Designing a New Framework for the Patient Affected with Heart Problem


  • I-Shyan Hwang Yuan Ze University Chung-Li, Taiwan
  • D. Beybutyan Institute for Informatics and Automation Problems of NAS RA


Mobile healthcare, GSM, ECG signal, heart attack, visual data, predict, software, java


According to recent research healthcare is becoming more expensive and less accessible for general public. Cellular phones and GSM networks are considered as cheapest and most accessible means of communications, so they can be used as one of the best devices in creating healthcare systems. During recent years several commercial tools are introduced to monitor and improve health. Each of the present pieces of software in the field of heart diseases accounts for a separate task. In present work we try to define a new frame work and software that can cover all kinds of heart disorders is an ideal for patients and that they can use it with the least know-how and effort. We designed the software so, that it should be easy to use, independent from any operating system and hardware of cell phone, independent from place as well as it can predict heart attack and in case of emergency, immediately send the alarm.


The University of Maine, The U.S. Healthcare system: best in the world, or just the most expensive?

World Economic Forum transforming pensions and healthcare in a rapidly ageing world: Opportunities and Collaborative Strategies., 2009.

M. Duplaga, M. Leszczuk, K. Zielinski, “Improving access of associated states to advanced concepts in medical telematics – a day before the accession to EU", Int. J. Med. Inf., 75, pp. 300-305, 2006.

Y.E. Lee, I. Benbasat, “A framework for the study of customer interface design for mobile commerce", Int. J. Electron. Commer. vol. 8, no. 3, pp. 79-102, 2004.

Mobile Health: The potential of mobile telephony to bring healthcare to the majority. 2009. Available at, August 1, 2010.

R.A. Clark, S.C. Inglis, F.A. McAlister, A.L. Smith, "Telemonitoring or structured telephone support programmes for patients with chronic heart failure: Systematic review and meta-analysis". Br Med J; 334, pp. 942-950, 2007.

MOCA. Care anywhere-Wireless healthcare, MIT MOCA Project. Available at last accessed October 20, 2009.

R. Iqbal, R.A. Gatward, A.E. James, “A general approach to ethnographic analysis for systems design", in Proceedings of the 23rd Annual International Conference on Design of Communication: Documenting & Designing for Pervasive Information, SIGDOC, pp. 34-40, 2005.

J.E. Bardram, H.B. Christensen, “Pervasive computing support for hospitals: an overview of the activity-based computing project", IEEE Pervasive Computing, vol. 6, issue 1, pp. 44-51, 2007.

N. Oliver, F. Flores-Mangas. "Health Gear: a real-time wearable system for monitoring and analyzing physiological signals", Microsoft Research Technical Report MSR-TR-2005-182, 2005.

D.D. Vergados, “Service personalization for assistive living in a mobile ambient healthcare-networked environment", Pers Ubiquit Comput, vol. 14, pp. 575-590, 2010.

iPhone Developer Program: Available on Accessed on March 3, 2009.

Ars Technica: iPhone, App Store Problems causing more than just Headaches. Available on, Accessed on March 25, 2009.

Y. Kogure, H. Matsuoka, M. Akutagawa, Y. Shimada, Y. Kinouchi. The applications of remote patient monitoring system using a Java-enabled 3G mobile phone. Berlin: Springer, 2007.

W. Walker, T. Polk, A. Hande, D. Bhatia. Remote blood pressure monitoring using a wireless sensor network. Dallas: Department of Electrical Engineering, University of Texas, 2006.

C.E. Tseng, C.Y. Peng and M.W. Chang, “Novel approach to fuzzy-wavelet ecg signal analysis for a mobile device", J. Med. Syst., vol. 34, pp. 71-81, 2010.

R. Mark, A. Mateo and J. Lee, “Ubiquitous and intelligent framework for mobile healthcare of senior citizens", Korean Soc. Internet Inform. vol. 9, no. 1, pp. 199-202, 2008.

D. Seo, J. Lee, Y. Kim, C. Choi, H. Choi and I. Jung, “Load distribution strategies in cluster-based transcoding servers for mobile clients", Lecture Notes in Comp. Sci., vol. 3983, pp. 1156-1165, 2006.

E. Kosta and O. Pitkanen Marketta Niemela. Eija Kaasinen, “Mobile-centric ambient intelligence in health-and homecare-anticipating ethical and legal challenges", Sci Eng Ethics, vol. 16, pp. 303-323, 2010.

S. Tanaka, K. Motoi, A. Ikarashi, M. Nogawa, Y. Higashi, H. Asanoi and K. Yamakoshi, “Development of non-invasive and ambulatory physiological monitoring systems for ubiquitous healthcare", SICE Annual Conf., pp. 311-315, 2008.

C.L. Fok,G.-C. Roman and C. Lu, “Mobile agent middleware for sensor networks: An application case study", Information Processing in Sensor Networks, pp. 382-387, 2005.

T. Kohonen. Self-Organizing Maps, 3rd ed. Heidelberge, Berlin. K. V Laerhoven, S. Lowette, "Real-time analysis of data from many sensors with neural networks", Proceedings of the Fourth International Symposium on Wearable Computers, pp. 7-9, 2001.

K.K. Lee, “A dynamic load balancing model for concurrently connected users in u-healthcare monitoring systems", International Journal of Pattern Recognition and Artificial Intelligence, vol. 24, No. 8, pp. 1329-1346, 2010.

B. Mirkin, K-means clustering. In: J. Lafferty, D. Madigan, F. Murtagh, P. Smyth, (Eds.), “Clustering for data mining: A data recovery approach". Chapman & Hall/CRC, London, pp. 75-110, 2005.

J.D. Piette, Milton O. Mendoza-Avelares, “Access to mobile communication technology and willingness to participate in automated telemedicine calls among chronically, Ill Patients in Honduras, vol. 16, No. 10, pp. 1030-1041, 2010.

E.G. Kim, M.R. Lee and Y.P. Zhang, A model of active agent design to cope hypertension's emergencies situation rapidly home healthcare. Las Vegas, Nevada, Worldcomp'08, 2008.

S.B. Patil, Y.S. Kumaraswamy, “Intelligent and effective heart attack prediction system using data mining and artificial neural network", European Journal of Scientific Research, vol. 31, No. 4, pp. 642-656, 2009.

H.G. Lee, K.Y. Noh, H.K. Park, K.H. Ryu, “Predicting coronary artery disease from heart rate variability using classification and statistical analysis". In: 7th IEEE International Conference on Computer and Information Technology, pp. 59-64, 2007.

Sun Developer Network-MIDP GUI programming: programming the phone interface

L. Heslop, S. Weeding, L. Dawson and other. “Implementation issues for mobile-wireless infrastructure and mobile healthcare computing devices for a hospital ward setting", J. Med. Syst, vol. 34, pp. 509-518, 2010.

E. Jovanvoc, A. Milenkovic, C. Otto, “A wireless body area network of intelligent motion sensors for computer assisted physical rehabilitation", Journal of Neuro-engineering and Rehabilitation, vol. 2, no. 6, March 2005.




How to Cite

Hwang, I.-S. ., & Beybutyan, . D. . (2021). Designing a New Framework for the Patient Affected with Heart Problem. Mathematical Problems of Computer Science, 36, 63–69. Retrieved from