DocumentCode :
1038391
Title :
Analysis of First-Derivative Based QRS Detection Algorithms
Author :
Arzeno, Natalia M. ; Deng, Zhi-De ; Poon, Chi-Sang
Author_Institution :
Massachusetts Inst. of Technol., Cambridge
Volume :
55
Issue :
2
fYear :
2008
Firstpage :
478
Lastpage :
484
Abstract :
Accurate QRS detection is an important first step for the analysis of heart rate variability. Algorithms based on the differentiated ECG are computationally efficient and hence ideal for real-time analysis of large datasets. Here, we analyze traditional first-derivative based squaring function (Hamilton-Tompkins) and Hilbert transform-based methods for QRS detection and their modifications with improved detection thresholds. On a standard ECG dataset, the Hamilton-Tompkins algorithm had the highest detection accuracy (99.68% sensitivity, 99.63% positive predictivity) but also the largest time error. The modified Hamilton-Tompkins algorithm as well as the Hilbert transform-based algorithms had comparable, though slightly lower, accuracy; yet these automated algorithms present an advantage for real-time applications by avoiding human intervention in threshold determination. The high accuracy of the Hilbert transform-based method compared to detection with the second derivative of the ECG is ascribable to its inherently uniform magnitude spectrum. For all algorithms, detection errors occurred mainly in beats with decreased signal slope, such as wide arrhythmic beats or attenuated beats. For best performance, a combination of the squaring function and Hilbert transform-based algorithms can be applied such that differences in detection will point to abnormalities in the signal that can be further analyzed.
Keywords :
Hilbert transforms; electrocardiography; medical signal detection; ECG; Hamilton-Tompkins algorithm; Hilbert transform; arrhythmic beat; automatic QRS complex detection; electrocardiography; first-derivative based squaring function; heart rate variability analysis; peak detection; real-time analysis; signal slope; Algorithm design and analysis; Data analysis; Data mining; Detection algorithms; Electrocardiography; Heart rate detection; Heart rate variability; Humans; Performance analysis; Signal analysis; Electrocardiography; Hilbert transform; heart rate variability; peak detection; Algorithms; Arrhythmias, Cardiac; Diagnosis, Computer-Assisted; Electrocardiography; Humans; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity;
fLanguage :
English
Journal_Title :
Biomedical Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9294
Type :
jour
DOI :
10.1109/TBME.2007.912658
Filename :
4432721
Link To Document :
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