Morphology-based detection of premature ventricular contractions

Hadia, Rohit; Guldenring, Daniel; Finlay, Dewar D.; Kennedy, Alan; Janjua, Ghalib; Bond, Raymond; McLaughlin, James

Abstract

Premature ventricular contraction (PVC) is the type of ectopic heartbeat, commonly found in the healthy population and is often considered benign. However, they are reported to adversely affect the accuracy of R-R variability based electrocardiographic (ECG) algorithms. This study proposes a Principal Component Analysis (PCA) based algorithmic approach to detect the PVCs based on their morphology. The eigenvectors were derived from signal window around the R-peak, where signal window for the PVC (wPVC) and that of NSR (wNSR) were set to 0.55 seconds and 0.16 seconds respectively. We used 24 ECG recordings from MIT BIH arrhythmia database as training dataset and the remaining 24 ECG recordings as testing dataset. Using the derived eigenvectors and the Linear regression (LR) analysis; complexes corresponding to the wNSR and wPVC were estimated from training and testing datasets. Four different classification methods were employed to differentiate between wPVS and wNSR, namely, Root mean squared error (RMSE), Pearson product-moment correlation coefficient comparision, Histogram probability distribution and k-Nearest Neighbour (KNN). All four methods were implemented individually to classify the wPVC and wNSR. The performance of each of the classification approach was evaluated by computing sensitivity and specificity. With the sensitivity of 93.45% and specificity of 93.14%, KNN based classification method has shown the best performance. The method proposed in this study allows for an effective differentiation between NSR beats and PVC beats.

Citation

HADIA, R., GULDENRING, D., FINLAY, D.D., KENNEDY, A., JANJUA, G., BOND, R. and MCLAUGHLIN, J. 2017. Morphology-based detection of premature ventricular contractions. In Proceedings of 2017 Computing in cardiology, 24-27 September 2017, Rennes, France. Piscataway: IEEE [online], 44, pages 1-4. Available from: https://doi.org/10.22489/CinC.2017.211-260

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2017 Computing in cardiology
• Start Date: Sep 24, 2017
• End Date: Sep 27, 2017
• Acceptance Date: Apr 15, 2017
• Online Publication Date: Sep 14, 2017
• Publication Date: Dec 31, 2017
• Deposit Date: Jun 27, 2022
• Publicly Available Date: Jun 27, 2022
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Volume: 44
• Pages: 1-4
• Series Title: Computing in cardiology
• Series ISSN: 2325-8861; 2325-887X
• Book Title: Proceedings of 2017 Computing in cardiology
• ISBN: 9781538666302
• DOI: https://doi.org/10.22489/CinC.2017.211-260
• Keywords: Electrocardiography; Principal component analysis; Heart rate variability; Morphology; Training; Testing; Correlation coefficient
• Public URL: https://rgu-repository.worktribe.com/output/1677537