Published on in Vol 8, No 3 (2020): March

A Lightweight Deep Learning Model for Fast Electrocardiographic Beats Classification With a Wearable Cardiac Monitor: Development and Validation Study

A Lightweight Deep Learning Model for Fast Electrocardiographic Beats Classification With a Wearable Cardiac Monitor: Development and Validation Study

A Lightweight Deep Learning Model for Fast Electrocardiographic Beats Classification With a Wearable Cardiac Monitor: Development and Validation Study

Journals

  1. Chen C, Lin Y, Lee S, Tsai W, Huang T, Liu Y, Cheng M, Dai C. Automated ECG classification based on 1D deep learning network. Methods 2022;202:127 View
  2. Alqudah A, Alqudah A. Deep learning for single-lead ECG beat arrhythmia-type detection using novel iris spectrogram representation. Soft Computing 2022;26(3):1123 View
  3. Thapa R, Garikipati A, Shokouhi S, Hurtado M, Barnes G, Hoffman J, Calvert J, Katzmann L, Mao Q, Das R. Predicting Falls in Long-term Care Facilities: Machine Learning Study. JMIR Aging 2022;5(2):e35373 View
  4. Rominger C, Schwerdtfeger A. Feelings from the Heart Part II: Simulation and Validation of Static and Dynamic HRV Decrease-Trigger Algorithms to Detect Stress in Firefighters. Sensors 2022;22(8):2925 View
  5. Mazumder O, Banerjee R, Roy D, Mukherjee A, Ghose A, Khandelwal S, Sinha A. Computational Model for Therapy Optimization of Wearable Cardioverter Defibrillator: Shockable Rhythm Detection and Optimal Electrotherapy. Frontiers in Physiology 2021;12 View
  6. Svennberg E, Tjong F, Goette A, Akoum N, Di Biase L, Bordachar P, Boriani G, Burri H, Conte G, Deharo J, Deneke T, Drossart I, Duncker D, Han J, Heidbuchel H, Jais P, de Oliveira Figueiredo M, Linz D, Lip G, Malaczynska-Rajpold K, Márquez M, Ploem C, Soejima K, Stiles M, Wierda E, Vernooy K, Leclercq C, Meyer C, Pisani C, Pak H, Gupta D, Pürerfellner H, Crijns H, Chavez E, Willems S, Waldmann V, Dekker L, Wan E, Kavoor P, Turagam M, Sinner M. How to use digital devices to detect and manage arrhythmias: an EHRA practical guide. EP Europace 2022;24(6):979 View
  7. Karthik S, Santhosh M, S. Kavitha M, Christopher Paul A. Automated Deep Learning Based Cardiovascular Disease Diagnosis Using ECG Signals. Computer Systems Science and Engineering 2022;42(1):183 View
  8. Petmezas G, Stefanopoulos L, Kilintzis V, Tzavelis A, Rogers J, Katsaggelos A, Maglaveras N. State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review. JMIR Medical Informatics 2022;10(8):e38454 View
  9. 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
  10. Zhou X, Zhu X, Nakamura K, Noro M. Electrocardiogram Quality Assessment with a Generalized Deep Learning Model Assisted by Conditional Generative Adversarial Networks. Life 2021;11(10):1013 View
  11. Lee S, Chu Y, Ryu J, Park Y, Yang S, Koh S. Artificial Intelligence for Detection of Cardiovascular-Related Diseases from Wearable Devices: A Systematic Review and Meta-Analysis. Yonsei Medical Journal 2022;63(Suppl):S93 View
  12. Lee K, Park H, Kim J, Kim H, Chon S, Kim S, Jang J, Kim J, Jang S, Gil Y, Son H. Compressed Deep Learning to Classify Arrhythmia in an Embedded Wearable Device. Sensors 2022;22(5):1776 View