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Cardiac Biometric Identification using Phonocardiogram Signals by Binary Decision Tree based SVM

by K. Lakshmi Devi, M. Arthanari
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 105 - Number 8
Year of Publication: 2014
Authors: K. Lakshmi Devi, M. Arthanari
10.5120/18400-9663

K. Lakshmi Devi, M. Arthanari . Cardiac Biometric Identification using Phonocardiogram Signals by Binary Decision Tree based SVM. International Journal of Computer Applications. 105, 8 ( November 2014), 41-46. DOI=10.5120/18400-9663

@article{ 10.5120/18400-9663,
author = { K. Lakshmi Devi, M. Arthanari },
title = { Cardiac Biometric Identification using Phonocardiogram Signals by Binary Decision Tree based SVM },
journal = { International Journal of Computer Applications },
issue_date = { November 2014 },
volume = { 105 },
number = { 8 },
month = { November },
year = { 2014 },
issn = { 0975-8887 },
pages = { 41-46 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume105/number8/18400-9663/ },
doi = { 10.5120/18400-9663 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:37:12.638210+05:30
%A K. Lakshmi Devi
%A M. Arthanari
%T Cardiac Biometric Identification using Phonocardiogram Signals by Binary Decision Tree based SVM
%J International Journal of Computer Applications
%@ 0975-8887
%V 105
%N 8
%P 41-46
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Analyzing Phonocardiogram signals for Automatic Identification system by Binary Decision Tree based Support Vector Machine is a new approach in the research and this paper examines the applicability of the biometric properties of the Heart Sounds. It is a highly reliable method as it cannot be forged and difficult to disguise. This reduces falsification with highly accurate results. Multi-pass Moving Average Filters (MAF) smoothes the up-sampled DWT coefficients and the peaks are detected by Averaging the Neighbors. Spectral Features are extracted and clustered by HSOM. Rough sets Theory (RST) select the best features for classification. Binary Decision Tree based Support Vector Machine is used as a classifier for recognition and Identification.

References
  1. Anil K. Jain, Arun A. Ross, Karthik A. Nandakumar. , "Introduction to Biometrics".
  2. Walker HK, Hall WD, Hurst JW. , "Clinical methods: The History, Physical and laboratory Examinations", 3rd Edition.
  3. Kokosoon Phua, J. Chen, Tran. H. Louis shue. , "Heart Sound as a Biomteric". In Pattern Recognition Society, 2007.
  4. Mustafa Yamach, Zumray Dokur, Tamer Olmez. ,"Segmentation of S1-S2 sounds in Phonocardiogram Records using Wavelet Energies", IEEE, 2008.
  5. Cota Navin Gupta,Ramasamy Palaniappan, Sundaram Swaminathan, Shankar M. Krishnan. , " Neural Network classification of homomorphic segmented heart sounds".
  6. Yiqi Deng, peter J. bentley. , "A Robust Heart Sound Segmentation and classification Algorithm using Wavelet Decomposition and Spectrogram".
  7. Meng Ma, Van Genderen, Peter Beukelman. , "Developing and Implementing peak Detection for real time Image Registration", 2005.
  8. Lie Lu, Hao Jiang, Hong Jiang Zhang. ," A Robust Audio Classification and Segmentation Method".
  9. S. rossignol, X. Rodett, J. Soumagne, J. L. Collette, P. Depalle, " Feature Extraction and temporal segmentation of acoustic signals".
  10. Munoz. A. , Muruzabal . j. , " Self-organizing Maps for outlier detection", Neuro Computing,1998.
  11. F. Hadzic, T. S. Dillon, H. Tan, " Outlier detection strategy using the self-organizing map", Knowledge Discovery and Data Mining: Challenges and Realities, pp. 224-243, 2007.
  12. A. Nag, A. Mitra, S. Mitra, " Multiple Outlier detection in multivariate data using self-organizing maps", Computational Statistics, pp. 245-264.
  13. B. Walczk, D. L. Massart, " Rough Sets Theory".
  14. Zoya Gavrilov, "SVM Tutorial.
Index Terms

Computer Science
Information Sciences

Keywords

Phonocardiogram DWT Threshold Self-Organizing Maps Rough sets SVM.

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