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      Artificial-Intelligence-Enhanced Mobile System for Cardiovascular Health Management

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          Abstract

          The number of patients with cardiovascular diseases is rapidly increasing in the world. The workload of existing clinicians is consequently increasing. However, the number of cardiovascular clinicians is declining. In this paper, we aim to design a mobile and automatic system to improve the abilities of patients’ cardiovascular health management while also reducing clinicians’ workload. Our system includes both hardware and cloud software devices based on recent advances in Internet of Things (IoT) and Artificial Intelligence (AI) technologies. A small hardware device was designed to collect high-quality Electrocardiogram (ECG) data from the human body. A novel deep-learning-based cloud service was developed and deployed to achieve automatic and accurate cardiovascular disease detection. Twenty types of diagnostic items including sinus rhythm, tachyarrhythmia, and bradyarrhythmia are supported. Experimental results show the effectiveness of our system. Our hardware device can guarantee high-quality ECG data by removing high-/low-frequency distortion and reverse lead detection with 0.9011 Area Under the Receiver Operating Characteristic Curve (ROC–AUC) score. Our deep-learning-based cloud service supports 20 types of diagnostic items, 17 of them have more than 0.98 ROC–AUC score. For a real world application, the system has been used by around 20,000 users in twenty provinces throughout China. As a consequence, using this service, we could achieve both active and passive health management through a lightweight mobile application on the WeChat Mini Program platform. We believe that it can have a broader impact on cardiovascular health management in the world.

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          Most cited references 54

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                Author and article information

                Contributors
                Role: Academic Editor
                Role: Academic Editor
                Role: Academic Editor
                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                24 January 2021
                February 2021
                : 21
                : 3
                Affiliations
                [1 ]School of Management, University of Science and Technology of China, Hefei 230026, China; fuzj@ 123456mail.ustc.edu.cn (Z.F.); sdu@ 123456ustc.edu.cn (S.D.)
                [2 ]HeartVoice Medical Technology, Hefei 230027, China
                [3 ]National Institute of Health Data Science at Peking University, Peking University, Beijing 100191, China
                [4 ]Institute of Medical Technology, Health Science Center of Peking University, Beijing 100191, China
                [5 ]Division of Life Science and Medicine, University of Science and Technology of China, Hefei 230026, China; rae@ 123456ustc.edu.cn
                Author notes
                [* ]Correspondence: hongshenda@ 123456pku.edu.cn
                Article
                sensors-21-00773
                10.3390/s21030773
                7865877
                33498892
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

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