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

P. Mathur1 , V.S. Chouhan2

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-8 , Page no. 77-79, Aug-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i8.7779

Online published on Aug 31, 2018

Copyright © P. Mathur, V.S. Chouhan . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: P. Mathur, V.S. Chouhan, “Implementation of K-Nearest Neighbor (KNN) algorithm for detection of QRS Complexes,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.77-79, 2018.

MLA Style Citation: P. Mathur, V.S. Chouhan "Implementation of K-Nearest Neighbor (KNN) algorithm for detection of QRS Complexes." International Journal of Computer Sciences and Engineering 6.8 (2018): 77-79.

APA Style Citation: P. Mathur, V.S. Chouhan, (2018). Implementation of K-Nearest Neighbor (KNN) algorithm for detection of QRS Complexes. International Journal of Computer Sciences and Engineering, 6(8), 77-79.

BibTex Style Citation:
@article{Mathur_2018,
author = {P. Mathur, V.S. Chouhan},
title = {Implementation of K-Nearest Neighbor (KNN) algorithm for detection of QRS Complexes},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {8 2018},
volume = {6},
Issue = {8},
month = {8},
year = {2018},
issn = {2347-2693},
pages = {77-79},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=2657},
doi = {https://doi.org/10.26438/ijcse/v6i8.7779}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i8.7779}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=2657
TI - Implementation of K-Nearest Neighbor (KNN) algorithm for detection of QRS Complexes
T2 - International Journal of Computer Sciences and Engineering
AU - P. Mathur, V.S. Chouhan
PY - 2018
DA - 2018/08/31
PB - IJCSE, Indore, INDIA
SP - 77-79
IS - 8
VL - 6
SN - 2347-2693
ER -

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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.

Key-Words / Index Term

K-NN Alogorithm, QRS detection

References

[1] https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm
[2] N. S. Altman, "An introduction to kernel and nearest-neighbor nonparametric regression". The American Statistician, Vol. 46, Issue 3, pp. 175–185, 1992.
[3] P. A. Jaskowiak, R. J. G. B. Campello, "Comparing Correlation Coefficients as Dissimilarity Measures for Cancer Classification in Gene Expression Data". http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.208.993. Brazilian Symposium on Bioinformatics (BSB 2011). pp. 1–8. Retrieved 16 October 2014.
[4] 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.
[5] 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.
[6] Willems J.L., “The CSE Multilead atlas measurement results data set 3,” Common Standards for Quantitative Electrocardiography, Leuven, Belgium, 1988