108537 View
  84. Spooner M, Messé S, Chaturvedi S, Do M, Gluckman T, Han J, Russo A, Saxonhouse S, Wiggins N. 2024 ACC Expert Consensus Decision Pathway on Practical Approaches for Arrhythmia Monitoring After Stroke. Journal of the American College of Cardiology 2024 View

Books/Policy Documents

  1. Tao J, Xie S, Jiang J, Wei W. Artificial Intelligence Algorithms and Applications. View
  2. Lemay M, Bertschi M, Sola J, Renevey P, Genzoni E, Proença M, Ferrario D, Braun F, Parak J, Korhonen I. Wearable Sensors. View