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A simple real-time QRS detection algorithm utilizing curve-length concept with combined adaptive threshold for electrocardiogram signal classification

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4 Author(s)
Jacek Lewandowski ; Fac. of Eng. & Comput., Coventry Univ., Coventry, UK ; Hisbel E. Arochena ; Raouf N. G. Naguib ; Kuo-Ming Chao

QRS detection is a standard procedure in electrocardiogram (ECG) signal classification and analysis. Although there is a large number of methods published, some featuring high accuracy, the problem remains open. This is especially true with respect to high accuracy QRS detection in noisy ECGs such as long-term Holter monitoring during normal daily activity. In this paper a robust real-time QRS detector for noisy applications is proposed. It exploits a modified curve-length concept with combined adaptive threshold derived by basic mean, standard deviation and average peak-to-peak interval. The method was tested using the MIT-BIH arrhythmia database with an observed detection accuracy of 99.70%, sensitivity of 99.86%, positive prediction of 99.84%, and an average failed detection of 0.30%. The proposed approach compares favourably with published results for other QRS detectors, and proves superior to those having constant and manually entered threshold parameters.

Published in:

TENCON 2012 - 2012 IEEE Region 10 Conference

Date of Conference:

19-22 Nov. 2012