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 detectand recognize ECG arrhythmias. The problem with ECG analysis is the vast variations among morphologies of ECGsignals. Premature Ventricular Contractions (PVC) is a commontype of arrhythmia which may lead to critical situations and contains risk. This study, proposes a novel approach for detectingPVC 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 systemwas evaluated through accuracy, sensitivity and specificitymeasures. The best result shows that 98.97

Manifold Learning for ECG Arrhythmia Recognition Keywords:

Manifold Learning , Laplacian Eigenmaps , Electrocardiogra , Nonlinear Dimensionality Reduction Methods