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An algorithm for microprocessor-based QRS detection

Escalona, OJ, Passariello, G and Mora, F (1986) An algorithm for microprocessor-based QRS detection. Journal of Clinical Engineering, 11 (3). pp. 213-219. [Journal article]

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URL: http://journals.lww.com/jcejournal/Abstract/1986/05000/An_Algorithm_For_Microprocessor_Based_QRS.9.aspx

Abstract

QRS detection is fundamental for intensive care cardiac instrumentation, such as in arrhythmia recognition or ambulatory recording of cardiac events. Reliable detection of the QRS is necessary in order to obtain accurate measurements of the R-R intervals and QRS width. This paper describes a real-time algorithm for QRS detection based upon the theory of maxima and minima to locate peaks in the digitized ECG. A three-point sliding window is used to determine the presence of peaks and valleys on the signal. Analysis of the difference in amplitude and the time interval between consecutive peaks and valleys allows QRS detection. Thresholds used for detection are adapted according to changes in the amplitude and duration of the previous QRS complexes. Testing was performed with a normal ECG recording contaminated with different levels of added Gaussian noise, giving an average detection rate from 99.7 percent True Positive for a signal-to-noise ratio of 19 dB to 86.5 percent for a low SNR of 7 dB.QRS detection is fundamental for intensive care cardiac instrumentation, such as in arrhythmia recognition of ambulatory recording of cardiac events. This paper describes a real-time algorithm for QRS detection based upon the theory of maxima and minima to locate peaks in the digitized ECG. A three-point sliding window is used to determine the presence of peaks and valleys on the signal. Analysis of the difference in amplitude and the time interval between consecutive peaks and valleys allows QRS detection. Thresholds used for detection are adapted according to changes in the amplitude and duration of the previous QRS complexes. Testing was performed with a normal ECG recording contaminated with different levels of added Gaussian noise, giving an average detection rate from 99. 7 percent True Positives for a signal-to-noise ratio of 19 db to 86. 5 percent for a low snr of 7 db.

Item Type:Journal article
Keywords:ECG, ECG Processing, QRS Detection, Microcomputer Applications, Arrhythmia Analysis, Biomedical Signal Processing, Microcontrollers Fast Algorithms
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Engineering
Research Institutes and Groups:Engineering Research Institute
Engineering Research Institute > Nanotechnology & Integrated BioEngineering Centre (NIBEC)
ID Code:29980
Deposited By: Professor Omar Escalona
Deposited On:08 Feb 2016 14:06
Last Modified:08 Feb 2016 14:06

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