Heart DT: Monitoring and Preventing Cardiac Pathologies Using AI and IoT Sensors

Author:

Avanzato Roberta1ORCID,Beritelli Francesco1,Lombardo Alfio1,Ricci Carmelo12

Affiliation:

1. Department of Electrical, Electronic and Computer Engineering, University of Catania, Viale Andrea Doria, 95125 Catania, Italy

2. CNIT—Research Unit of the Catania, National Inter-University Consortium for Telecommunications, 43124 Parma, Italy

Abstract

Today’s healthcare facilities require new digital tools to cope with the rapidly increasing demand for technology that can support healthcare operators. The advancement of technology is leading to the pervasive use of IoT devices in daily life, capable of acquiring biomedical and biometric parameters, and providing an opportunity to activate new tools for the medical community. Digital twins (DTs) are a form of technology that are gaining more prominence in these scenarios. Many scientific research papers in the literature are combining artificial intelligence (AI) with DTs. In this work, we propose a case study including a proof of concept based on microservices, the heart DT, for the evaluation of electrocardiogram (ECG) signals by means of an artificial intelligence component. In addition, a higher-level platform is presented and described for the complete management and monitoring of cardiac pathologies. The overall goal is to provide a system that can facilitate the patient–doctor relationship, improve medical treatment times, and reduce costs.

Funder

National Operational Plan (PON) Project 4FRAILTY

Publisher

MDPI AG

Subject

Computer Networks and Communications

Reference41 articles.

1. Healthcare IT Market (2023, April 19). Global Forecast to 2024, By Product and End User, MarketsandMarkets. Available online: https://www.marketsandmarkets.com/Market-Reports/healthcare-it-252.html.

2. Globenewswire (2023, April 20). Global Wearable Medical Devices Market Report 2022. Available online: https://www.globenewswire.com/en/news-release/2022/07/08/2476457/28124/en/Global-Wearable-Medical-Devices-Market-Report-2022-Sector-to-Grow-to-49-6-Billion-by-2026-Despite-Data-Privacy-Concerns.html,.

3. Tyagi, S., Agarwal, A., and Maheshwari, P. (2016, January 14–15). A conceptual framework for IoT-based healthcare system using cloud computing. Proceedings of the 2016 6th International Conference—Cloud System and Big Data Engineering (Confluence), Noida, India.

4. Avanzato, R., and Beritelli, F. (2020). Automatic ECG diagnosis using convolutional neural network. Electronics, 9.

5. Avanzato, R., and Beritelli, F. (2022, January 24–26). Heart disease recognition based on extended ECG sequence database and deep learning techniques. Proceedings of the 2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS), Bali, Indonesia.

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3