Atrial Fibrillation Discrimination for Real-Time ECG Monitoring Based On QT Interval Variation

Background/Objectives: An occasional Atrial Fibrillation (AF) event in heart rhythm should be monitored regularly, in continuous intervals. Timely detection of these anomalies in heart rhythm is required to save patients from sudden cardiac arrest. Method: A long-duration ECG categorization algorithm named AFECOC is proposed. For this one-minute-long 71 signals are attained from the Physionet’s “MIT-BIH arrhythmia (MA)” and “AF” database. Two-stage filtering of noisy signals is employed before signal analysis. Four algorithms i.e. Error-Correcting Output Code (ECOC), Naïve Bayes, Decision Tree, and K-Nearest Neighbor(K-NN) are applied to reduce the feature set and then signals are classified with ECOC classifier. Findings: It was found that the ECOC algorithm gives the highest accuracy of 81.95% on the complete feature set. To exclude the irrelevant features, the highest performing algorithm ECOC was used that extracts the combination of the feature sets that get most affected during AF. The combination of ’heart-beat’ and ’mean QT-interval’ are found to be the most relevant features affected during AF events. The accuracy of these two features was evaluated with four classifiers namely ECOC, Naïve Bayes, Decision tree-based and K mean classifier and the accuracy obtained was 89.6%, 76.19%, 76.19%, and 61% respectively. It concludes that the proposed methodology achieved the highest accuracy of 89.6% with the ECOC classifier. Finally, all the AF rhythms have been checked using annotated labels for spontaneous change in QT-interval to verify the designed methodologies. Novelty: Instead of missing P-waves and RR-interval variation, recognition of mean QT interval variation-based AF event detection algorithm gives better accuracy for longer signals. Hence, it can be implemented in Real- Time continuous monitoring.