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Henry Castro
Universidad Santiago de Cali and Universidad del Valle
Computer Science & IT Electrical & Electronics Engineering

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Methodology for detection of paroxysmal atrial fibrillation based on P-Wave, HRV and QR electrical alternans features Henry Castro; Juan David Garcia-Racines; Alvaro Bernal-Noreña
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (988.688 KB) | DOI: 10.11591/ijece.v10i4.pp4023-4034

Abstract

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.
Co-Authors Alvaro Bernal-Noreña Juan David Garcia-Racines