IDEAS home Printed from https://ideas.repec.org/a/igg/jhisi0/v3y2008i4p68-80.html
   My bibliography  Save this article

Ambient Intelligence and Pervasive Architecture Designed within the EPI-MEDICS Personal ECG Monitor

Author

Listed:
  • Hussein Atoui

    (Université de Lyon and INSERM, France)

  • David Télisson

    (Université de Lyon and INSERM, France)

  • Jocelyne Fyan

    (Université de Lyon and INSERM, France)

  • Paul Rubel

    (Université de Lyon and INSERM, France)

Abstract

Recent years have witnessed a growing interest in developing personalized and nonhospital based care systems to improve the management of cardiac care. The EPI-MEDICS project has designed an intelligent, portable Personal ECG Monitor (PEM) embedding an advanced decision making system. We present two of the ambient intelligence models embedded in the PEM: the neural-network based ischemia detection module and the Bayesian-network risk stratification module. Ischemia detection was expanded to take into account the patient ECG, clinical data, and medical history. The neural-network ECG interpretation module and the Bayesian-network risk factors module collaborate through a fuzzy-logic-based layer. We also present two telemedicine solutions that we have designed and in which the PEM is integrated. The first telemedical architecture was created to allow the collection of medical data and their transmission between healthcare providers to get an expert opinion. The second one is intended for improving healthcare in old people’s homes.

Suggested Citation

  • Hussein Atoui & David Télisson & Jocelyne Fyan & Paul Rubel, 2008. "Ambient Intelligence and Pervasive Architecture Designed within the EPI-MEDICS Personal ECG Monitor," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 3(4), pages 68-80, October.
    • Handle: RePEc:igg:jhisi0:v:3:y:2008:i:4:p:68-80
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jhisi.2008100105
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nida Shahid & Tim Rappon & Whitney Berta, 2019. "Applications of artificial neural networks in health care organizational decision-making: A scoping review," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-22, February.
    2. Jui-Chien Hsieh & Ai-Hsien Li & Chung-Chi Yang, 2013. "Mobile, Cloud, and Big Data Computing: Contributions, Challenges, and New Directions in Telecardiology," IJERPH, MDPI, vol. 10(11), pages 1-23, November.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jhisi0:v:3:y:2008:i:4:p:68-80. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.