Atrial Fibrillation (A-Fib), Atrial Flutter (AFL) and Ventricular Fibrillation (V-Fib) are fatal cardiac abnormalities commonly affecting people in advanced age and have indication of life-threatening condition. To detect these abnormal rhythms, Electrocardiogram (ECG) signal is most commonly visualized as a significant clinical tool. Concealed non-linearities in the ECG signal can be clearly unraveled using Recurrence Quantification Analysis (RQA) technique. In this paper, RQA features are applied for classifying four classes of ECG beats namely Normal Sinus Rhythm (NSR), A-Fib, AFL and V-Fib using ensemble classifiers. The clinically significant (p<0.05) features are ranked and fed independently to three classifiers viz. Decision Tree (DT), Random Forest (RAF) and Rotation Forest (ROF) ensemble methods to select the best classifier. The training and testing of the feature set is accomplished using 10-fold cross-validation strategy. The RQA coefficients using ROF provided an overall accuracy of 98.37% against 96.29% and 94.14% for the RAF and DT, respectively. The results achieved evidently ratify the superiority of ROF ensemble classifier in the diagnosis of A-Fib, AFL and V-Fib. Precision of four classes is measured using class-specific accuracy (%) and reliability of the performance is assessed using Cohen's kappa statistic (K). The developed approach can be used in therapeutic devices and help the physicians in automatic monitoring of fatal tachycardia rhythms.
All Science Journal Classification (ASJC) codes
- Biomedical Engineering