Manifold learning for ECG arrhythmia recognition

Heart is a complex system and we can find its function in electrocardiogram (ECG) signal. The records show high mortality rate of heart diseases. So it is essential to detect and recognize ECG arrhythmias. The problem with ECG analysis is the vast variations among morphologies of ECG signals. Premature Ventricular Contractions (PVC) is a common type of arrhythmia which may lead to critical situations and contains risk. This study, proposes a novel approach for detecting PVC and visualizing data with respect to ECG morphologies by using manifold learning. To this end, the Laplacian Eigenmaps - One of the reduction method and it is in the nonlinear category - is used to extract important dimensions of the ECG signals, followed by the application of Bayesian and FLDA methods for classifying the ECG data. The recognition performance of system was evaluated through accuracy, sensitivity and specificity measures. The best result shows that 98.97 ± 0.99 in sensitivity and 99.95 ± 0.01 in specificity with 98.85 ± 0.90 accuracy. These Results show that this method is able to predict and appropriately diagnose ECG arrhythmia.