Published on in Vol 22, No 5 (2020): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/16443, first published .
Detection of Atrial Fibrillation Using a Ring-Type Wearable Device (CardioTracker) and Deep Learning Analysis of Photoplethysmography Signals: Prospective Observational Proof-of-Concept Study

Detection of Atrial Fibrillation Using a Ring-Type Wearable Device (CardioTracker) and Deep Learning Analysis of Photoplethysmography Signals: Prospective Observational Proof-of-Concept Study

Detection of Atrial Fibrillation Using a Ring-Type Wearable Device (CardioTracker) and Deep Learning Analysis of Photoplethysmography Signals: Prospective Observational Proof-of-Concept Study

Journals

  1. Hong W, Lee W. Wearable sensors for continuous oral cavity and dietary monitoring toward personalized healthcare and digital medicine. The Analyst 2020;145(24):7796 View
  2. Lopez Perales C, Van Spall H, Maeda S, Jimenez A, Laţcu D, Milman A, Kirakoya-Samadoulougou F, Mamas M, Muser D, Casado Arroyo R. Mobile health applications for the detection of atrial fibrillation: a systematic review. EP Europace 2021;23(1):11 View
  3. Liaqat S, Dashtipour K, Zahid A, Assaleh K, Arshad K, Ramzan N. Detection of Atrial Fibrillation Using a Machine Learning Approach. Information 2020;11(12):549 View
  4. Han D, Bashar S, Mohagheghian F, Ding E, Whitcomb C, McManus D, Chon K. Premature Atrial and Ventricular Contraction Detection Using Photoplethysmographic Data from a Smartwatch. Sensors 2020;20(19):5683 View
  5. GÜNDÜZ O, TEPE C, ŞENYER N, ODABAS M. Tıbbi Cihaz Sektöründe Kullanılan Derin Öğrenme Yöntemlerine Genel Bakış. Black Sea Journal of Engineering and Science 2021;4(2):68 View
  6. Shin H, Sun S, Lee J, Kim H. Complementary Photoplethysmogram Synthesis From Electrocardiogram Using Generative Adversarial Network. IEEE Access 2021;9:70639 View
  7. Roxburgh T, Li A, Guenancia C, Pernollet P, Bouleti C, Alos B, Gras M, Kerforne T, Frasca D, Le Gal F, Christiaens L, Degand B, Garcia R. Virtual Reality for Sedation During Atrial Fibrillation Ablation in Clinical Practice: Observational Study. Journal of Medical Internet Research 2021;23(5):e26349 View
  8. Kotalczyk A, Mazurek M, Jędrzejczyk-Patej E. New methods of detecting atrial fibrillation. In a good rythm 2020;3(56):23 View
  9. Olier I, Ortega-Martorell S, Pieroni M, Lip G. How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management. Cardiovascular Research 2021;117(7):1700 View
  10. Ahmed A, Ahmad M. Evaluating the Use of AI in Implantable Loop Recorders for AF Detection. JACC: Clinical Electrophysiology 2021;7(8):1068 View
  11. Mittal S, Henry C, Gardella C. Reply. JACC: Clinical Electrophysiology 2021;7(8):1069 View
  12. Dilaveris P, Antoniou C, Caiani E, Casado-Arroyo R, Climent A, Cluitmans M, Cowie M, Doehner W, Guerra F, Jensen M, Kalarus Z, Locati E, Platonov P, Simova I, Schnabel R, Schuuring M, Tsivgoulis G, Lumens J. ESC Working Group on e-Cardiology Position Paper: accuracy and reliability of electrocardiogram monitoring in the detection of atrial fibrillation in cryptogenic stroke patients. European Heart Journal - Digital Health 2022;3(3):341 View
  13. Kumar S, Victoria-Castro A, Melchinger H, O’Connor K, Psotka M, Desai N, Ahmad T, Wilson F. Wearables in Cardiovascular Disease. Journal of Cardiovascular Translational Research 2023;16(3):557 View
  14. Adasuriya G, Haldar S. Remote Monitoring of Cardiac Arrhythmias Using Wearable Digital Technology: Paradigm Shift or Pipe Dream?. European Journal of Arrhythmia & Electrophysiology 2022;8(1):7 View
  15. Yuan K, Tsai L, Lai K, Teng S, Lo Y, Peng S. Using Transfer Learning Method to Develop an Artificial Intelligence Assisted Triaging for Endotracheal Tube Position on Chest X-ray. Diagnostics 2021;11(10):1844 View
  16. Huhn S, Axt M, Gunga H, Maggioni M, Munga S, Obor D, Sié A, Boudo V, Bunker A, Sauerborn R, Bärnighausen T, Barteit S. The Impact of Wearable Technologies in Health Research: Scoping Review. JMIR mHealth and uHealth 2022;10(1):e34384 View
  17. Chang C, Lai F, Christian M, Chen Y, Hsu C, Chen Y, Chang D, Roan T, Yu Y. Deep Learning–Assisted Burn Wound Diagnosis: Diagnostic Model Development Study. JMIR Medical Informatics 2021;9(12):e22798 View
  18. Loh H, Xu S, Faust O, Ooi C, Barua P, Chakraborty S, Tan R, Molinari F, Acharya U. Application of photoplethysmography signals for healthcare systems: An in-depth review. Computer Methods and Programs in Biomedicine 2022;216:106677 View
  19. Chan N, Orchard J, Agbayani M, Boddington D, Chao T, Johar S, John B, Joung B, Krishinan S, Krittayaphong R, Kurokawa S, Lau C, Lim T, Linh P, Long V, Naik A, Okumura Y, Sasano T, Yan B, Raharjo S, Hanafy D, Yuniadi Y, Nwe N, Awan Z, Huang H, Freedman B. 2021 Asia Pacific Heart Rhythm Society (APHRS) practice guidance on atrial fibrillation screening. Journal of Arrhythmia 2022;38(1):31 View
  20. Naseri Jahfari A, Tax D, Reinders M, van der Bilt I. Machine Learning for Cardiovascular Outcomes From Wearable Data: Systematic Review From a Technology Readiness Level Point of View. JMIR Medical Informatics 2022;10(1):e29434 View
  21. Bonini N, Vitolo M, Imberti J, Proietti M, Romiti G, Boriani G, Paaske Johnsen S, Guo Y, Lip G. Mobile health technology in atrial fibrillation. Expert Review of Medical Devices 2022;19(4):327 View
  22. Prieto-Avalos G, Cruz-Ramos N, Alor-Hernández G, Sánchez-Cervantes J, Rodríguez-Mazahua L, Guarneros-Nolasco L. Wearable Devices for Physical Monitoring of Heart: A Review. Biosensors 2022;12(5):292 View
  23. Valenti S, Volpes G, Parisi A, Peri D, Lee J, Faes L, Busacca A, Pernice R. Wearable Multisensor Ring-Shaped Probe for Assessing Stress and Blood Oxygenation: Design and Preliminary Measurements. Biosensors 2023;13(4):460 View
  24. Santala O, Lipponen J, Jäntti H, Rissanen T, Tarvainen M, Väliaho E, Rantula O, Naukkarinen N, Hartikainen J, Martikainen T, Halonen J. Novel Technologies in the Detection of Atrial Fibrillation: Review of Literature and Comparison of Different Novel Technologies for Screening of Atrial Fibrillation. Cardiology in Review 2024;32(5):440 View
  25. Ciccarelli M, Giallauria F, Carrizzo A, Visco V, Silverio A, Cesaro A, Calabrò P, De Luca N, Mancusi C, Masarone D, Pacileo G, Tourkmani N, Vigorito C, Vecchione C. Artificial intelligence in cardiovascular prevention: new ways will open new doors. Journal of Cardiovascular Medicine 2023;24(Supplement 2):e106 View
  26. Park J. Machine Learning for Predicting Atrial Fibrillation Recurrence After Cardioversion: A Modest Leap Forward. Korean Circulation Journal 2023;53(10):690 View
  27. Musa E, Levitan A, Hughes G. Wearable Health Technology for the Diagnosis and Management of Atrial Fibrillation: A Systematic Review. Undergraduate Research in Natural and Clinical Science and Technology (URNCST) Journal 2021;5(4):1 View
  28. Joung J, Jung C, Lee H, Chae M, Kim H, Park J, Shin W, Kim C, Lee M, Choi C. Continuous cuffless blood pressure monitoring using photoplethysmography-based PPG2BP-net for high intrasubject blood pressure variations. Scientific Reports 2023;13(1) View
  29. Kwon S, Lee E, Ju H, Ahn H, Lee S, Choi E, Suh J, Oh S, Rhee W. Machine Learning Prediction for the Recurrence After Electrical Cardioversion of Patients With Persistent Atrial Fibrillation. Korean Circulation Journal 2023;53(10):677 View
  30. Momynaliev K, Ivanov I. Portable health monitoring devices. Biomedical Engineering 2023;57(4):295 View
  31. Wang W, Zheng L, Cheng H, Xu X, Meng B. Applications and progress of machine learning in wearable intelligent sensing systems. Chinese Science Bulletin 2023;68(34):4630 View
  32. Hong W. Advances and Opportunities of Mobile Health in the Postpandemic Era: Smartphonization of Wearable Devices and Wearable Deviceization of Smartphones. JMIR mHealth and uHealth 2024;12:e48803 View
  33. Salvi M, Acharya M, Seoni S, Faust O, Tan R, Barua P, García S, Molinari F, Acharya U. Artificial intelligence for atrial fibrillation detection, prediction, and treatment: A systematic review of the last decade (2013–2023). WIREs Data Mining and Knowledge Discovery 2024;14(3) View
  34. 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
  35. Wasilewski T, Kamysz W, Gębicki J. AI-Assisted Detection of Biomarkers by Sensors and Biosensors for Early Diagnosis and Monitoring. Biosensors 2024;14(7):356 View
  36. Saad H, Zaki J, Abdelsalam M. Employing of machine learning and wearable devices in healthcare system: tasks and challenges. Neural Computing and Applications 2024;36(29):17829 View
  37. Jafleh E, Alnaqbi F, Almaeeni H, Faqeeh S, Alzaabi M, Al Zaman K. The Role of Wearable Devices in Chronic Disease Monitoring and Patient Care: A Comprehensive Review. Cureus 2024 View
  38. Fiore M, Bianconi A, Sicari G, Conni A, Lenzi J, Tomaiuolo G, Zito F, Golinelli D, Sanmarchi F. The Use of Smart Rings in Health Monitoring—A Meta-Analysis. Applied Sciences 2024;14(23):10778 View
  39. Xie C, Wang Z, Yang C, Liu J, Liang H. Machine Learning for Detecting Atrial Fibrillation from ECGs: Systematic Review and Meta-Analysis. Reviews in Cardiovascular Medicine 2024;25(1) View

Books/Policy Documents

  1. Tung J, Gower S, Ooteghem K, Nouredanesh M, Gage W. Digital Health. View
  2. Sureja N, Mehta K, Shah V, Patel G. Machine Learning for Advanced Functional Materials. View
  3. Chung C, Roy V, Tse G, Liu H. Signal Processing Driven Machine Learning Techniques for Cardiovascular Data Processing. View