Title: Detection of QRS-complex using K-nearest neighbour algorithm

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: The automatic detection of ECG wave is important for cardiac disease diagnosis. A good performance of an automatic ECG analysing system depends upon the accurate and reliable detection of the QRS complex. This paper presents an application of K-nearest neighbour (KNN) algorithm for detection of QRS-complex in ECG. Here, the ECG signal was filtered using a band-pass filter to remove power line interference and baseline wander and gradient of the signal was used as a feature for QRS detection. The accuracy of KNN algorithm is largely dependent on the value of K and type of distance metric. Hence, K = 3 and Euclidean distance metric has been proposed, using five-fold cross-validation. The performance of this algorithm was evaluated on EUROBAVAR database and ECGs recorded using BIOPAC®MP100 system and using Atria®6100 ECG machine. The detection rates of 100%, 99.97% and 100% have been achieved for respective datasets. These results emphasises that KNN is a useful tool for QRS detection.

Keywords: classifier; K-nearest neighbour; KNN; QRS complex; detection rate; cross-validation; gradient; electrocardiograms; ECG wave detection; ECG signals; cardiac disease diagnosis; cardiovascular disease; cornonary heart disease; QRS detection.

DOI: 10.1504/IJMEI.2013.051668

International Journal of Medical Engineering and Informatics, 2013 Vol.5 No.1, pp.81 - 101

Received: 08 Oct 2012
Accepted: 19 Nov 2012
Published online: 28 Jan 2014 *

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