Fully Automatic Detection of Premature Ventricular Contractions: A New Approach Based On Unsupervised Learning

Premature Ventricular Contractions (PVCs), a common type of cardiac arrhythmia, can be identified by analyzing electrocardiogram (ECG) signals. If not treated on time, PVCs become life-threatening. In this paper, a high-performance approach is proposed for detecting PVCs in an unsupervised manner. The main objective is to perform an automatic PVCs detection in ECG without prior knowledge. Ten different statistical features are extracted to represent various characteristics of the signal. Thereafter, the proposed approach explores PVCs detection by two different strategies. Performance evaluation results over the MIT-BIH Arrhythmia Database (MIT-BIH-AD) show that the strategy based on Agglomerative Hierarchical Clustering (AHC) Method outperforms K-means Clustering Method with an average Accuracy (ACC), Specificity (SPE), Sensitivity (SEN), and Positive Predictive Value (PPV) of 98.43%, 99.23%, 94.47%, and 96.67%, respectively. With less complexity and computation load, AHC can be an accurate candidate for PVCs detection to be used in clinical applications.