Analysis of first-derivative based QRS detection algorithms.
Journal Article (Journal Article)
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.
Full Text
Duke Authors
Cited Authors
- Arzeno, NM; Deng, Z-D; Poon, C-S
Published Date
- February 2008
Published In
Volume / Issue
- 55 / 2 Pt 1
Start / End Page
- 478 - 484
PubMed ID
- 18269982
Pubmed Central ID
- PMC2532677
Electronic International Standard Serial Number (EISSN)
- 1558-2531
International Standard Serial Number (ISSN)
- 0018-9294
Digital Object Identifier (DOI)
- 10.1109/tbme.2007.912658
Language
- eng