Atrial fibrillation detection with multiparametric RR interval feature and machine learning technique

Islam S., Ammour N., Alajlan N.

2017 International Conference on Informatics, Health and Technology, ICIHT 2017, Riyadh, Saudi Arabia, 21 - 23 February 2017 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/iciht.2017.7899003
  • City: Riyadh
  • Country: Saudi Arabia
  • Keywords: atrial fibrillation, automatic screening, decision boundary, RR interval feature, support vector machine
  • TED University Affiliated: No


Automatic screening of atrial fibrillation (AF) in the out-of-clinic environment is potentially an effective method for early detection of this life-threatening arrhythmia which is often paroxysmal and asymptomatic. Different technologies such as modified blood pressure monitor, single lead ECG-based finger-probe, and smartphone using plethysmogram signal have been emerging for this purpose. All these technologies use irregularity of RR interval (RRI) as a feature for AF detection. For real-time applications scalar feature is extracted from RRI signal and classified with a threshold. In this work, we have introduced multi-parametric RRI feature yielding a multidimensional feature vector. We used machine learning technique to learn the optimal decision boundary. The proposed method was tested with a publicly available landmark database. Initial experiments show promising AF detection performances comparable to those of state-of-the-art methods. Development and implementation of such a method in existing screen devices such a smartphone could be important for prevention of AFrelated risk of stroke, dementia, and death.