skip to main content
research-article

A Real-Time FPGA-Based Accelerator for ECG Analysis and Diagnosis Using Association-Rule Mining

Authors Info & Claims
Published:26 February 2016Publication History
Skip Abstract Section

Abstract

Telemedicine provides health care services at a distance using information and communication technologies, which intends to be a solution to the challenges faced by current health care systems with growing numbers of population, increased demands from patients, and shortages in human resources. Recent advances in telemedicine, especially in wearable electrocardiogram (ECG) monitors, call for more intelligent and efficient automatic ECG analysis and diagnostic systems. We present a streaming architecture implemented on Field-Programmable Gate Arrays (FPGAs) to accelerate real-time ECG signal analysis and diagnosis in a pipelining and parallel way. Association-rule mining is employed to generate early diagnostic results by matching features of ECG with generated association rules. To improve performance of the processing, we propose a hardware-oriented data-mining algorithm named Bit_Q_Apriori. The corresponding hardware implementation indicates a good scalability and outperforms other hardware designs in terms of performance, throughput, and hardware cost.

References

  1. R. Agrawal, T. Imieliński, and A. Swami. 1993. Mining association rules between sets of items in large databases. ACM SIGMOD Record 22, 2, 207--216. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Z. K. Baker and V. K. Prasanna. 2005. Efficient hardware data mining with the Apriori algorithm on FPGAs. Field-programmable custom computing machines, 2005. FCCM 2005. In 13th Annual IEEE Symposium, IEEE. 3--12. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. J. Byungkook, L. Jundong, and C. Jaehong. 2013. Design and implementation of a wearable ECG system. International Journal of Smart Home 7, 2, 61--70.Google ScholarGoogle Scholar
  4. D. Cantzos, D. Dimogianopoulos, and D. Tseles. 2013. ECG diagnosis via a sequential recursive time series—wavelet classification scheme. In 2013 IEEE EUROCON, 1770--1777.Google ScholarGoogle Scholar
  5. P. Chazal, M. O’Dwyer, and R. B. Reilly. 2004. Automatic classification of heart-beats using ECG morphology and heartbeat interval features. IEEE Transactions on Biomedical Engineering 51, 7, 1196--1206.Google ScholarGoogle ScholarCross RefCross Ref
  6. M. Cvikl, F. Jager, and A. Zemva. 2007. Hardware implementation of a modified delay-coordinate mapping-based QRS complex detection algorithm. EURASIP Journal on Applied Signal Processing 1, 104--104. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. M. Cvikl and A. Zemva. 2010. FPGA-oriented HW/SW implementation of ECG beat detection and classification algorithm. Digital Signal Processing 20, 1, 238--248. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. J. Han, J. Pei, and Y. Yin. 2000. Mining frequent patterns without candidate generation. ACM SIGMOD Record 29, 2, 1--12. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. O. T. Inan, L. Giovangrandi, and G. T. A. Kovacs. 2006. Robust neural-network-based classification of premature ventricular contractions using wavelet transform and timing interval features. IEEE Transactions on Biomedical Engineering 53, 12, 2507--2515.Google ScholarGoogle ScholarCross RefCross Ref
  10. M. A. Jabbar, B. L. Deekshatulu, and P. Chandra. 2012. Prediction of risk score for heart disease using associative classification and hybrid feature subset selection. In IEEE International Conference on Intelligent Systems Design and Applications (ISDA’12). 628--634.Google ScholarGoogle Scholar
  11. S. M. Jadhav, S. L. Nalbalwar, and A. Ghatol. 2010. Artificial neural network based cardiac arrhythmia classification using ECG signal data. International Conference on Electronics and Information Engineering (ICEIE’10) 1, 228--231.Google ScholarGoogle Scholar
  12. D. Jun, J. W. Zhang, and H. Z. Hong. 2012. A remote diagnosis service platform for wearable ECG monitors. IEEE Intelligent Systems 27, 6, 36--43. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. S. Kadambe, R. Murray, and G. F. Boudreaux-Bartels. 1999. Wavelet transform-based QRS complex detector. IEEE Transactions on Biomedical Engineering 46, 7, 838--848.Google ScholarGoogle Scholar
  14. C. Li and C. Zheng. 1995. Detection of ECG characteristic points using wavelet transforms. IEEE Transactions on Biomedical Engineering 42, 1, 21--28.Google ScholarGoogle ScholarCross RefCross Ref
  15. J. P. Martinez, R. Almeida, A. P. Rocha, and P. Laguna. 2004. A wavelet-based ECG delineator: Evaluation on standard databases. IEEE Transactions on Biomedical Engineering 51, 4, 570--581.Google ScholarGoogle ScholarCross RefCross Ref
  16. F. Massé, M. V. Bussel, and A. Serteyn. 2013. Miniaturized wireless ECG monitor for real-time detection of epileptic seizures. ACM Transactions on Embedded Computing Systems 12, 4, 102. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Y. J. Min, H. K. Kim, Y. R. Kang, G. S. Kim, J. Park, and S. W. Kim. 2013. Design of wavelet-based ECG detector for implantable cardiac pacemakers. IEEE Transactions on Biomedical Circuits and Systems 7, 4, 426--436.Google ScholarGoogle ScholarCross RefCross Ref
  18. MIT-BIH. 2015. MIT-BIH Arrhythmia Database. Retrieved January 11, 2015 from http://www.physionet.org/physiobank/database/mitdb/.Google ScholarGoogle Scholar
  19. D. P. Morales, A. Garcia, E. Castillo, and M. A. Carvajal. 2011. Flexible ECG acquisition system based on analog and digital reconfigurable devices. Sensors and Actuators A: Physical 165, 2, 261--270.Google ScholarGoogle ScholarCross RefCross Ref
  20. S. O. Nambiar, Y. Abhyankar, and S. Chandrababu. 2010. Migrating FPGA based PCI express GENI design to gen2. In 2010 International Conference on IEEE Computer and Communication Technology (ICCCT’10). 617--620.Google ScholarGoogle Scholar
  21. S. Osowski and T. H. Linh. 2001. ECG beat recognition using fuzzy hybrid neural network. IEEE Transactions on Biomedical Engineering 48, 11, 1265--1271.Google ScholarGoogle ScholarCross RefCross Ref
  22. J. Pan and W. J. Tompkins. 1985. A real-time QRS detection algorithm. IEEE Transactions on Biomedical Engineering 3, 230--236.Google ScholarGoogle ScholarCross RefCross Ref
  23. C. Papaloukas, D. I. Fotiadis, A. Likas, A. P. Liavas, and L. K. Michalis. 2001. A knowledge-based technique for automated detection of ischemic episodes in long duration electrocardiograms. Medical and Biological Engineering and Computing 39, 105--112.Google ScholarGoogle ScholarCross RefCross Ref
  24. M. M. Peiró, F. Ballester, G. Paya, R. Colom, R. Gadea, and J. Belenguer. 2004. FPGA custom DSP for ECG signal analysis and compression. In Field Programmable Logic and Application (Lecture Notes in Computer Science), J. Becker, M. Platzner, S. Vernalde (Eds.). Springer, Berlin, 954--958.Google ScholarGoogle Scholar
  25. R. Srikant and R. Agrawal. 1996. Mining quantitative association rules in large relational tables. ACM SIGMOD Record 25, 2, 1--12. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Y. Sun and A. C. Cheng. 2012. Machine learning on-a-chip: A high-performance low-power reusable neuron architecture for artificial neural networks in ECG classifications. Computers in Biology and Medicine 42, 7, 751--757. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. D. W. Thoni and A. Strey. 2009. Novel strategies for hardware acceleration of frequent itemset mining with the Apriori algorithm. In International Conference on Field Programmable Logic and Applications, 2009 (FPL’09). IEEE. 489--492.Google ScholarGoogle Scholar
  28. P. E. Trahanias. 1993. An approach to QRS complex detection using mathematical morphology. IEEE Transactions on Biomedical Engineering 40, 2, 201--205.Google ScholarGoogle ScholarCross RefCross Ref
  29. M. Tsipouras and D. I. Fotiadis. 2004. Automatic arrhythmia detection based on time and time-frequency analysis of heart rate variability. Computer Methods and Programs in Biomedicine 74, 95--108.Google ScholarGoogle ScholarCross RefCross Ref
  30. X. A. Valtino and Q. N. Truong. 1999. ECG beat detection using filter banks. IEEE Transactions on Biomedical Engineering 46, 2, 192--202.Google ScholarGoogle ScholarCross RefCross Ref
  31. Y. H. Wen, J. W. Huang, and M. S. Chen. 2008. Hardware-enhanced association rule mining with hashing and pipelining. IEEE Transactions on Knowledge and Data Engineering 20, 6, 784--795. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Q. Xue, Y. H. Hu, and W. J. Tompkins. 1992. Neural network based adaptive matched filtering for QRS detection. IEEE Transactions on Biomedical Engineering 39, 4, 317--329.Google ScholarGoogle ScholarCross RefCross Ref
  33. Shengyan Zhou, Yongxin Zhu, Chaojun Wang, Xiaoqi Gu, Jun Yin, Jiang Jiang, Guoguang Rong. An FPGA-assisted cloud framework for massive ECG signal processing. In Proceedings of the 2014 IEEE 12th International Conference on Dependable, Autonomic and Secure Computing (DASC’14). 208--213. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. A Real-Time FPGA-Based Accelerator for ECG Analysis and Diagnosis Using Association-Rule Mining

          Recommendations

          Comments

          Login options

          Check if you have access through your login credentials or your institution to get full access on this article.

          Sign in

          Full Access

          PDF Format

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader
          About Cookies On This Site

          We use cookies to ensure that we give you the best experience on our website.

          Learn more

          Got it!