A Comparison of Different Clustering Methods for MIT BIH ECG Data
Electrocardiogram can be occasionally or continuously measured from living Human beings. Regardless of their Disease, Now a day’s physicians or doctors are suggesting to take ECG. These signals reflect the physiological processes and electrical activity of the Heart. Therefore, the study of ECG signals is essential for both medical applications and scientific studies for this purpose one requires best clustering method. It is difficult to provide a best clustering methods for the ECG signals because these categories may overlap, so that a method may have features from several categories. Nevertheless, it is useful to present a relatively organized picture of the different clustering methods.
(1) A Tutorial on Clustering Algorithms; home.deib.polimi.it/matteucc/clustering/tutorial_html/index.html
(2) www.physionet.org/physiobank/database/mitdb MIT Database.
(3) Ms. Ananya, Dr.S.L.Nalbalwar,Swarali Seth, “Detection of QRS Complexes in ECG using Kmeans Algorithm” International Journal of Engineering Research & Technology (IJERT), Vol. 3 Issue 5, May – 2014
(4) S. S. Meht. "Development of FCM based algorithm for the delineation of QRS-complexes in Electrocardiogram", 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 12/2009
(5) Sharathchandra C, M.Prabhakara reddy, Ibrahim patel, Rameshwar “Identification of QRS complexes using Hierarichal clustering Algorithm” IJIRCCE, Volume 4, Issue 2, Feb-2016
(6) Sharathchandra C, M.Prabhakara reddy, Ibrahim patel, Rameshwar “Mark out of QRS complexes using fuzzy C-means Algorithm” IJAREST, Volume 3, Issue 2, Feb-2016
(7) J. A. Hartigan and M. A. Wong (1979) "A K-Means Clustering Algorithm", Applied Statistics, Vol. 28, No. 1, p100-108
(8) B.U. Kohler, C. Hennig, and R. Orglmeister, “The principles of software QRS detection,” IEEE Eng Biol. Mag, vol. 21, pp. 42–57, 2002.
(9) Matlab help, MATLAB MATHWORKS. http://www.mathworks.com
(10) G.V. Van, K.V. Podmasteryev; Review of Algorithms Detection the QRS Complex based on machine Learning.
(11) Jalil, Bushra, Olivier Laligant, Eric Fauvet, and Ouadi Beya. "Detection of QRS complex in ECG signal based on classification approach", 2010 IEEE International Conference