A novel technique for cardiac arrhythmia classification using spectral correlation and support vector machines

Cardiac disorders are one of the main causes leading to death. Therefore, they require continuous and efficient detection techniques. ECG is one of the main tools to diagnose cardiovascular disorders such as arrhythmias. Computer aided diagnosis (CAD) systems play a very important role in early detection and diagnosis of cardiac arrhythmias. In this work, we propose a CAD system for classifying five beat types including: normal (N), Premature Ventricular Contraction (PVC), Premature Atrial Contraction (APC), Left Bundle Branch Block (LBBB) and Right Bundle Branch Block (RBBB). The proposed system is based on cyclostationary signal analysis approach, which explores hidden periodicities in the signal of interest and thus it is able to detect hidden features. In order to study the cyclostationarity properties of the signal, we utilized the spectral correlation as a nonlinear statistical transformation inspecting the periodicity of the correlation. Three experiments were investigated in our study; raw spectral correlation data were used in the first experiment while the other two experiments utilized statistical features for the raw spectral data followed by principal component analysis (PCA) and fisher score for feature space reduction purposes respectively. As for the classification task, support vector machine (SVM) with linear kernel was employed for all experiments. The experimental results showed that the approach that uses the raw spectral correlation data is superior compared to several state of the art methods. This approach achieved sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) of 99.20%, 99.70%, 98.60%, 99.90% and 97.60% respectively. (C) 2015 Elsevier Ltd. All rights reserved.