IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i2p813-d482834.html
   My bibliography  Save this article

Hybrid Decision Support to Monitor Atrial Fibrillation for Stroke Prevention

Author

Listed:
  • Ningrong Lei

    (College of Business, Technology and Engineering, Sheffield Hallam University, Sheffield S1 1WB, UK)

  • Murtadha Kareem

    (Materials & Engineering Research Institute, Sheffield Hallam University, Sheffield S1 1WB, UK)

  • Seung Ki Moon

    (School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore)

  • Edward J. Ciaccio

    (Department of Medicine-Cardiology, Columbia University, New York, NY 10027, USA)

  • U Rajendra Acharya

    (Ngee Ann Polytechnic, Singapore 598269, Singapore
    Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
    School of Management and Enterprise, University of Southern Queensland, Toowoomba 4350, Australia)

  • Oliver Faust

    (College of Business, Technology and Engineering, Sheffield Hallam University, Sheffield S1 1WB, UK)

Abstract

In this paper, we discuss hybrid decision support to monitor atrial fibrillation for stroke prevention. Hybrid decision support takes the form of human experts and machine algorithms working cooperatively on a diagnosis. The link to stroke prevention comes from the fact that patients with Atrial Fibrillation (AF) have a fivefold increased stroke risk. Early diagnosis, which leads to adequate AF treatment, can decrease the stroke risk by 66% and thereby prevent stroke. The monitoring service is based on Heart Rate (HR) measurements. The resulting signals are communicated and stored with Internet of Things (IoT) technology. A Deep Learning (DL) algorithm automatically estimates the AF probability. Based on this technology, we can offer four distinct services to healthcare providers: (1) universal access to patient data; (2) automated AF detection and alarm; (3) physician support; and (4) feedback channels. These four services create an environment where physicians can work symbiotically with machine algorithms to establish and communicate a high quality AF diagnosis.

Suggested Citation

  • Ningrong Lei & Murtadha Kareem & Seung Ki Moon & Edward J. Ciaccio & U Rajendra Acharya & Oliver Faust, 2021. "Hybrid Decision Support to Monitor Atrial Fibrillation for Stroke Prevention," IJERPH, MDPI, vol. 18(2), pages 1-19, January.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:2:p:813-:d:482834
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/2/813/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/18/2/813/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Oliver Faust & Edward J. Ciaccio & U. Rajendra Acharya, 2020. "A Review of Atrial Fibrillation Detection Methods as a Service," IJERPH, MDPI, vol. 17(9), pages 1-34, April.
    2. Oliver Faust & Ningrong Lei & Eng Chew & Edward J. Ciaccio & U Rajendra Acharya, 2020. "A Smart Service Platform for Cost Efficient Cardiac Health Monitoring," IJERPH, MDPI, vol. 17(17), pages 1-18, August.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Jun Liu & Shuang Lai & Ayesha Akram Rai & Abual Hassan & Ray Tahir Mushtaq, 2023. "Exploring the Potential of Big Data Analytics in Urban Epidemiology Control: A Comprehensive Study Using CiteSpace," IJERPH, MDPI, vol. 20(5), pages 1-24, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fatma Murat & Ferhat Sadak & Ozal Yildirim & Muhammed Talo & Ender Murat & Murat Karabatak & Yakup Demir & Ru-San Tan & U. Rajendra Acharya, 2021. "Review of Deep Learning-Based Atrial Fibrillation Detection Studies," IJERPH, MDPI, vol. 18(21), pages 1-17, October.
    2. Oliver Faust & Ningrong Lei & Eng Chew & Edward J. Ciaccio & U Rajendra Acharya, 2020. "A Smart Service Platform for Cost Efficient Cardiac Health Monitoring," IJERPH, MDPI, vol. 17(17), pages 1-18, August.

    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:gam:jijerp:v:18:y:2021:i:2:p:813-:d:482834. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.