Paper Info
Title
Delineation of ECG Wave Components Using K-Nearest Neighbor (KNN) Algorithm: ECG Wave Delineation Using KNN
Abstract
Detection of the boundaries of electrocardiogram (ECG) characteristic waves with a reasonable accuracy has been a difficult task. As a classical statistical pattern recognition algorithm characterized with high accuracy and stability, KNN has been proposed for locating the waveform boundaries (the onsets and offsets of P, QRS, and T waves) in ECG signals. First, the QRS-complex of each beat is detected from the ECG signal. Next, the onset and offset of each QRS complex are located. The P wave and T wave, relative to each QRS complex along with their onset and offset points, are then identified using this algorithm. Further, QRS duration, heart rate, QT-interval, P-wave duration and PR-interval have also been computed using ECG wave fiducial points. This algorithm is tested on the ECG dataset acquired using ATRIA庐6100 ECG machine in our own laboratory. The results obtained using the proposed algorithm presented for the assessment of performance, has been compared with the output of inbuilt software based detector of ATRIA machine.
Year
DOI
Venue
2013
10.1109/ITNG.2013.76
Information Technology: New Generations
Keywords
Field
DocType
characteristic wave,ecg signal,p wave,ecg wave delineation,proposed algorithm,k-nearest neighbor,qrs duration,ecg wave components,ecg machine,atria machine,ecg dataset,qrs complex,ecg wave fiducial point,statistical analysis
k-nearest neighbors algorithm,Pattern recognition,T wave,Computer science,Waveform,QRS complex,Artificial intelligence,Electrocardiography,Classifier (linguistics),Detector,Offset (computer science)
Conference
ISBN
Citations 
PageRank 
978-0-7695-4967-5
0
0.34
References 
Authors
7
3
Name
Order
Citations
PageRank
Indu Saini1114.90
Dilbag Singh26715.16
Arun Khosla3436.56