Real-time premature ventricular contractions detection based on Redundant Discrete Wavelet Transform

Abstract

Introduction: Premature Ventricular Contraction (PVC) is among the most common types of ventricular cardiac arrhythmia. However, it only poses danger if the person suffers from a heart disease, such as heart failure. Hence, this is an important factor to consider in heart disease people. This paper presents an ECG real-time analysis system for PVC detection. Methods: This system is based on threshold adaptive methods and Redundant Discrete Wavelet Transform (RDWT), with a real-time approach. This analysis is based on wavelet coefficients energy for PVC detection. It is presented also a study to find the most indicated wavelet mother for ECG analysis application among the following wavelet families: Daubechies, Coiflets and Symlets. The system detection performance was validated on the MIT-BIH Arrhythmia Database. Results: The best results were verified with db2 wavelet mother: the Sensitivity Se = 99.18%, Positive Predictive Value P+ = 99.15% and Specificity Sp = 99.94%, on 80.872 annotated beats, and 61.2 s processing speed for a half-hour record. Conclusion: The proposed system exhibits reliable PVC detection, with real-time approach, and a simple algorithmic structure that can be implemented in many platforms.

Searching for Premature Ventricular Contraction from Electrocardiogram by Using One-Dimensional Convolutional Neural Network

“…Junior et al developed a system based on threshold adaptive algorithm and wavelet transform for PVC detection. The result validated on the MIT-BIH arrhythmia database reported that Daubechies 2 wavelet mother is more indicated compared with Coiflets and Symlets [10]. Oliveira et al proposed a simplified set of features extracted from geometric figures constructed over QRS complexes and selected the most suitable classifiers based on the analytic hierarchy process (AHP).…”