Title: Premature ventricular contraction detection using swarm-based support vector machine and QRS wave features

Authors: Nuryani Nuryani; Iwan Yahya; Anik Lestari

Addresses: Department of Physics, University of Sebelas Maret, Surakarta 57126, Indonesia ' Department of Physics, University of Sebelas Maret, Surakarta 57126, Indonesia ' Faculty of Medicine, University of Sebelas Maret, Surakarta 57126, Indonesia

Abstract: A novel strategy for detecting Premature Ventricular Contraction (PVC) is proposed and investigated. The strategy employs a Swarm-based Support Vector Machine (SSVM). An SSVM is an SVM optimised by using Particle Swarm Optimisation (PSO). The strategy proposes new inputs. The inputs involve the width and the gradient of the electrocardiographic QRS wave. Experiments with different inputs and different SVM kernel functions are conducted to find the best one for PVC detection. On a test using clinical data, SSVM performs well in PVC detection with sensitivity, specificity and accuracy of 98.94%, 99.99% and 99.46%, respectively.

Keywords: PVC detection; premature ventricular contraction; electrocardiograms; ECG signals; PSO; particle swarm optimisation; SVM; support vector machines; QRS wave features; premature heartbeat; heart diseases.

DOI: 10.1504/IJBET.2014.066224

International Journal of Biomedical Engineering and Technology, 2014 Vol.16 No.4, pp.306 - 316

Received: 07 May 2014
Accepted: 18 Aug 2014
Published online: 25 Apr 2015 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article