The K-Nearest Neighbor algorithm with K=3 and Euclidean distance achieved QRS detection rates of 99.89% and 99.81% on the CSE and MIT-BIH Arrhythmia databases, respectively.
The proposed KNN algorithm provides highly accurate and reliable QRS-complex detection in 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.
Saini et al. (Sat,) conducted a other in Arrhythmia / ECG analysis (n=173). K-Nearest Neighbor (KNN) algorithm vs. Other published QRS detection algorithms was evaluated on QRS detection rate. The K-Nearest Neighbor algorithm with K=3 and Euclidean distance achieved QRS detection rates of 99.89% and 99.81% on the CSE and MIT-BIH Arrhythmia databases, respectively.