Key result
A feedforward neural network utilizing P-Wave, HRV, and QR electrical alternans features detected paroxysmal atrial fibrillation with an average accuracy of 97.4%.
Why the study?
Manual detection of paroxysmal atrial fibrillation on ECG is complex, motivating automated computational detection techniques.
Does an automated detection methodology based on P-Wave, HRV, and QR electrical alternans features improve the accuracy of detecting paroxysmal atrial fibrillation in ECG signals?
Does an automated detection methodology based on P-Wave, HRV, and QR electrical alternans features improve the accuracy of detecting paroxysmal atrial fibrillation in ECG signals?
An automated detection algorithm incorporating P-Wave morphology, HRV, and QR electrical alternans achieved 97.4% accuracy in identifying paroxysmal atrial fibrillation from ECG signals.
Promising for automated paroxysmal AF detection algorithms; leaves open prospective clinical validation before practice adoption.
The detection of Paroxysmal Atrial Fibrillation (PAF) is a fairly complex process performed manually by cardiologists or electrophysiologists by reading an electrocardiogram (ECG). Currently, computational techniques for automatic detection based on fast Fourier transform (FFT), Bayes optimal classifier (BOC), k-nearest neighbors (K-NNs), and artificial neural network (ANN) have been proposed. In this study, six features were obtained based on the morphology of the P-Wave, the QRS complex and the heart rate variability (HRV) of the ECG. The performance of this methodology was validated using clinical ECG signals from the Physionet arrhythmia database MIT-BIH. A feedforward neural network was used to detect the presence of PAF reaching a general accuracy of 97.4%. The results obtained show that the inclusion of the information of the P-Wave, HRV and QR Electrical alternans increases the accuracy to identify the PAF event compared to other works that use the information of only one or at most two of them.
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Castro et al. (2020) studied Paroxysmal atrial fibrillation (n=99,002). Feedforward neural network using P-Wave, HRV, and QR electrical alternans features was evaluated on Accuracy of PAF detection. A feedforward neural network utilizing P-Wave, HRV, and QR electrical alternans features detected paroxysmal atrial fibrillation with an average accuracy of 97.4%.
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