DocumentCode :
3258458
Title :
Delineation of ECG Wave Components Using K-Nearest Neighbor (KNN) Algorithm: ECG Wave Delineation Using KNN
Author :
Saini, Indu ; Singh, D. ; Khosla, Aditya
Author_Institution :
Dept. of Electron. & Commun. Eng., Dr B R Ambedkar Nat. Inst. of Technol. Jalandhar, Jalandhar, India
fYear :
2013
fDate :
15-17 April 2013
Firstpage :
712
Lastpage :
717
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®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.
Keywords :
electrocardiography; medical signal detection; medical signal processing; signal classification; statistical analysis; ATRIA6100 ECG machine; ECG signal detection; ECG wave component delineation; ECG wave fiducial points; K-nearest neighbor algorithm; KNN algorithm; P-wave duration; PR-interval; QRS-complex; QT-interval; electrocardiogram characteristic waveform boundary; heart rate; offset points; onset points; software based detector; statistical pattern recognition algorithm; Accuracy; Classification algorithms; Electrocardiography; Heart rate; Measurement; Software algorithms; Training; ECG; KNN; classifier; durations; gradient; intervals;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Technology: New Generations (ITNG), 2013 Tenth International Conference on
Conference_Location :
Las Vegas, NV
Print_ISBN :
978-0-7695-4967-5
Type :
conf
DOI :
10.1109/ITNG.2013.76
Filename :
6614392
Link To Document :
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