QRS detection using K-Nearest Neighbor algorithm (KNN) and evaluation on standard ECG databases

The performance of computer aided ecg analysis depends on the precise and accurate delineation of qrs-complexes. this paper presents an application of k-nearest neighbor (knn) algorithm as a classifier for detection of qrs-complex in ecg. the proposed algorithm is evaluated on two manually annotated standard databases such as cse and mit-bih arrhythmia database. in this work, a digital band-pass filter is used to reduce false detection caused by interference present in ecg signal and further gradient of the signal is used as a feature for qrs-detection. in addition the accuracy of knn based classifier is largely dependent on the value of k and type of distance metric. the value of k = 3 and euclidean distance metric has been proposed for the knn classifier, using fivefold cross-validation. the detection rates of 99.89% and 99.81% are achieved for cse and mit-bih databases respectively. the qrs detector obtained a sensitivity se = 99.86% and specificity sp= 99.86% for cse database, and se = 99.81% and sp= 99.86% for mit-bih arrhythmia database. a comparison is also made between proposed algorithm and other published work using cse and mit-bih arrhythmia databases. these results clearly establishes knn algorithm for reliable and accurate qrs-detection.