title: QRS detection using K-Nearest Neighbor algorithm (KNN) and evaluation on standard ECG databases.
abstract: 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.
authors: Department of Electronics and Communication Engineering, Dr. B.R. Ambedkar National Institute of Technology Jalandhar, Jalandhar 144 011, India.
authors: Indu
authors: I
authors: Saini
authors: Department of Instrumentation and Control Engineering, Dr. B.R. Ambedkar National Institute of Technology Jalandhar, Jalandhar 144 011, India.
authors: Dilbag
authors: D
authors: Singh
authors: Department of Electronics and Communication Engineering, Dr. B.R. Ambedkar National Institute of Technology Jalandhar, Jalandhar 144 011, India.
authors: Arun
authors: A
authors: Khosla
keywords: Classifier; Cross-validation; ECG; Gradient; KNN; QRS detection