A Robust QRS Complex Detection Algorithm Using Dynamic Thresholds

Automatic QRS complex detection is important in ECG signal analysis. QRS detection methods are affected by the quality of the ECG recordings and the abnormalities in the ECG signals. In this paper, a generic algorithm is introduced to improve the detection of QRS complexes in arrhythmia ECG signals that suffer from: (1) non-stationary effects, (2) low signal-to-noise ratio, (3) negative QRS polarities, (4) low QRS amplitudes, and (5) ventricular ectopics. We compared the algorithm to the method described by Chouhan et al. [16] by applying both algorithms to 19 records of the MIT-BIH database. It was shown that the new algorithm achieves significantly better detection rates resulting in an overall 97.5% sensitivity and 99.9% positive predictivity.