An Algorithm For Microprocessor-Based QRS Detection


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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 Positives for a signal-to-noise ratio of 19 dB to 86.5 percent for a low SNR of 7 dB.

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