Analyzing single-lead short ECG recordings using dense convolutional neural networks and feature-based post-processing to detect atrial fibrillation.

Journal: Physiological measurement
Published Date: Aug 23, 2018

Abstract

OBJECTIVE: The prevalence of atrial fibrillation (AF) in the general population is 0.5%-1%. As AF is the most common sustained cardiac arrhythmia that is associated with an increased morbidity and mortality, its timely diagnosis is clinically desirable. The main aim of this study as our contribution to the PhysioNet/CinC Challenge 2017 was to develop an automatic algorithm for classification of normal sinus rhythm (NSR), AF, other rhythm (O), and noise using a short single-channel ECG. Furthermore, the impact of changing labels/annotations on performance of the proposed algorithm was studied in this article.

Authors

  • Saman Parvaneh
    Philips Research North America, Cambridge, MA, USA.
  • Jonathan Rubin
    Philips Research North America, Cambridge, MA, United States. Electronic address: Jonathan.Rubin@philips.com.
  • Asif Rahman
    Philips Research North America, Cambridge, MA, United States.
  • Bryan Conroy
    Philips Research North America, Cambridge, MA, USA.
  • Saeed Babaeizadeh
    Advanced Algorithm Research Center, Philips Healthcare, Andover, MA, USA.

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