Abstract The QRS detection is a crucial step in ECG signal analysis; it has a great impact on the beats segmentation and in the final classification of the ECG signal. The Pan-Tompkins is one of the first and best-performing algorithms for QRS detection. It performs filtering for noise suppression, differentiation for slope dominance, and thresholding for decision making. All of the parameters of the Pan-Tompkins algorithm are selected empirically. However, we think that the Pan- Tompkins method can achieve better performance if the parameters were optimized. Therefore, we propose an adaptive algorithm that looks for the best set of parameters that improves the Pan-Tompkins algorithm performance. For this purpose, we formulate the parameter design as an optimization problem within a particle swarm optimization framework. Experiments conducted on the 24 hours recording of the MIT/BIH arrhythmia benchmark dataset achieved an overall accuracy of 99.83% which outperforms the state-of-the-art time-domain algorithms.

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[30] Zhang C. and BaeTae-Wuk., “VLSI Friendly ECG QRS Complex Detector for Body Sensor Networks,” EEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 2, no. 1, pp. 52-59, 2012. Mohamed Belkadi born on 1989 at Algiers, Algeria, he obtained his B.E in Computer Science in 2012 and M.S degree in 2014 from Medea University. Currently, he is a Ph.D. student at the University of Boumerdes, Algeria. His research interests include Digital Signal Processing, Biomedical Signal Processing, and Deep Learning. Abdelhamid Daamouche received the state engineer degree in Telecommunications from (INELEC), Boumerdes, Algeria, and the Magister degree from the University of Batna, and a Ph.D. degree in signal and image processing from the University of Boumerdes, in 2012. In 2003, he joined the University of Boumerdes as a Lecturer, and in 2019 became a Professor. His current research interests include pattern recognition, biomedical engineering, and remote sensing.