Wireless Personal Communications

, Volume 75, Issue 1, pp 483–509 | Cite as

Artificial Neural Network Expert System for Integrated Heart Rate Variability

  • Gwo-Jia Jong
  • Chen-Shen Huang
  • Gwo-Jeng Yu
  • Gwo-Jiun Horng
Article
  • 296 Downloads

Abstract

This paper describes the combination of an expert system for bio-information with smart devices using a wireless sensor network. A wireless bio-sensor module acquires physiological signals, including electrocardiogram, heart rate, heart rate variability (HRV) and autonomic nervous system activity. The smart device transmits the bio-information over a wireless network to a real-time expert consultation function for analysis, storage and decision making. An artificial neural network algorithm detects the HRV parameters and examines them for features of diabetes. A centralized internet information service platform can interrogate the remote client at any time for its bio-information. In addition, the system platform can compare data files. Bio-information and diabetes information can trigger timely alert messages. The system described in this paper could be the basis for a ubiquitous mobile physiological monitor.

Keywords

WSN Smart device Expert system HRV ANN Diabetes 

References

  1. 1.
    Seyd, P. T. A., Joseph, P. K., & Jacob, J. (2011). Automated diagnosis of diabetes using heart rate variability signals. Journal of Medical System, 36, 1935–1941.Google Scholar
  2. 2.
    Silipo, R., & Marchesi, C. (1998). Artificial neural networks for automatic ECG analysis. IEEE Transactions on Signal Processing, 46(5), 1417–1425.CrossRefGoogle Scholar
  3. 3.
    Prasad, G. K., & Sahambi, J. S. (2003). Classification of ECG arrhythmias using multi-resolution analysis and neural networks. In TENCON 2003 conference on convergent technologies for Asia-Pacific region (pp. 227–231).Google Scholar
  4. 4.
    Osowski, S., & Linh, T. H. (2001). ECG beat recognition using fuzzy hybrid neural network. IEEE Transactions on Biomedical Engineering, 48, 1265–1271.CrossRefGoogle Scholar
  5. 5.
    Geer, D. (2005). Users make a Beeline for ZigBee sensor technology. Published by the IEEE Computer Society, 38, 16–19.Google Scholar
  6. 6.
    Kim, T., Kim, D., Park, N., Yoo, S. E., & Lopez, T. S. (2007). Shortcut tree routing in ZigBee networks. In Proceeding of the wireless pervasive computing, ISWPC ’07. 2nd international symposium (pp. 42–47). San Juan.Google Scholar
  7. 7.
    Safaric, S., & Malaric, K. (2006). ZigBee wireless standard. In Multimedia signal processing and communications, 48th international, symposium ELMAR-2006 (pp. 259–262).Google Scholar
  8. 8.
    Lee, J. S. (2005). An experiment on performance study of IEEE 802.15.4 wireless networks. In Proceeding of 10th IEEE conference on emerging technologies and factory automation, vol. 2 (pp. 451–458).Google Scholar
  9. 9.
    Ding, G., Sahinoglu, Z., Orlik, P., Zhang, J., & Bhargava, B. (2006). Tree-based data broadcast in IEEE 802.15.4 and ZigBee networks. IEEE Transactions on Mobile Computing, 38(11), 1561–1574.CrossRefGoogle Scholar
  10. 10.
    Eady, F. (2007). Hands-on Zigbee: Implementing 802.15.4 with microcontrollers (pp. 5–32). London: Newnes.Google Scholar
  11. 11.
    Carpenter, T. (2006). Wireless# certification official study guide (Exam PW0-050) (pp. 242–250). New York: McGraw-Hill.Google Scholar
  12. 12.
    Jennic Ltd. JN-UG-3017 (2008). ZigBee Stack User Guide, Rev. 1.6 (pp. 1–30).Google Scholar
  13. 13.
    Jennic Ltd., JN-UG-3024 (2006). IEEE802.15.4 Wireless Networks User Guide, Rev.1.1 (pp. 1–28).Google Scholar
  14. 14.
    LAN/MAN Standards Committee of the IEEE Computer Society (2003). 802.15.4 IEEE Standard for Information technology Part 15.4:Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low-Rate Wireless Personal Area Networks (LR-WPANs), IEEE.Google Scholar
  15. 15.
    Android Developer, http://developer.android.com/.
  16. 16.
    Conder, S., & Darcey, L. (2011). Android wireless application development (pp. 7–80). Reading, MA: Addison-Wesley.Google Scholar
  17. 17.
    Malik, M. (1996). Heart rate variability. European Heart Journal, 17(3), 354–381.Google Scholar
  18. 18.
    National Institutes of Health (2009). Diabetic neuropathies: The nerve damage of diabetes (pp. 1–12). U.S. Department of Health and Human Services.Google Scholar
  19. 19.
    Nathan, D. M., Clary, P. A., Backlund, J. Y., Genuth, S. M., Lachin, J.M., Orchard, T. J., Raskin, P., & Zinman, B. (2005). Intensive diabetes treatment and cardiovascular disease in patients with type 1 diabetes. The Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) Study Research Group*, 353(25), 2643–2653.Google Scholar
  20. 20.
    Uehara, A., Kurata, C., Sugi, T., Mikami, T., & Shouda, S. (1999). Diabetic cardiac autonomic dysfunction: Parasympathetic versus sympathetic. Annals of Nuclear Medicine, 13(2), 95–100.CrossRefGoogle Scholar
  21. 21.
    Emily, B., Lloyd, E., Liao, D., Prineas, R. J., Gregory, W., Wayne, D., et al. (2005). Diabetes, Glucose, Insulin, and heart rate variability. Diabetes Care, 28, 668–674.CrossRefGoogle Scholar
  22. 22.
    Seyd, P. T. A., Ahamed, V. I. T., Jacob, J., & Joseph, K. P. (2008). Time and frequency domain analysis of heart rate variability and their correlations in diabetes mellitus. International Journal of Biological and, Life Sciences, 4(1), 24–27.Google Scholar
  23. 23.
    Kudat, H., Akkaya, V., Sozen, A. B., Salman, S., Demirel, S., Ozcan, M., et al. (2006). Heart rate variability in diabetes patients. The Journal of International Medical Researc, 34, 291–296.CrossRefGoogle Scholar
  24. 24.
    Lee, T. F., Chao, P. J., & Huang, W. L. (2012). An intelligent system approach using artificial neural networks to evaluate the quality of treatment planning for nasopharyngeal carcinoma. Scientific Research and Essays, 7(14), 156–169.Google Scholar
  25. 25.
    Zhu, J., Zhu, X. D., Liang, S. X., Xu, Z. Y., Zhao, J. D., Huang, Q. F., et al. (2006). Prediction of radiation induced liver disease using artificial neural networks. Japanese Journal of Clinical, 36(12), 783–788.CrossRefGoogle Scholar
  26. 26.
    Khashei, M., & Bijari, M. (2010). An artificial neural network (p, d, q) model for timeseries forecasting. Expert Systems with Applications, 37, 479–489.CrossRefGoogle Scholar
  27. 27.
    Liu, H. S. (2006). Design and implementation of a wireless home health-care network. National Taipei University of Technology, Master paper.Google Scholar
  28. 28.
    He, T. Y. (2006). A wearable device for real time electrocardiogram(ECG) monitoring. Tzu Chi University, Master paper.Google Scholar
  29. 29.
    Huang, S. H. (2010). Fuzzy decision analysis applied to wireless physiological detection system. National Kaohsiung University of Applied Sciences, Master paper.Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Gwo-Jia Jong
    • 1
  • Chen-Shen Huang
    • 1
  • Gwo-Jeng Yu
    • 2
  • Gwo-Jiun Horng
    • 3
  1. 1.Institute of Electronic EngineeringNational Kaohsiung University of Applied SciencesKaohsiungTaiwan
  2. 2.Department of Computer Science and Information EngineeringCheng-Shiu UniversityKaohsiungTaiwan
  3. 3.Department of Computer Science and Information EngineeringNational Cheng Kung UniversityTainanTaiwan

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