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

Formative Evaluation of Digital Health Interventions (2491) Artificial Intelligence (1421) Machine Learning (1501) Wearable Devices and Sensors (903) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1428) mHealth for Data Collection and Research (981)

Published on in Vol 6, No 8 (2022): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/35396, first published .
Diagnosis of Atrial Fibrillation Using Machine Learning With Wearable Devices After Cardiac Surgery: Algorithm Development Study

Diagnosis of Atrial Fibrillation Using Machine Learning With Wearable Devices After Cardiac Surgery: Algorithm Development Study

Diagnosis of Atrial Fibrillation Using Machine Learning With Wearable Devices After Cardiac Surgery: Algorithm Development Study

Authors of this article:

Daisuke Hiraoka1 Author Orcid Image ;   Tomohiko Inui1 Author Orcid Image ;   Eiryo Kawakami2, 3 Author Orcid Image ;   Megumi Oya2, 3 Author Orcid Image ;   Ayumu Tsuji2 Author Orcid Image ;   Koya Honma2 Author Orcid Image ;   Yohei Kawasaki4 Author Orcid Image ;   Yoshihito Ozawa4 Author Orcid Image ;   Yuki Shiko4 Author Orcid Image ;   Hideki Ueda1 Author Orcid Image ;   Hiroki Kohno1 Author Orcid Image ;   Kaoru Matsuura1 Author Orcid Image ;   Michiko Watanabe1 Author Orcid Image ;   Yasunori Yakita1 Author Orcid Image ;   Goro Matsumiya1 Author Orcid Image
Article Authors Cited by (12) Tweetations (4) Metrics

Journals

  1. Moshawrab M, Adda M, Bouzouane A, Ibrahim H, Raad A. Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review. Sensors 2023;23(2):828 View
  2. Manetas-Stavrakakis N, Sotiropoulou I, Paraskevas T, Maneta Stavrakaki S, Bampatsias D, Xanthopoulos A, Papageorgiou N, Briasoulis A. Accuracy of Artificial Intelligence-Based Technologies for the Diagnosis of Atrial Fibrillation: A Systematic Review and Meta-Analysis. Journal of Clinical Medicine 2023;12(20):6576 View
  3. Kim D, Min J, Ko S. Recent Developments and Future Directions of Wearable Skin Biosignal Sensors. Advanced Sensor Research 2024;3(2) View
  4. Ghomrawi H, O’Brien M, Carter M, Macaluso R, Khazanchi R, Fanton M, DeBoer C, Linton S, Zeineddin S, Pitt J, Bouchard M, Figueroa A, Kwon S, Holl J, Jayaraman A, Abdullah F. Applying machine learning to consumer wearable data for the early detection of complications after pediatric appendectomy. npj Digital Medicine 2023;6(1) View
  5. El-Sherbini A, Shah A, Cheng R, Elsebaie A, Harby A, Redfearn D, El-Diasty M. Machine Learning for Predicting Postoperative Atrial Fibrillation After Cardiac Surgery: A Scoping Review of Current Literature. The American Journal of Cardiology 2023;209:66 View
  6. Jiang Z, Van Zoest V, Deng W, Ngai E, Liu J. Leveraging Machine Learning for Disease Diagnoses Based on Wearable Devices: A Survey. IEEE Internet of Things Journal 2023;10(24):21959 View
  7. Hernandez J, Shah K, Spayd R, Davis S. Is wearable technology accurate at detecting atrial fibrillation?. Evidence-Based Practice 2024;27(5):9 View
  8. Ding C, Xiao R, Wang W, Holdsworth E, Hu X. Photoplethysmography based atrial fibrillation detection: a continually growing field. Physiological Measurement 2024;45(4):04TR01 View
  9. Shah S, Chahil V, Battisha A, Haq S, Kalra D. Postoperative Atrial Fibrillation: A Review. Biomedicines 2024;12(9):1968 View
  10. Menezes Junior A, e Silva A, e Silva L, de Lima K, Oliveira H. A Scoping Review of the Use of Artificial Intelligence in the Identification and Diagnosis of Atrial Fibrillation. Journal of Personalized Medicine 2024;14(11):1069 View
  11. Naseri A, Tax D, Reinders M, van der Bilt I. Heart disease detection using an acceleration-deceleration curve-based neural network with consumer-grade smartwatch data. Heliyon 2024;10(21):e39927 View

Books/Policy Documents

  1. Chung C, Roy V, Tse G, Liu H. Signal Processing Driven Machine Learning Techniques for Cardiovascular Data Processing. View