A Comparison of Different Clustering Methods for MIT BIH ECG Data

  • Sharathchandra Chilkuri Dept. of Biomedical Engineering, B.V. Raju Inst. of Tech., Narsapur Medak,(T. S) India
  • M. Prabhakara Reddy Dept. of Biomedical Engineering, B.V. Raju Inst. of Tech., Narsapur Medak,(T. S) India
  • Ibrahim Patel Dept. of ECE B.V. Raju Inst. of Tech., Narsapur Medak,(T. S) India
Keywords: ECG, Clustering, MITBIH, QRS Detection, Filtering.

Abstract

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.

 

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Published
2016-05-01
How to Cite
Chilkuri, S., Reddy, M. P., & Patel, I. (2016). A Comparison of Different Clustering Methods for MIT BIH ECG Data. European Journal of Applied Sciences, 4(2), 25. https://doi.org/10.14738/aivp.42.2008