Key result
A novel algorithm based on recurrence plot feature extraction and a multilayer perceptron neural network correctly classified 97% and 95% of AF termination events in two testing sets.
Why the study?
Can a novel algorithm based on recurrence plot quantification of ECG signals accurately predict the spontaneous termination of atrial fibrillation?
Can a novel algorithm based on recurrence plot quantification of ECG signals accurately predict the spontaneous termination of atrial fibrillation?
A novel machine learning algorithm based on ECG recurrence plots can accurately predict the spontaneous termination of atrial fibrillation.
May aid noninvasive AF termination prediction; leaves open prospective clinical validation before practice adoption.
Predicting the spontaneous termination of the atrial fibrillation (AF) leads to not only better understanding of mechanisms of the arrhythmia but also the improved treatment of the sustained AF. A novel method is proposed to characterize the AF based on structure and the quantification of the recurrence plot (RP) to predict the termination of the AF. The RP of the electrocardiogram (ECG) signal is firstly obtained and eleven features are extracted to characterize its three basic patterns. Then the sequential forward search (SFS) algorithm and Davies-Bouldin criterion are utilized to select the feature subset which can predict the AF termination effectively. Finally, the multilayer perceptron (MLP) neural network is applied to predict the AF termination. An AF database which includes one training set and two testing sets (A and B) of Holter ECG recordings is studied. Experiment results show that 97% of testing set A and 95% of testing set B are correctly classified. It demonstrates that this algorithm has the ability to predict the spontaneous termination of the AF effectively.
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Sun et al. (2008) studied Atrial fibrillation. Recurrence plot (RP) based feature extraction and multilayer perceptron (MLP) neural network was evaluated on Correct classification of spontaneous termination of atrial fibrillation. A novel algorithm based on recurrence plot feature extraction and a multilayer perceptron neural network correctly classified 97% and 95% of AF termination events in two testing sets.
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