Platform for Healthcare Promotion and Cardiovascular Disease Prevention

This article presents the hardware-software design and implementation of an open, integrated, and scalable healthcare platform oriented to multiple point-care scenarios for healthcare promotion and cardiovascular disease prevention. The platform has the capability to provide continuous monitoring, e...

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Bibliographic Details
Published in:IEEE journal of biomedical and health informatics 2021-07, Vol.25 (7), p.2758-2767
Main Authors: Gomez-Garcia, Carlos A., Askar-Rodriguez, Miguel, Velasco-Medina, Jaime
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Biomedical data
Biomedical monitoring
Bluetooth
Cardiovascular disease
Cardiovascular diseases
Computer architecture
Computer programs
Cybersecurity
Data acquisition
Data analysis
Deep learning
Disease prevention
E-Health
Electrocardiography
Epidemiology
Hardware
Health care
Health kiosk
Health risks
Healthcare systems
Information management
Integration
IoMT
Learning algorithms
Logic gates
M-Health
Machine learning
Monitoring
Physicians
Prevention
Remote monitoring
Software
Telemedicine
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Summary:This article presents the hardware-software design and implementation of an open, integrated, and scalable healthcare platform oriented to multiple point-care scenarios for healthcare promotion and cardiovascular disease prevention. The platform has the capability to provide continuous monitoring, extended device integration, strategies based on artificial intelligence for the information analysis and cybersecurity support, delivering a secure end-to-end hardware-software solution. This platform is used to perform the remote patient health monitoring and supervision by doctors, triage procedures in hospitals, or self-care monitoring using personal devices such as tablets and cellphones. The proposed hardware architecture facilitates the integration of biomedical data acquired from different health-point cares, collecting relevant information for the detection of cardiovascular risk through deep-learning algorithms. All these characteristics make our development a strong tool to perform epidemiological profiling and future implementation of strategies for comprehensive cardiovascular risk intervention. The components of the platform are described, and their main functionalities are highlighted.
ISSN:2168-2194
2168-2208
DOI:10.1109/JBHI.2021.3051967