Classification techniques for arrhythmia patterns using convolutional neural networks and Internet of Things (IoT) devices

Agyeman, Michael Opoku, Guerrero, Andres Felipe and Vien, Quoc-Tuan ORCID: https://orcid.org/0000-0001-5490-904X (2022) Classification techniques for arrhythmia patterns using convolutional neural networks and Internet of Things (IoT) devices. IEEE Access, 10 . pp. 87387-87403. ISSN 2169-3536 [Article] (doi:10.1109/ACCESS.2022.3192390)

Preview	PDF - Published version (with publisher's formatting) Available under License Creative Commons Attribution 4.0. Download (2MB) | Preview
	PDF - Final accepted version (with author's formatting) Restricted to Repository staff and depositor only Available under License Creative Commons Attribution 4.0. Download (2MB)

Abstract

The rise of Telemedicine has revolutionized how patients are being treated, leading to several advantages such as enhanced health analysis tools, accessible remote healthcare, basic diagnostic of health parameters, etc. The advent of the Internet of Things (IoT), Artificial Intelligence (AI) and their incorporation into Telemedicine extends the potential of health benefits of Telemedicine even further. Therefore, the synergy between AI, IoT, and Telemedicine creates diverse innovative scenarios for integrating cyber-physical systems into medical health to provide remote monitoring and interactive assistance to patients. Data from World Health Organization reports that 7.4 million people died because of Atrial Fibrillation (AF), recognizing the most common arrhythmia associated with human heart rate. Causes like unhealthy diet, smoking, poor resources to go to the doctor and based on research studies, about 12 and 17.9 million of people will be suffering the AF in the USA and Europe, in 2050 and 2060, respectively. The AF as a cardiovascular disease is becoming an important public health issue to tackle. By using a systematic approach, this paper reviews recent contributions related to the acquisition of heart beats, arrhythmia detection, IoT, and visualization. In particular, by analysing the most closely related papers on Convolutional Neural Network (CNN) and IoT devices in heart disease diagnostics, we present a summary of the main research gaps with suggested directions for future research.

Actions (login required)

View Item

Statistics

DownloadsShow export options

Export as CSVXMLJSON

Activity Overview

6 month trend

45Downloads

6 month trend

86Hits

Additional statistics are available via IRStats2.