Advances in ECG Signal Processing: Improved R-Peak Detection and Denoising Techniques for Accurate Cardiac Diagnosis


  • Luis Bernal
  • Diego H. Stalder
  • Félix Morales-Mareco


The analysis of electrocardiograms (ECGs) is critical for diagnosing various cardiac diseases, which are the leading cause of mortality in developed countries. The significant points of the ECG, which consist of characteristic wave peaks and boundaries, contain essential information about intervals and amplitudes that are clinically relevant. It is, therefore, crucial to continuously test and improve the accuracy and robustness of techniques used for automatically delineating ECGs, particularly when analyzing extended recordings. To address this need for ongoing improvement, there are now open-source tools available, such as Neurokit [1]. These tools can help researchers and clinicians to evaluate and refine their techniques for automatically analyzing ECGs, which ultimately leads to more accurate diagnoses and better patient outcomes. Therefore, the development and utilization of such tools are crucial for advancing the field of ECG analysis and improving the diagnosis and treatment of cardiac diseases [2]. [...]


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Biografia do Autor

Luis Bernal

School of Engineering, National University of Asuncion, Paraguay

Diego H. Stalder

School of Engineering, National University of Asuncion, Paraguay

Félix Morales-Mareco

School of Engineering, National University of Asuncion, Paraguay


Dominique Makowski, Tam Pham, Zen J. Lau, Jan C. Brammer, François Lespinasse, Hung Pham, Christopher Schölzel, and S. H. Annabel Chen. “NeuroKit2: A Python toolbox for neurophysiological signal processing”. In: Behavior Research Methods 53.4 (Feb. 2021), pp. 1689–1696. doi: 10.3758/s13428-020-01516-y.

J.P. Martinez, R. Almeida, S. Olmos, A.P. Rocha, and P. Laguna. “A wavelet-based ECG delineator: evaluation on standard databases”. In: IEEE Transactions on Biomedical Engineering 51.4 (2004), pp. 570–581. doi: 10.1109/TBME.2003.821031.

M. Rakshit, D. Panigrahy, and P. K. Sahu. “An improved method for R-peak detection by using Shannon energy envelope”. In: Sādhanā 41.5 (May 2016), pp. 469–477. issn: 0973-7677. doi: 10.1007/s12046-016-0485-8. url:

Fars Samann and Thomas Schanze. In: Current Directions in Biomedical Engineering 5.1 (2019), pp. 385–387. doi: doi:10.1515/cdbme-2019-0097.