An automated noise-robust premature ventricular contraction (PVC) detection method is proposed based on the sparse signal decomposition, temporal features, and decision rules. In this Letter, the authors exploit sparse expansion of electrocardiogram (ECG) signals on mixed dictionaries for simultaneously enhancing the QRS complex and reducing the influence of tall P and T waves, baseline wanders, and muscle artefacts. They further investigate a set of ten generalised temporal features combined with decision-rule-based detection algorithm for discriminating PVC beats from non-PVC beats. The accuracy and robustness of the proposed method is evaluated using 47 ECG recordings from the MIT/BIH arrhythmia database. Evaluation results show that the proposed method achieves an average sensitivity of 89.69%, and specificity 99.63%. Results further show that the proposed decision-rule-based algorithm with ten generalised features can accurately detect different patterns of PVC beats (uniform and multiform, couplets, triplets, and ventricular tachycardia) in presence of other normal and abnormal heartbeats.
Keywords: ECG signal sparse expansion; MIT-BIH arrhythmia database; P waves; QRS complex; T waves; automated PVC detection; baseline wanders; decision rule based detection algorithm; decision rules; decision trees; electrocardiogram; electrocardiography; knowledge based systems; medical signal detection; medical signal processing; mixed dictionaries; muscle artefacts; noise robust PVC detection; premature ventricular contraction; sparse signal decomposition; temporal features.