Detection of premature ventricular contraction (PVC) using linear and nonlinear techniques: an experimental study

Cardiovascular diseases are identified as one of the most dangerous diseases and the major causes of the death around the world. One of the most common cardiac arrhythmias that have always been a concern for cardiologists is premature ventricular contractions. Regarding its abundance among all ages, prediction and diagnosis of this type of arrhythmia has particular importance. One of the most common, most non-invasive; and the least costly method for investigation of heart diseases is to record and analyze the electrocardiogram (ECG) signals. The purpose of this study is to analyze the ECG in order to classify premature ventricular contraction heartbeats. Having proposed a new technique based on evolutionary optimization for R peak detection, several methodologies, such as morphological assessment, polynomial curve fitting, discrete wavelet transform, and nonlinear analysis, are employed to extract features from ECG signal. Support vector machine (SVM) classifier with a linear kernel is used to detect the normal and PVC heart rates. In order to evaluate the proposed method, in addition to the MIT-BIH database, the experimental data is used and the methodology performance is proved for both databases. Finally, using different feature selection criteria such as fisher distinction, minimal-redundancy maximum-relevance, and SVM-based recursive feature elimination with correlation bias reduction, six features are introduced as best ones. The proposed PVC detection algorithm acquires the overall detection accuracy of 99.78%, with the sensitivity of 99.91% and specificity of 99.37%, for MIT-BIH dataset.