Application of PNN for the Detection of QRS-complexes in Electrocardiogram using Combined Entropy

Authors

  • M. K. Bhaskar Department of Electrical Engineering, M.B.M Engineering College, J.N.V University, Jodhpur, Rajasthan, India Author
  • S. S. Mehta Department of Electrical Engineering, M.B.M Engineering College, J.N.V University, Jodhpur, Rajasthan, India Author
  • Guneet Singh Mehta Department of Electrical Engineering, Indian Institute of Technology, Jodhpur, Rajasthan, India Author
  • N. S. Lingayat Department of Electrical Engineering, Dr. B.A.T. University, I.O.P.E., Lonere-402103, Maharashtra, India Author

Keywords:

Delineation, Electrocardiogram (ECG), Morphologies of QRS-complexes, Probabilistic Neural Networks (PNN), QRS-complexes.

Abstract

This paper presents a new classification scheme for the automatic detection of QRS-boundaries in Electrocardiogram (ECG) using Probabilistic Neural Networks (PNN). Digital filtering techniques are used to remove power line interference and baseline wander present in the ECG signal. PNN is used as a classifier to delineate QRS and non-QRS-regions in single-lead ECG signal. The algorithm is implemented using MATLAB. The performance of the algorithm is validated using each lead of the 12-lead simultaneously recorded ECGs from the dataset-3 of the CSE multi-lead measurement library. Significant detection rate of 99.50% is achieved. The percentage of false positive and false negative is 0.63% and 0.50% respectively. The overall results obtained show the capability of PNN in terms of the detection rate performance in comparison to the other methods reported in literature.

References

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Published

2013-08-31

Issue

Vol.3, No.3 (Jul/Aug 2013)

Section

Articles

How to Cite

Application of PNN for the Detection of QRS-complexes in Electrocardiogram using Combined Entropy. (2013). International Journal of Current Engineering and Technology, 3(3), 856-862. https://ijcet.evegenis.org/index.php/ijcet/article/view/1313