Detection of QRS-complex using K-nearest neighbour algorithm Online publication date: Tue, 28-Jan-2014
by Indu Saini; Dilbag Singh; Arun Khosla
International Journal of Medical Engineering and Informatics (IJMEI), Vol. 5, No. 1, 2013
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.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Medical Engineering and Informatics (IJMEI):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook