Implementation of K-Nearest Neighbor (KNN) algorithm for detection of QRS Complexes

Authors

  • P Mathur Research Scholar, JNU Jodhpur, India
  • VS Chouhan Dept. of ECE, MBM Engineering College, Jodhpur, India

DOI:

https://doi.org/10.26438/ijcse/v6i8.7779

Keywords:

K-NN Alogorithm, QRS detection

Abstract

In this paper, K-Nearest Neighbor (KNN) algorithm as a classifier is implemented with slope as feature for detection of QRS-complex in ECG, the detection rate of 99.32% is achieved. The proposed algorithm is evaluated on standard databases CSE dataset-3.

References

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Brazilian Symposium on Bioinformatics (BSB 2011). pp. 1–8. Retrieved 16 October 2014.

J.A. Alste Van and T.S. Schilder, “Removal of baseline wander and power line interference from the ECG by an efficient FIR filter with a reduced number of taps,” IEEE Transactions on Biomedical Engineering, Vol. 32, Issue 12, pp.1052-1059, 1985.

G.S. Furno and W.J. Tompkins, “A learning filter for removing noise interference,” IEEE Transactions on Biomedical

Engineering, Vol. 30, issue 4, pp. 234-235, 1983.

Willems J.L., “The CSE Multilead atlas measurement results data set 3,” Common Standards for Quantitative Electrocardiography, Leuven, Belgium, 1988

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Published

2018-08-31
CITATION
DOI: 10.26438/ijcse/v6i8.7779
Published: 2018-08-31

How to Cite

[1]
P. Mathur and V. S. Chouhan, “Implementation of K-Nearest Neighbor (KNN) algorithm for detection of QRS Complexes”, Int. J. Comp. Sci. Eng., vol. 6, no. 8, pp. 77–79, Aug. 2018.

Issue

Vol. 6 No. 8 (2018): IJCSE August Edition

Section

Research Article

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