Title: Support vector machine-based QRS-detection - evaluation on standard databases
Authors: Indu Saini; Dilbag Singh; Arun Khosla
Addresses: Department of Electronics and Communication Engineering, Dr. B.R. Ambedkar National Institute of Technology, Jalandhar-144011, Punjab, India. ' Department of Instrumentation and Control Engineering, Dr. B.R. Ambedkar National Institute of Technology, Jalandhar-144011, Punjab, India. ' Department of Electronics and Communication Engineering, Dr. B.R. Ambedkar National Institute of Technology, Jalandhar-144011, Punjab, India
Abstract: Detection of QRS-complex is an important issue in the analysis and interpretation of electrocardiogram (ECG) signals. In this work, a classifier motivated from statistical learning theory, i.e., support vector machine (SVM), has been explored for detection of QRS-complex. Here, a raw ECG signal is band-pass filtered to remove base line wander and power line interference. Further, gradient criterion was used to enhance the QRS-complexes. The performance of the algorithm was tested on MIT-BIH arrhythmia standard database. The numerical results indicated that the algorithm achieved 99.87% of detection rate. This algorithm performs better in comparison to other published works on the same database. Furthermore, the performance of this algorithm was also estimated on EUROBAVAR database and ECGs recorded using BIOPAC®MP100 system and using Atria®6100 ECG machine. The detection rates of 100%, 99.9% and 100% have been achieved for respective datasets. This demonstrates the superiority of SVM algorithm for QRS detection.
Keywords: classifiers; support vector machines; SVM; QRS complex; detection rate; gradient; informatics; electrocardiograms; ECG signals; arrhythmia.
International Journal of Medical Engineering and Informatics, 2012 Vol.4 No.3, pp.299 - 324
Received: 20 Feb 2012
Accepted: 20 Jun 2012
Published online: 11 Aug 2014 *