Real-Time Management of Multimodal Streaming Data for Monitoring of Epileptic Patients


New generation of healthcare is represented by wearable health monitoring systems, which provide real-time monitoring of patient’s physiological parameters. It is expected that continuous ambulatory monitoring of vital signals will improve treatment of patients and enable proactive personal health management. In this paper, we present the implementation of a multimodal real-time system for epilepsy management. The proposed methodology is based on a data streaming architecture and efficient management of a big flow of physiological parameters. The performance of this architecture is examined for varying spatial resolution of the recorded data.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7


  1. 1.

    Hao Y. and R. Foster, “Wireless body sensor networks for healthmonitoring applications,”. Physiol. Meas., vol. 29, pp. R27–R56, 2008

  2. 2.

    World Health Organization, “Integrating prevention into health care,” cd/en/, 2012, [03–23-2014]

  3. 3.

    World Health Organization. (2009, Oct. 9). The World Health Report 2008, Primary Health Care, Now More Than Ever. World Health Org., Geneva, Switzerland. [Online]. Available:

  4. 4.

    Joseph M. Woodside, EDI and ERP: a real-time framework for HealthCare data exchange. Journal of Medical Systems, June 2007, Volume 31, Issue 3, pp 178–184.

  5. 5.

    Apiletti, D.; Baralis, E.; Bruno, G.; Cerquitelli, T., Real-Time Analysis of Physiological Data to Support Medical Applications. IEEE Transactions on Information Technology in Biomedicine, vol.13, no.3, pp.313, 321, 2009

  6. 6.

    Pantelopoulos, A., and Bourbakis, N., Prognosis—a wearable health-monitoring system for people at risk: methodology and modeling. Information Technology in Biomedicine. IEEE Transactions on. 14(3):613,621, 2010.

    Article  Google Scholar 

  7. 7.

    Wang PengWei, ZhiJun Ding, Changjun Jiang, MengChu Zhou, "Design and Implementation of a Web-Service-Based Public-Oriented Personalized Health Care Platform," Systems, Man, and Cybernetics: Systems. IEEE Transactions on, vol.43, no.4, pp.941,957, 2013

  8. 8.

    Cho, G.-Y., Lee, S.-J., and Lee, T.-R., An optimized compression algorithm for real-time ECG data transmission in wireless network of medical information systems. Journal of Medical Systems. 39:161, 2014.

    Article  PubMed  Google Scholar 

  9. 9.

    Hauser, A., Epidemiology of seizures and epilepsy in the elderly. In: Rowan, A., and Ramsay, R. (Eds.), Seizures and epilepsy in the elderly. Butterworth-Heinemann, Boston, pp. 7–18, 1997.

    Google Scholar 

  10. 10.

    Gatzoulis, L.; Iakovidis, I., Wearable and Portable eHealth Systems. Engineering in Medicine and Biology Magazine, IEEE, vol. 26, no. 5, pp.51,56, 2007

  11. 11.

    Lymberis, A.; Dittmar, A., Advanced Wearable Health Systems and Applications - Research and Development Efforts in the European Union. Engineering in Medicine and Biology Magazine, IEEE, vol. 26, no. 3, pp. 29,33, 2007

  12. 12.

    Seyfettin Noyan Oğulata, Cenk Şahin, Rızvan Erol, Neural Network-Based Computer-Aided Diagnosis in Classification of Primary Generalized Epilepsy by EEG Signals. Journal of Medical Systems, Volume 33, Issue 2, pp 107–112.

  13. 13.

    Ahmet Alkan, M. Kemal Kiymik, Comparison of AR and Welch Methods in Epileptic Seizure Detection. Journal of Medical Systems, Volume 30, Issue 6, pp 413–419.

  14. 14.

    M. Kemal Kiymik, Abdulhamit Subasi, H. Rıza Ozcalık, Neural Networks with Periodogram and Autoregressive Spectral Analysis Methods in Detection of Epileptic Seizure. Journal of Medical Systems, Volume 28, Issue 6, pp 511–522.

  15. 15.

    Bonato, P., Advances in wearable technology and applications in physical medicine and rehabilitation. Journal of NeuroEngineering and Rehabilitation. 2:2, 2005.

    PubMed Central  Article  PubMed  Google Scholar 

  16. 16.

    Bonato, P., Wearable sensors/systems and their impact on biomedical engineering. IEEE Eng. Med. Biol. Mag. 22(3):18–20, 2003.

    Article  PubMed  Google Scholar 

  17. 17.

    Pantelopoulos, A.; Bourbakis, N.G., A Survey on Wearable Sensor-Based Systems for Health Monitoring and Prognosis. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, vol. 40, no. 1, pp.1,12, 2010

  18. 18.

    Chang, Da-Wei, Sheng-Fu, Liang, Chung-Ping, Young, Fu-Zen, Shaw, Su, A.W.Y., You-De Liu, Yu-Lin, Wang, Yi-Che, Liu, Jing-Jhong, Chen, Chun-Yu, Chen, A Versatile Wireless Portable Monitoring System for Brain–Behavior Approaches. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 1, no. 4, pp.440,450, 2011

  19. 19.

    Seeger, C.; Van Laerhoven, K.; Buchmann, A., MyHealthAssistant: An Event-driven Middleware for Multiple Medical Applications on a Smartphone-mediated Body Sensor Network. Biomedical and Health Informatics, IEEE Journal of, vol.PP, no.99, pp.1,1, 2014

  20. 20.

    Habetha, J., "The myheart project - fighting cardiovascular diseases by prevention and early diagnosis". Engineering in Medicine and Biology Society, 2006. EMBS ‘06. 28th Annual International Conference of the IEEE, vol.Supplement, no., pp.6746,6749, Aug. 30 2006-Sept. 3 2006

  21. 21.

    Gyselinckx, B., Penders, J., and Vullers, R., Potential and challenges ofbody area networks for cardiac monitoring. J. Electrocardiol. 40:S165–S168, 2007.

    Article  PubMed  Google Scholar 

  22. 22.

    Brown, L, Grundlhner B, Penders J, and Gyselinckx B, “Body Area Network for Monitoring Autonomic Nervous System Responses”, in Proc. of 2009 3rd International Conference on Pervasive Computing Technologies for Healthcare - Pervasive Health 2009, Article number 5173603, April 2009.

  23. 23.

    Jin, Zhanpeng; Oresko, J.; Shimeng Huang; Cheng, A.C., "HeartToGo: A Personalized medicine technology for cardiovascular disease prevention and detection," Life Science Systems and Applications Workshop, 2009. LiSSA 2009. IEEE/NIH, vol., no., pp.80,83, 9–10 April 2009

  24. 24.

    Constantinescu, L.; Jinman Kim; Feng, D.D., SparkMed: A Framework for Dynamic Integration of Multimedia Medical Data Into Distributed m-Health Systems. Information Technology in Biomedicine, IEEE Transactions on, vol. 16, no. 1, pp.40,52, 2012

  25. 25.

    Ali, M.; Chandramouli, B.; Goldstein, J.; Schindlauer, R., 2011. The extensibility framework in Microsoft StreamInsight. In Proceedings of the 2011 I.E. 27th International Conference on Data Engineering (ICDE ‘11)

  26. 26.

    Arasu, A., Babu, S., and Widom, J., CQL: a language for continuous queries over streams and relations. Dbpl. 1-19, 2003.

  27. 27.

    StreamBase Inc.

  28. 28.

    ARMOR [Online]. Available:

  29. 29.

    Hey S., Anastasopoulou P., Bideaux A., Antonopoulos C., Voros N., Fernandez A., Megalooikonomou V., Krukowski A., "System Architecture", Cyberphysical Systems for Epilepsy and Related Brain Disorders, Voros N., Antonopoulos C. (Eds), Springer, pp. 127–136

  30. 30.

    Townsend, K.A.; Haslett, J.W.; Tsang, T.K.K.; El-Gamal, M.N.; Iniewski, K., "Recent advances and future trends in low power wireless systems for medical applications," System-on-Chip for Real-Time Applications, 2005. Proceedings. Fifth International Workshop on, vol., no., pp.476,481, 20–24 July 2005

  31. 31.


  32. 32.

    Tucker, P., Maier, D., Sheard, T., and Faragas, L., Exploiting punctuation semantics in continuous data streams. IEEE Trans. Knowl. And Data Eng. 15(3):555–568, 2003.

    Article  Google Scholar 

  33. 33.

    Johnson, T.; Muthukrishnan, S.; Shkapenyuk, V.; Spatscheck, O. "A heartbeat mechanism and its application in Gigascope." In Proc. 31st Int. Conf. on Very Large Data Bases, pages 1079–1088, 2005

  34. 34.

    Srivastava, U., and Widom, J., Flexible time management in data stream systems. In Proc. ACM SIGACT-SIGMOD Symp. On Principles of Database Systems:263–274, 2004.

  35. 35.

    Chaudhry, N.; Shaw, K.; Abdelguerfi, M. "Stream data management". Advances in Database Systems, Vol. 30, Springer 2005

  36. 36.

    Mporas, I., Tsirka, V., Zacharaki, E.I., Koutroumanidis, M., and Megalooikonomou, V., Online seizure detection from EEG and ECG signals for monitoring of epileptic patients, 8th Hellenic conference on artificial intelligence (SETN 2014). Lecture Notes in Computer Science. 8445:442–447, 2014.

    Article  Google Scholar 

  37. 37.

    Mporas, I., Tsirka, V., Zacharaki, E.I., Koutroumanidis, M., Richardson, M., and Megalooikonomou, V., Seizure detection using EEG and ECG signals for computer-based monitoring, analysis and management of epileptic patients. Expert Systems with Applications. 42(6):3227–3233, 2015.

    Article  Google Scholar 

  38. 38.

    Kemp Bob, Jesus Olivan European data format ‘plus’ (EDF+), an EDF alike standard format for the exchange of physiological data. Clinical Neurophysiology, Volume 114, Issue 9, 2003, Pages 1755–1761, ISSN 1388-2457.

Download references


The research reported in the present paper was partially supported by the ARMOR Project (FP7-ICT-2011-5.1 - 287720) “Advanced multi-paRametric Monitoring and analysis for diagnosis and Optimal management of epilepsy and Related brain disorders”, co-funded by the European Commission under the Seventh’ Framework Programme.

Author information



Corresponding author

Correspondence to Iosif Mporas.

Additional information

This article is part of the Topical Collection on Patient Facing Systems

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Triantafyllopoulos, D., Korvesis, P., Mporas, I. et al. Real-Time Management of Multimodal Streaming Data for Monitoring of Epileptic Patients. J Med Syst 40, 45 (2016).

Download citation


  • Multimodal health data
  • Data streaming
  • Online processing