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BioMed Research International
Volume 2015, Article ID 680381, 7 pages
http://dx.doi.org/10.1155/2015/680381
Research Article

Disease Classification and Biomarker Discovery Using ECG Data

Department of Statistics and Actuarial Sciences, East China Normal University, Shanghai 200241, China

Received 26 August 2015; Revised 2 November 2015; Accepted 10 November 2015

Academic Editor: Cristiana Corsi

Copyright © 2015 Rong Huang and Yingchun Zhou. 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.

Linked References

  1. R. Ghongade and A. Ghatol, “A robust and reliable ECG pattern classification using QRS morphological features and ANN,” in Proceedings of the IEEE Region 10 Conference (TENCON ’08), pp. 1–6, 2008.
  2. M. Kallas, C. Francis, L. Kanaan, D. Merheb, P. Honeine, and H. Amoud, “Multi-class SVM classification combined with kernel PCA feature extraction of ECG signals,” in Proceedings of the 19th International Conference on Telecommunications (ICT ’12), pp. 1–5, Jounieh, Lebanon, April 2012. View at Publisher · View at Google Scholar · View at Scopus
  3. A. Rabee and I. Barhumi, “ECG signal classification using support vector machine based on wavelet multiresolution analysis,” in Proceedings of the 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA ’12), pp. 1319–1323, IEEE, Montreal, Canada, July 2012. View at Publisher · View at Google Scholar
  4. M. Shen, L. Wang, K. Zhu, and J. Zhu, “Multi-lead ECG classification based on independent component analysis and support vector machine,” in Proceedings of the 3rd International Conference on BioMedical Engineering and Informatics (BMEI ’10), vol. 3, pp. 960–964, IEEE, Yantai, China, October 2010. View at Publisher · View at Google Scholar · View at Scopus
  5. E. Zellmer, F. Shang, and H. Zhang, “Highly accurate ECG beat classification based on continuous wavelet transformation and multiple support vector machine classifiers,” in Proceedings of the 2nd International Conference on Biomedical Engineering and Informatics (BMEI ’09), pp. 1–5, IEEE, Tianjin, China, October 2009. View at Publisher · View at Google Scholar · View at Scopus
  6. P. de Chazal, M. O’Dwyer, and R. B. Reilly, “Automatic classification of heartbeats using ECG morphology and heartbeat interval features,” IEEE Transactions on Biomedical Engineering, vol. 51, no. 7, pp. 1196–1206, 2004. View at Publisher · View at Google Scholar · View at Scopus
  7. J. Fayn, “A classification tree approach for cardiac ischemia detection using spatiotemporal information from three standard ECG leads,” IEEE Transactions on Biomedical Engineering, vol. 58, no. 1, pp. 95–102, 2011. View at Publisher · View at Google Scholar · View at Scopus
  8. J. Mair, J. Smidt, P. Lechleitner, F. Dienstl, and B. Puschendorf, “A decision tree for the early diagnosis of acute myocardial infarction in nontraumatic chest pain patients at hospital admission,” Chest, vol. 108, no. 6, pp. 1502–1509, 1995. View at Publisher · View at Google Scholar · View at Scopus
  9. C. L. Tsien, H. S. Fraser, W. J. Long, and R. L. Kennedy, “Using classification tree and logistic regression methods to diagnose myocardial infarction,” Studies in Health Technology and Informatics, vol. 52, part 1, pp. 493–497, 1998. View at Google Scholar
  10. G. Dorffner, E. Leitgeb, and H. Koller, “Toward improving exercise ECG for detecting ischemic heart disease with recurrent and feedforward neural nets,” in Proceedings of the 4th IEEE Workshop on Neural Networks for Signal Processing (NNSP ’94), pp. 499–508, Ermioni, Greece, September 1994. View at Publisher · View at Google Scholar · View at Scopus
  11. Y. H. Hu, W. J. Tompkins, J. L. Urrusti, and V. X. Afonso, “Applications of artificial neural networks for ECG signal detection and classification,” Journal of Electrocardiology, vol. 26, supplement, pp. 66–73, 1993. View at Google Scholar · View at Scopus
  12. R. J. Martis, C. Chakraborty, and A. K. Ray, “A two-stage mechanism for registration and classification of ECG using Gaussian mixture model,” Pattern Recognition, vol. 42, no. 11, pp. 2979–2988, 2009. View at Publisher · View at Google Scholar · View at Scopus
  13. T. G. Zimmerman and T. Syeda-Mahmood, “Automatic detection of heart disease from twelve channel electrocardiogram waveforms,” in Proceedings of the Computers in Cardiology, pp. 809–812, Durham, NC, USA, October 2007. View at Publisher · View at Google Scholar · View at Scopus
  14. H. C. Bazett, “An analysis of the time-relations of electrocardiograms,” Heart, vol. 7, pp. 353–370, 1920. View at Google Scholar
  15. C. C. Chang and C. J. Lin, “LIBSVM: a library for support vector machines,” ACM Transactions on Intelligent Systems and Technology, vol. 2, no. 3, article 27, 27 pages, 2011. View at Publisher · View at Google Scholar
  16. R. Tibshirani, “Regression shrinkage and selection via the lasso,” Journal of the Royal Statistical Society B: Methodological, vol. 58, no. 1, pp. 267–288, 1996. View at Google Scholar · View at MathSciNet
  17. S. I. Lee, H. Lee, P. Abbeel, and A. Y. Ng, “Efficient L1 regularized logistic regression,” in Proceedings of the 21th National Conference on Artificial Intelligence, AAAI-06, Boston, Mass, USA, July 2006.
  18. A. Khemphila and V. Boonjing, “Comparing performances of logistic regression, decision trees, and neural networks for classifying heart disease patients,” in Proceedings of the International Conference on Computer Information Systems and Industrial Management Applications (CISIM ’10), pp. 193–198, Krackow, Germany, October 2010. View at Publisher · View at Google Scholar · View at Scopus