Published on in Vol 7, No 6 (2019): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/12770, first published .
Deep Learning Approaches to Detect Atrial Fibrillation Using Photoplethysmographic Signals: Algorithms Development Study

Deep Learning Approaches to Detect Atrial Fibrillation Using Photoplethysmographic Signals: Algorithms Development Study

Deep Learning Approaches to Detect Atrial Fibrillation Using Photoplethysmographic Signals: Algorithms Development Study

Journals

  1. Kwon S, Hong J, Choi E, Lee B, Baik C, Lee E, Jeong E, Koo B, Oh S, Yi Y. Detection of Atrial Fibrillation Using a Ring-Type Wearable Device (CardioTracker) and Deep Learning Analysis of Photoplethysmography Signals: Prospective Observational Proof-of-Concept Study. Journal of Medical Internet Research 2020;22(5):e16443 View
  2. Prasitlumkum N, Cheungpasitporn W, Chokesuwattanaskul A, Thangjui S, Thongprayoon C, Bathini T, Vallabhajosyula S, Kanitsoraphan C, Leesutipornchai T, Chokesuwattanaskul R. Diagnostic accuracy of smart gadgets/wearable devices in detecting atrial fibrillation: A systematic review and meta-analysis. Archives of Cardiovascular Diseases 2021;114(1):4 View
  3. Pereira T, Tran N, Gadhoumi K, Pelter M, Do D, Lee R, Colorado R, Meisel K, Hu X. Photoplethysmography based atrial fibrillation detection: a review. npj Digital Medicine 2020;3(1) View
  4. Dagher L, Shi H, Zhao Y, Marrouche N. Wearables in cardiology: Here to stay. Heart Rhythm 2020;17(5):889 View
  5. Lee Y, Chou W, Chien T, Chou P, Yeh Y, Lee H. An App Developed for Detecting Nurse Burnouts Using the Convolutional Neural Networks in Microsoft Excel: Population-Based Questionnaire Study. JMIR Medical Informatics 2020;8(5):e16528 View
  6. Ma S, Chou W, Chien T, Chow J, Yeh Y, Chou P, Lee H. An App for Detecting Bullying of Nurses Using Convolutional Neural Networks and Web-Based Computerized Adaptive Testing: Development and Usability Study. JMIR mHealth and uHealth 2020;8(5):e16747 View
  7. Mehrang S, Lahdenoja O, Kaisti M, Tadi M, Hurnanen T, Airola A, Knuutila T, Jaakkola J, Jaakkola S, Vasankari T, Kiviniemi T, Airaksinen J, Koivisto T, Pankaala M. Classification of Atrial Fibrillation and Acute Decompensated Heart Failure Using Smartphone Mechanocardiography: A Multilabel Learning Approach. IEEE Sensors Journal 2020;20(14):7957 View
  8. Jeon E, Oh K, Kwon S, Son H, Yun Y, Jung E, Kim M. A Lightweight Deep Learning Model for Fast Electrocardiographic Beats Classification With a Wearable Cardiac Monitor: Development and Validation Study. JMIR Medical Informatics 2020;8(3):e17037 View
  9. Sekelj S, Sandler B, Johnston E, Pollock K, Hill N, Gordon J, Tsang C, Khan S, Ng F, Farooqui U. Detecting undiagnosed atrial fibrillation in UK primary care: Validation of a machine learning prediction algorithm in a retrospective cohort study. European Journal of Preventive Cardiology 2021;28(6):598 View
  10. Cheng P, Chen Z, Li Q, Gong Q, Zhu J, Liang Y. Atrial Fibrillation Identification With PPG Signals Using a Combination of Time-Frequency Analysis and Deep Learning. IEEE Access 2020;8:172692 View
  11. 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
  12. Dall’Olio L, Curti N, Remondini D, Safi Harb Y, Asselbergs F, Castellani G, Uh H. Prediction of vascular aging based on smartphone acquired PPG signals. Scientific Reports 2020;10(1) View
  13. Chou P, Chien T, Yang T, Yeh Y, Chou W, Yeh C. Predicting Active NBA Players Most Likely to Be Inducted into the Basketball Hall of Famers Using Artificial Neural Networks in Microsoft Excel: Development and Usability Study. International Journal of Environmental Research and Public Health 2021;18(8):4256 View
  14. Duncker D, Ding W, Etheridge S, Noseworthy P, Veltmann C, Yao X, Bunch T, Gupta D. Smart Wearables for Cardiac Monitoring—Real-World Use beyond Atrial Fibrillation. Sensors 2021;21(7):2539 View
  15. Fradi M, Khriji L, Machhout M, Hossen A. Automatic heart disease class detection using convolutional neural network architecture‐based various optimizers‐networks. IET Smart Cities 2021;3(1):3 View
  16. Väliaho E, Kuoppa P, Lipponen J, Hartikainen J, Jäntti H, Rissanen T, Kolk I, Pohjantähti-Maaroos H, Castrén M, Halonen J, Tarvainen M, Santala O, Martikainen T. Wrist Band Photoplethysmography Autocorrelation Analysis Enables Detection of Atrial Fibrillation Without Pulse Detection. Frontiers in Physiology 2021;12 View
  17. Sun J, Shen H, Qu Q, Sun W, Kong X. The application of deep learning in electrocardiogram: Where we came from and where we should go?. International Journal of Cardiology 2021;337:71 View
  18. Shin H, Sun S, Lee J, Kim H. Complementary Photoplethysmogram Synthesis From Electrocardiogram Using Generative Adversarial Network. IEEE Access 2021;9:70639 View
  19. Taniguchi H, Takata T, Takechi M, Furukawa A, Iwasawa J, Kawamura A, Taniguchi T, Tamura Y. Explainable Artificial Intelligence Model for Diagnosis of Atrial Fibrillation Using Holter Electrocardiogram Waveforms. International Heart Journal 2021;62(3):534 View
  20. 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
  21. Kareem M, Lei N, Ali A, Ciaccio E, Acharya U, Faust O. A review of patient-led data acquisition for atrial fibrillation detection to prevent stroke. Biomedical Signal Processing and Control 2021;69:102818 View