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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.