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A Smart Service Platform for Cost Efficient Cardiac Health Monitoring

Authors
  • Faust, Oliver
  • Lei, Ningrong
  • Chew, Eng
  • Ciaccio, Edward J.
  • Acharya, U Rajendra1, 2
  • 1 Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
  • 2 School of Management and Enterprise, University of Southern Queensland, Springfield, QLD 4350, Australia
Type
Published Article
Journal
International Journal of Environmental Research and Public Health
Publisher
MDPI AG
Publication Date
Aug 30, 2020
Volume
17
Issue
17
Identifiers
DOI: 10.3390/ijerph17176313
PMID: 32872667
PMCID: PMC7504315
Source
PubMed Central
Keywords
License
Green

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.

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