QRS detection using S-Transform and Shannon energy

Z Zidelmal; A Amirou; D Ould-Abdeslam; A Moukadem; A Dieterlen

doi:10.1016/j.cmpb.2014.04.008

QRS detection using S-Transform and Shannon energy

Comput Methods Programs Biomed. 2014 Aug;116(1):1-9. doi: 10.1016/j.cmpb.2014.04.008. Epub 2014 May 2.

Authors

Z Zidelmal¹, A Amirou², D Ould-Abdeslam³, A Moukadem⁴, A Dieterlen⁵

Affiliations

¹ Mouloud Mammeri University, Tizi-Ouzou, Algeria. Electronic address: z-zidelmal@mail.ummto.dz.
² Mouloud Mammeri University, Tizi-Ouzou, Algeria. Electronic address: a-amirou@mail.ummto.dz.
³ MIPS Laboratory, University of Haute Alsace, 68093 Mulhouse Cedex, France. Electronic address: djaffar.ould-abdeslam@uha.fr.
⁴ MIPS Laboratory, University of Haute Alsace, 68093 Mulhouse Cedex, France. Electronic address: ali.moukadem@uha.fr.
⁵ MIPS Laboratory, University of Haute Alsace, 68093 Mulhouse Cedex, France. Electronic address: alain.dieterlen@uha.fr.

PMID: 24856322
DOI: 10.1016/j.cmpb.2014.04.008

Abstract

This paper presents a novel method for QRS detection in electrocardiograms (ECG). It is based on the S-Transform, a new time frequency representation (TFR). The S-Transform provides frequency-dependent resolution while maintaining a direct relationship with the Fourier spectrum. We exploit the advantages of the S-Transform to isolate the QRS complexes in the time-frequency domain. Shannon energy of each obtained local spectrum is then computed in order to localize the R waves in the time domain. Significant performance enhancement is confirmed when the proposed approach is tested with the MIT-BIH arrhythmia database (MITDB). The obtained results show a sensitivity of 99.84%, a positive predictivity of 99.91% and an error rate of 0.25%. Furthermore, to be more convincing, the authors illustrated the detection parameters in the case of certain ECG segments with complicated patterns.

Keywords: QRS detection; S-Transform; Shannon energy.

MeSH terms

Algorithms*
Arrhythmias, Cardiac / diagnosis*
Arrhythmias, Cardiac / physiopathology*
Diagnosis, Computer-Assisted / methods*
Electrocardiography / methods*
Heart Rate*
Humans
Pattern Recognition, Automated / methods*
Reproducibility of Results
Sensitivity and Specificity
Signal Processing, Computer-Assisted