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 2020:204748732094233 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