Probability density distribution of delta RR intervals: a novel method for the detection of atrial fibrillation

  • Yanjun Li
  • Xiaoying Tang
  • Ancong Wang
  • Hui Tang
Scientific Note


Atrial fibrillation (AF) monitoring and diagnosis require automatic AF detection methods. In this paper, a novel image-based AF detection method was proposed. The map was constructed by plotting changes of RR intervals (△RR) into grid panes. First, the map was divided into grid panes with 20 ms fixed resolution in y-axes and 15–60 s step length in x-axes. Next, the blank pane ratio (BPR), the entropy and the probability density distribution were processed using linear support-vector machine (LSVM) to classify AF and non-AF episodes. The performance was evaluated based on four public physiological databases. The Cohen’s Kappa coefficients were 0.87, 0.91 and 0.64 at 50 s step length for the long-term AF database, the MIT-BIH AF database and the MIT-BIH arrhythmia database, respectively. Best results were achieved as follows: (1) an accuracy of 93.7%, a sensitivity of 95.1%, a specificity of 92.0% and a positive predictive value (PPV) of 93.5% were obtained for the long-term AF database at 60 s step length. (2) An accuracy of 95.9%, a sensitivity of 95.3%, a specificity of 96.3% and a PPV of 94.1% were obtained for the MIT-BIH AF database at 40 s step length. (3) An accuracy of 90.6%, a sensitivity of 94.5%, a specificity of 90.0% and a PPV of 55.0% were achieved for the MIT-BIH arrhythmia database at 60 s step length. (4) Both accuracy and specificity were 96.0% for the MIT-BIH normal sinus rhythm database at 40 s step length. In conclusion, the intuitive grid map of delta RR intervals offers a new approach to achieving comparable performance with previously published AF detection methods.


Arrhythmia Atrial fibrillation (AF) Grid map Probability density distribution (PDD) Delta RR intervals (△RR) Atrial fibrillation database 



This study was funded by State Key Laboratory of Space Medicine Fundamentals and Application, China Astronaut Research and Training Center (SMFA15B06, SMFA15A01), and it was also funded by China National Natural Science Fund (81471743, 81601561, 61401417).

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this article.

Ethical approval

This article does not contain any studies with human or animal subjects performed by any of the authors.

Informed consent

All data used in this paper are from the open databases of the PhysioNet “”.


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Copyright information

© Australasian College of Physical Scientists and Engineers in Medicine 2017

Authors and Affiliations

  • Yanjun Li
    • 1
    • 2
  • Xiaoying Tang
    • 1
  • Ancong Wang
    • 1
  • Hui Tang
    • 3
  1. 1.School of Life ScienceBeijing Institute of TechnologyBeijingChina
  2. 2.State Key Laboratory of Space Medicine Fundamentals and ApplicationChina Astronaut Research and Training CenterBeijingChina
  3. 3.Hongfeng Control Company of the Sanjiang Space GroupXiaoganChina

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