Signal Processing and Deep Learning Based Smartwatch Photoplethysmography Data Classification of Atrial Fibrillation, Premature Atrial and Ventricular Contraction
Author:
Affiliation:
1. National University of Sciences and Technology,Department of Computer and Software Engineering,Islamabad,Pakistan
2. Princess Nourah bint Abdulrahman University,Department of Computer Sciences,Riyadh,Saudi Arabia,11671
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10284032/10284045/10284500.pdf?arnumber=10284500
Reference21 articles.
1. A new deep learning algorithm of 12-lead electrocardiogram for identifying atrial fibrillation during sinus rhythm
2. 2014 aha/acc/hrs guideline for the management of patients with atrial fibrillation: a report of the american college of cardiology/american heart association task force on practice guidelines and the heart rhythm society;january;Journal of the American College of Cardiology,2014
3. Automated detection of atrial fibrillation in ECG signals based on wavelet packet transform and correlation function of random process
4. Photoplethysmography-Based Method for Automatic Detection of Premature Ventricular Contractions
5. Systolic Peak Detection in Acceleration Photoplethysmograms Measured from Emergency Responders in Tropical Conditions
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