single-jc.php

JACIII Vol.17 No.2 pp. 302-310
doi: 10.20965/jaciii.2013.p0302
(2013)

Paper:

Similarity-Based Fuzzy Classification of ECG and Capnogram Signals

Janet Pomares Betancourt*,**, Chastine Fatichah*,
Martin Leonard Tangel*, Fei Yan*,
Jesus Adrian Garcia Sanchez*, Fang-Yan Dong*,
and Kaoru Hirota*

*Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan

**Central Institute of Digital Research, 202, No.1704, Siboney, Playa, Havana, Cuba

Received:
November 26, 2012
Accepted:
February 17, 2013
Published:
March 20, 2013
Keywords:
classification, similarity, fuzzy inference, ECG, capnogram
Abstract

A method for ECG and capnogram signals classification is proposed based on fuzzy similarity evaluation, where shape exchange algorithm and fuzzy inference are combined. It aims to be applied to quasi-periodic biomedical signals and has low computational cost. On the experiments for atrial fibrillation (AF) classification using two databases: MIT-BIH AF and MITBIH Normal Sinus Rhythm, values of 100%, 94.4%, and 97.6% for sensitivity, specificity, and accuracy respectively, and execution time of 0.6 s are obtained. The proposal is capable of been extended to classify different diseases, from ECG and capnogram signals, such as: Brugada syndrome, AV block, hypoventilation, and asthma among others to be implemented in low computational resources devices.

Cite this article as:
J. Betancourt, C. Fatichah, <. Tangel, F. Yan, <. Sanchez, F. Dong, and <. Hirota, “Similarity-Based Fuzzy Classification of ECG and Capnogram Signals,” J. Adv. Comput. Intell. Intell. Inform., Vol.17, No.2, pp. 302-310, 2013.
Data files:
References
  1. [1] J. G. Webster and J. W. Clark, “Medical instrumentation: application and design,” Wiley, 1995.
  2. [2] P. de Chazal and R. B. Reilly, “A comparison of the ECG classification performance of different feature sets,” Proc. of Computer in Cardiology 2000 Conf., Boston, USA, Vol.37, pp. 327-330, 2000.
  3. [3] S. Kara and M. Okandan, “Atrial fibrillation classification with artificial neural networks,” Pattern Recognition, Vol.40, No.11, pp. 2967-2973, 2007.
  4. [4] R. Couceiro et al., “Detection of Atrial Fibrillation Using Modelbased ECG Analysis,” Int. Conf. in Pattern Recognition, pp. 1-5, Tampa, FL, Dec. 2008.
  5. [5] S. Dash, K. H. Chon, S. Lu, and E. A. Raeder, “Automatic real time detection of atrial fibrillation,” Annals of Biomediacl Engineering, Vol.37, No.9, pp. 1701-1709, September 2009.
  6. [6] K. Tateno and L. Glass, “Automatic detection of atrial fibrillation using the coefficient of variation and density histograms of RR and ΔRR intervals,” Medical and Biological Engineering and Computing, Vol.39, pp. 664-671, 2001.
  7. [7] K. Tateno and L. Glass, “A method for detection of atrial fibrillation using RR intervals,” Proc. of Computers in Cardiology Conf., Boston, USA, Vol.27, pp. 391-394, 2000.
  8. [8] S. Parvaresh and A. Ayatollahi, “Automatic atrial fibrillation detection using autoregresive modeling,” Int. Conf. on Biomedical Engineering and Technology, Vol.11, pp. 105-108, Singapore, 2011.
  9. [9] B. You, R. Peslin, C. Duvivier, V. Dang Vu, and J. P. Grilliat, “Expiratory capnography in asthma: evaluation of various shape indices,” European Respiratory J., Vol.7, No.2, pp. 318-323, 1994.
  10. [10] T. T. Kean, A. H. Teo, and M. B. Malarvili, “Feature extraction of capnogram for asthmatic patient,” Second Int. Conf. on Computer Engineering and Applications, Vol.2, pp. 251-255, 2010.
  11. [11] M. Kazemi and M. B. Malarvili, “Analysis of capnogram using linear predictive coding (LPC) to differentiate asthmatic conditions,” J. Tissue. Sci. Eng., Vol.2, No.5, doi:10.4172/2157-7552.1000111, 2011.
  12. [12] N. A. R. N. Hisamuddin et al., “Correlations between capnografic waveforms and peak flow meter measurement in Emergency Department management asthma,” Int. J. of Emergency Medicine, Vol.2, No.2, pp. 83-89, 2009.
  13. [13] T. A. Howe et al., “The use of end-tidal capnography to monitor non-intubated patients presenting with acute exacerbation of asthma in the Emergency Department,” The J. of Emergency Medicine, Vol.41, No.6, pp. 581-589, 2011.
  14. [14] B. Boucheham, “Matching of quasi-periodic time series patterns by exchange of block-sorting signatures,” Pattern Recognition Letters, Vol.29, No.4, pp. 501-514, 2008.
  15. [15] G. B. Moody and R. G. Mark, “A new method for detecting atrial fibrillation using R-R intervals,” Computers in Cardiology, No.10, pp. 227-230, 1983.
  16. [16] A. L. Goldberger et al., “PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals,” Circulation, Vol.101, No.23, pp. e215-e220, 2000.
  17. [17] A. E. Portela, A. Milanés, and J. Folgueras, “Desing of a transducer for Mainstream capnograph (Diseño de un transductor para capnógrafo Mainstream),” IFMBE Proc. of the VIII Latin American Congress of Biomedical Engineering, Havana, Cuba, T145, 2009 (in Spanish). ISBN: 978-959-212-531-5

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on Jan. 21, 2019