Advertisement

EKG Intelligent Mobile System for Home Users

  • Gabriel VillarrubiaEmail author
  • Juan F. De Paz
  • Juan M. Corchado
  • Javier Bajo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8864)

Abstract

Medical diagnosis is a fundamental field to detect potential diseases and illness in patients. Nowadays, decision support systems used to detect health problems present several technical advances with respect to the existing systems 20 or 30 years ago. This work is associated to this evolution in diagnostic systems. In this work, a low cost electrocardiography system is developed. The system is able of acquiring patient medical information and send it to a medical center in execution time. This system can be used as an alternative to the current Holter monitors in daily life to record heart activity for 24 hours.

Keywords

EKG mobile Information fusion Physical activity monitor Electrocardiogram Health sensors 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
  2. 2.
    L., Sang Yup: Examining the factors that influence early adopters’ smartphone adoption: The case of college students. Telematics and Informatics. 31(2), 308–318 (2014)CrossRefGoogle Scholar
  3. 3.
    Tseng, F., Liu, Y., Wu, H.: Market penetration among competitive innovation products: The case of the Smartphone Operating System. Journal of Engineering and Technology Management (2013)Google Scholar
  4. 4.
    Ström, R., Vendel, M., Bredican, J.: Mobile marketing: A literature review on its value for consumers and retailers. Journal of Retailing and Consumer Services (2014)Google Scholar
  5. 5.
    de Winter, C.F., Bastiaanse, L.P., Hilgenkamp, T.I.M., Evenhuis, H.M., Echteld, M.A.: Cardiovascular risk factors (diabetes, hypertension, hypercholesterolemia and metabolic syndrome) in older people with intellectual disability: Results of the HA-ID study. Research in Developmental Disabilities. 33(6), 1722–1731 (2012)CrossRefGoogle Scholar
  6. 6.
    Basterra-Gortari, F.J., Bes-Rastrollo, M., Seguí-Gómez, M., Forga, L., Alfredo, J., Martínez-González, M.A.: Trends in obesity, diabetes mellitus, hypertension and hypercholesterolemia in Spain (1997-2003). Medicina Clinica. 129(11), 405–408 (2007)CrossRefGoogle Scholar
  7. 7.
    Natarajan, S., Nietert, P.J.: Hypertension, diabetes, hypercholesterolemia, and their combinations increased health care utilization and decreased health status. Journal of Clinical Epidemiology. 57(9), 954–961 (2004)CrossRefGoogle Scholar
  8. 8.
    Lage, M.J.: Boye, KS. Pdb17 medical costs among individuals with diabetes, hypertension or hypercholesterolemia. Value in Health 10(6), A258–A259 (2007)CrossRefGoogle Scholar
  9. 9.
  10. 10.
    Kharrazi, H., Chisholm, R., VanNasdale, D., Thompson, B.: Mobile personal health records: An evaluation of features and functionality. International Journal of Medical Informatics. 81(9), 579–593 (2012)CrossRefGoogle Scholar
  11. 11.
    Liu, C., Zhu, Q., Holroyd, K., Seng, E.: Status and trends of mobile-health applications for iOS devices: A developer’s perspective. Journal of Systems and Software. 84(11), 2022–2033 (2011)CrossRefGoogle Scholar
  12. 12.
    Ritter, P.: Holter in Monitoring of Cardiac Pacing. Progress in Cardiovascular Diseases. 56(2), 211–223 (2013)CrossRefGoogle Scholar
  13. 13.
    Enriquez, A., Bittner, A., Almehairi, M., Baranchuk, A.: Electrophysiology study without intracardiac catheters. The value of proper Holter interpretation: A case report. Journal of Electrocardiology (2013)Google Scholar
  14. 14.
    Mehairi, M., Ghamdi, S., Dagriri, K., Fagih, A.: The importance of utilizing 24-h Holter monitoring as a non-invasive method of predicting the mechanism of supraventricular tachycardia. Journal of the Saudi Heart Association 23(4), 241–243 (2011)CrossRefGoogle Scholar
  15. 15.
  16. 16.
    Taylor, K., Abdulla, U., Helmer, R., Lee, J., Blanchonette, I.: Activity classification with smart phones for sports activities. 13, 428–433 (2011)Google Scholar
  17. 17.
  18. 18.
    Villarrubia, G., Bajo, J.; De Paz, J.F.; Corchado, J.M.: Real time positioning system using different sensors. In: 16th International Conference on Information Fusion (FUSION), pp. 604–609 (2013)Google Scholar
  19. 19.
    Shen, C., Kao, W., Yang, Y., Hsu, M., Wu, Y., Lai, F.: Detection of cardiac arrhythmia in electrocardiograms using adaptive feature extraction and modified support vector machines. Expert Systems with Applications 39(9), 7845–7852 (2012)CrossRefGoogle Scholar
  20. 20.
  21. 21.
    http://www.fitbit.com (Last Visited: 28/07/2014)
  22. 22.
    Shiozaki, A., Senra, T., Arteaga, E.: Myocardial fibrosis detected by cardiac CT predicts ventricular fibrillation/ventricular tachycardia events in patients with hypertrophic cardiomyopathy. Journal of Cardiovascular Computed Tomography, 171–181 (2003)Google Scholar
  23. 23.
    Chen, S.W., Chen, H.C., Chan, H.L.: A real-time QRS detection method based on moving-averaging incorporating with wavelet denoising. Comput. Methods Programs Biomed. 82, 187–195 (2006)CrossRefGoogle Scholar
  24. 24.
    Villarrubia G., De Paz J.F., Bajo, J., Corchado, J.M.: EKG Mobile. Advanced Science and Technology Letters, 49 (SoftTech 2014), pp. 95–100 (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Gabriel Villarrubia
    • 1
    Email author
  • Juan F. De Paz
    • 1
  • Juan M. Corchado
    • 1
  • Javier Bajo
    • 2
  1. 1.Department of Computer Science and AutomationUniversity of SalamancaSalamancaSpain
  2. 2.Department of Artificial Intelligence, Faculty of Computer ScienceTechnical University of MadridMadridSpain

Personalised recommendations