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A Smart Service Platform for Cost Efficient Cardiac Health Monitoring

Author

Listed:
  • Oliver Faust

    (Department of Engineering and Mathematics, Sheffield Hallam University, Sheffield S1 1WB, UK)

  • Ningrong Lei

    (Department of Engineering and Mathematics, Sheffield Hallam University, Sheffield S1 1WB, UK)

  • Eng Chew

    (Faculty of Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia)

  • Edward J. Ciaccio

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

  • U Rajendra Acharya

    (Biomedical Engineering Department, Ngee Ann Polytechnic, Singapore 599489, Singapore
    Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
    School of Management and Enterprise, University of Southern Queensland, Springfield, QLD 4350, Australia)

Abstract

Aim: In this study we have investigated the problem of cost effective wireless heart health monitoring from a service design perspective. Subject and Methods: There is a great medical and economic need to support the diagnosis of a wide range of debilitating and indeed fatal non-communicable diseases, like Cardiovascular Disease (CVD), Atrial Fibrillation (AF), diabetes, and sleep disorders. To address this need, we put forward the idea that the combination of Heart Rate (HR) measurements, Internet of Things (IoT), and advanced Artificial Intelligence (AI), forms a Heart Health Monitoring Service Platform (HHMSP). This service platform can be used for multi-disease monitoring, where a distinct service meets the needs of patients having a specific disease. The service functionality is realized by combining common and distinct modules. This forms the technological basis which facilitates a hybrid diagnosis process where machines and practitioners work cooperatively to improve outcomes for patients. Results: Human checks and balances on independent machine decisions maintain safety and reliability of the diagnosis. Cost efficiency comes from efficient signal processing and replacing manual analysis with AI based machine classification. To show the practicality of the proposed service platform, we have implemented an AF monitoring service. Conclusion: Having common modules allows us to harvest the economies of scale. That is an advantage, because the fixed cost for the infrastructure is shared among a large group of customers. Distinct modules define which AI models are used and how the communication with practitioners, caregivers and patients is handled. That makes the proposed HHMSP agile enough to address safety, reliability and functionality needs from healthcare providers.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:17:p:6313-:d:406267
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    References listed on IDEAS

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    1. U. Acharya & Oliver Faust & S. Sree & Dhanjoo Ghista & Sumeet Dua & Paul Joseph & V. Ahamed & Nittiagandhi Janarthanan & Toshiyo Tamura, 2013. "An integrated diabetic index using heart rate variability signal features for diagnosis of diabetes," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 16(2), pages 222-234.
    2. 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.
    3. Hsu, Chang Francis & Lin, Ping-Yen & Chao, Hsuan-Hao & Hsu, Long & Chi, Sien, 2019. "Average Entropy: Measurement of disorder for cardiac RR interval signals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 529(C).
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    Cited by:

    1. 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.

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