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.
['Indu Saini', 'Dilbag Singh', 'Arun Khosla']
Detection of QRS-complex using K-nearest neighbour algorithm