A Smart Service Platform for Cost Efficient Cardiac Health Monitoring

Oliver Faust, Ningrong Lei, Eng Chew, Edward J. Ciaccio and U Rajendra Acharya
Additional contact information
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

IJERPH, 2020, vol. 17, issue 17, 1-18

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.

Keywords: service platform; internet of things; e-health; deep learning; heart rate (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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