Top > JACIII > Fuzzy Association Rule Mining Based Myocardial Ischemia Diagno ...

single-jc.php

JACIII Vol.19 No.2 pp. 217-224
doi: 10.20965/jaciii.2015.p0217
(2015)

Paper:

Views over last 60 days: 884

Fuzzy Association Rule Mining Based Myocardial Ischemia Diagnosis on ECG Signal

Tianyu Li*, Fangyan Dong**, and Kaoru Hirota*

*Department of Computational Intelligence & Systems Science, Tokyo Institute of Technology
G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan

**Education Academy of Computational Life Sciences, Tokyo Institute of Technology
J3-141, 4259 Nagatsuta, Midori-ku, Yokohama 226-8501, Japan

Received:
June 15, 2014
Accepted:
December 1, 2014
Published:
March 20, 2015
Creative Commons License
Keywords:
time series data, ECG, heart disease, myocardial ischemia diagnosis, fuzzy association rule mining
Abstract
A fuzzy association rule mining based method is proposed for myocardial ischemia diagnosis on ECG signals. The proposal provides interpretable and understandable information to doctors as an assistant reference, while rule mining on fuzzy itemsets guarantees that the feature segmentation before rule extraction is feasible and effective. A set of fuzzy association rules is mined through experiments on data from the European ST-T Database, and classification results of myocardial ischemia and normal heartbeats on the test dataset using the extracted rules obtained values of 83.4%, 80.7%, and 81.4% for sensitivity, specificity, and accuracy, respectively. The proposed method aims to become a helpful tool to accelerate the diagnosis of myocardial ischemia on ECG signal, and to be expanded to other heart disease diagnosis areas such as hypertensive heart disease and arrhythmia.
Cite this article as:
T. Li, F. Dong, and K. Hirota, “Fuzzy Association Rule Mining Based Myocardial Ischemia Diagnosis on ECG Signal,” J. Adv. Comput. Intell. Intell. Inform., Vol.19 No.2, pp. 217-224, 2015.
Data files:
References
  1. [1] I. Babaoglu, O. Findik, and M. Bayrak, “Effects of principle component analysis on assessment of coronary artery diseases using support vector machine,” Expert Systems with Applications, Vol.37, No.3, pp. 2182-2185, March 2010.
  2. [2] J. Park, W. Pedrycz, and M. Jeon, “Ischemia episode detection in ECG using kernel density estimation, support vector machine and feature selection,” BioMedical Engineering OnLine, June 2012.
  3. [3] N. Maglaveras, T. Stamkopoulos, C. Pappas, and M. G. Strintzis, “An Adaptive Backpropagation Neural Network for Real-Time Ischemia Episodes Detection: Development and Performance Analysis using the European ST-T Database,” IEEE Trans. on Biomedical Engineering, Vol.45, No.7, pp. 805-813, 1998.
  4. [4] S. Papadimitriou, S. Mavroudi, L. Vladutu, and A. Bezerianos, “Ischemia Detection with a Self-Organizing Map Supplemented by Supervised Learning,” IEEE Trans. on Neural Networks, Vol.12, No.3, pp. 503-515, 2001.
  5. [5] R. V. Andreao, B. Dorizzi, J. Boudy, and J. Mota, “ST-segment Analysis using Hidden Markov Model Beat Segmentation: Approach to Ischemia Detection,” Computers in Cardiology, pp. 381-384, Sep 19-22, 2004.
  6. [6] P. Ranjith, P. C. Baby, and P. Joseph, “ECG Analysis using Wavelet Transform: Application to Myocardial Ischemia Detection,” ITBM-RBM, Vol.24, No.1, pp. 44-47, 2003.
  7. [7] A. Bakhshipour, M. Pooyan, H. Mohammadnejad, and A. Fallahi, “Myocardial Ischemia Detection with ECG Analysis, Using Wavelet Transform and Support Vector Machines,” Proc. of the 17th Iranian Conf. of Biomedical Engineering, pp. 1-4, Isfahan, Nov 3-4, 2010.
  8. [8] A. Taddei, G. Distante, M. Emdin, P. Pisani, G. B. Moody, C. Zeelenberg, and C. Marchesi, “The European ST-T Database: standard for evaluating systems for the analysis of ST-T changes in ambulatory electrocardiography,” European Heart J., Vol.13, No.9, pp. 1164-1172, 1992.
  9. [9] A. L. Goldberger, L. Amaral, L. Glass, J. M. Hausdorff, P. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C. K. Peng, and H. E. Stanley, “PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals,” Circulation, Vol.101, No.23, pp. 215-220, 2000.
  10. [10] Tests and diagnosis of myocardial ischemia at Mayo Clinic, http://www.mayoclinic.org/diseases-conditions/myocardial-ischemia/basics/tests-diagnosis/CON-20035096 [Accessed April 1, 2014]
  11. [11] M. Kaur, B. Singh, and Seema, “Comparisons of Different Approaches for Removal of Base Line Wander from ECG signal,” Proc. of Int Conf. & Workshop on Emerging Trends in Technology (ICWET), pp. 1290-1294, Mumbai, 2011.
  12. [12] T. Li, F. Dong, and K. Hirota, “Distance Measure for Symbolic Approximation Representation with Subsequence Direction for Time Series Data Mining,” J. of Advanced Computational Intelligence and Intelligent Informatics, Vol.17, No.2, pp. 263-271, 2013.
  13. [13] T. P. Exarchos, C. Papaloukas, D. I. Fotiadis, and L. K. Michalis, “An Association Rule Mining-Based Methodology for Automated Detection of Ischemic ECG Beats,” IEEE Trans. on Biomedical engineering, Vol.53, No.8, pp. 1531-1540, 2006.
  14. [14] C. M. Kuok, A. Fu, and M. H. Wong, “Mining Fuzzy Association Rules in Databases,” ACM SIGMOD, Vol.27, No.1, pp. 41-46, 1998.
  15. [15] J. P. Betancourt, C. Fatichah, M. L. Tangel, F. Yan, J. A. G. Sanchez, F. Y. Dong, and K. Hirota, “Similarity-Based Fuzzy Classification of ECG and Capnogram Signals,” J. of Advanced Computational Intelligence and Intelligent Informatics, Vol.17, No.2, pp. 302-310, 2013.
  16. [16] R. Agrawal, T. Imielinski, and A. Swami, “Mining association rules between sets of items in large databases,” Proc. of the 1993 ACM SIGMOD Int. Conf. on Management of Data, pp. 207-216, New York, USA, 1993.
  17. [17] M. Delgado, “Mining fuzzy association rules: an overview,” BISC Conf., Decemebr 2003.
  18. [18] C. H. Chen, T. P. Hong, and V. S. Tseng, “Fuzzy Data Mining for Time-Series Data,” Applied Soft Computing, Vol.12, No.1, pp. 536-542, 2012.
  19. [19] X. Yin and J. Han, “CPAR: Classification based on Predictive Association Rules,” Proc. of the 2003 SIAM Int. Conf. on Data Mining, San Francisco, May 1-3, 2003.
  20. [20] B. Liu, W. Hsu, and Y. Ma, “Integrating Classification and Association Rule Mining,” KDD-98 Proc., New York, Aug 27-31, 1998.

Creative Commons License  This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

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

Last updated on Dec. 06, 2024