Identification of Atrial Fibrillation Using Complex Network Similarity

Yajuan Zhang, Yuanyuan Wang

National Key Laboratory of ASIC and System, Fudan University, Shanghai, China Department of Electronic Engineering, Fudan University, Shanghai, China.

DOI: 10.4236/eng.2013.510B005                

Abstract

We investigate the use of complex network similarity for the identification of atrial fibrillation. The similarity of the network is estimated via the joint recurrence plot and Hamming distance. Firstly, we transform multi-electrodes epicardium signals recorded from dogs into the recurrence complex network. Then, we extract features representing its similarity. Finally, epicardium signals are classified utilizing the classification and regression tree with extracted features. The method is validated using 1000 samples including 500 atrial fibrillation cases and 500 normal sinus ones. The sensitivity, specificity and accuracy of the identification are 98.2%, 98.8% and 98.5% respectively. This experiment indicates that our approach may lay a foundation for the prediction of the onset of atrial fibrillation.

Keywords

Recurrence Complex Network; Phase Space Reconstruction; Classification and Regression Tree; Atrial Fibrillation

Conflicts of Interest

The authors declare no conflicts of interest.

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