The Use of Fuzzy Clustering and Correlation to Implement an Heart Disease Diagnosing System in FPGA
Evaldo Renó Faria Cintra, Tales Cleber Pimenta, Robson Luiz Moreno
DOI: 10.4236/jsea.2011.48057   PDF    HTML     5,207 Downloads   9,203 Views   Citations


In this paper we present a signal processing method capable of detecting cardiopathies in electrocardiograms that was implemented in FPGA. The adopted procedure is based on fuzzy clustering to reduce the amount of data sampling, and a comparison with samples from a previously established database. By using the correlation method on the samples, it is possible to establish an initial indication of a cardiopathy. The reduced number of samples of the clustering process turns the processing simpler and allows its hardware implementation. According to the tests conducted, the method achieves 91% correct diagnoses.

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E. Cintra, T. Pimenta and R. Moreno, "The Use of Fuzzy Clustering and Correlation to Implement an Heart Disease Diagnosing System in FPGA," Journal of Software Engineering and Applications, Vol. 4 No. 8, 2011, pp. 491-496. doi: 10.4236/jsea.2011.48057.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] R. V. Andreao, “ST-Segment Using Ridden Markov Model Beat Segmentation: Application to Ischemia Detection,” Computers in Cardiology, Vol. 31, 2004, pp. 381-384.
[2] J. Vila, J. Presedo, et al., “SUTIL: Intelligent Ischemia Monitoring System,” International Journal of Medical Informatics, Vol. 47, No. 3, 1997, pp. 193-214. doi:10.1016/S1386-5056(97)00095-6
[3] F. Jager, G. B. Moody and R. G. Mark, “Detection of Transient ST Segment Episodes during Ambulatory ECG Monitoring,” Computers and Biomedical Research, Vol. 31, No. 5, 1998, pp. 305-322. doi:10.1006/cbmr.1998.1483
[4] N. Maglaveras, T. Stamkopoulos, et al., “An Adaptive Backpropagation Neural Network for Real-Time Ischemia Episodes Detection: Development and Performance Analysis Using the European ST-T Database,” IEEE Transactions on Biomedical Engineering, Vol. 45, No. 7, 1998, pp. 193-214. doi:10.1109/10.686788
[5] A. Taddei, G. Constantino, et al., “A System for the Detection of Ischemic Episodes in Ambulatory ECG,” Computers in Cardiology, Vienna, 10-13 September 1995, pp. 705-708.
[6] B. Anuradha, V. Reddy and C. Veera, “Cardiac Arrhythmia Classification Using Fuzzy Classifiers,” Journal of Theoretical and Applied Information Technology, 2005- 2008, pp. 353-359.
[7] M. Setnes, “Supervised Fuzzy Clustering for Rule Extraction,” IEE Transactions on Fuzzy Systems, Vol. 8, No. 4, August 2000, pp. 416-424.
[8] N. A. Setiawan, P. A. Venkatachalam and A. F. M. Hani, “Missing Data Estimation on Heart Disease Using Artificial Neural Network and Rough Set Theory,” IEEE International Conference on Intelligent and Advanced Systems, Kuala Lumper, 25-28 November 2007.
[9] H. B. Zheng and J. K. Wu, “Real-Time QRS Detection Method,” 2008 10th IEEE International Conference on e-Health Net-Working, Applications and Service (HEALTHCOM 2008), Singapore, 7-9 July 2008, pp. 169- 170.
[10] L. Shi, H. Li, Z. F. Sun and W. Liu, “Research on Diagnosing Heart Disease Using Adaptive Network-Based Fuzzy Interferences System,” Proceedings of International Joint Conference on Neural Networks, Orlando, 12-17 August 2007, pp. 667-671.
[11] C. E. R. Faria, “Diagnóstico de Cardiopatias Baseado no Reconhecimento de Padroes Pelo método de Correlacao,” UNIFEI—Universidade Federal de Itajubá, 2006.
[12] O. T. Inan, L. Giovangrandi and K. T. A. Gregory, “Robust Neural-Network-Based Classification of Premature Ventricular Contractions Using Wavelet Transform and Timing Interval Features,” IEEE Transactions on Biomedical Engineering, Vol. 53, No. 12, December 2006, pp. 2507-2515.
[13] F. A. Afsar, M. U. Akram, M. Arif and J. Khurshid, “A Pruned Fuzzy k-Nearest Neighbor Classifier with Application to Electrocardiogram Based Cardiac Arrhytmia Recognition,” Proceedings of the 12th IEEE International Multitopic Conference, Karachi, 23-24 December 2008, pp. 143-148.
[14] A. Armato, E. Nardini, A. Lanatà, G. Valenza, C. Mancuso, E. P. Scilingo and D. De Rossi, “An FPGA Based Arrhythmia Recognition System for Wearable Applications,” Ninth International Conference on Intelligent Systems Design and Applications, Pisa, 30 November - 2 December 2009. doi:10.1109/ISDA.2009.246
[15] Xilinx Inc., “Spartan-3A/3AN FPGA Starter Kit Board User Guide,” June 2008.

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