CNN-ENHANCED ECG WEARABLES FOR CARDIAC HEALTH ASSESSMENT WITH ARRHYTHMIA PREDICTION
DOI:
https://doi.org/10.29284/ijasis.10.1.2024.13-21Keywords:
Convolutional neural networks, arrhythmia prediction, cardiac health, real-time monitoring, feature extraction.Abstract
A new generation of ECG wearables, enhanced by Convolutional Neural Networks (CNNs), is now possible because of the rapid development of technologies. These sophisticated devices in today's technological landscape not only monitor the heart's electrical activity in real-time, but they can also predict and proactively manage arrhythmias. The ability of wearables to detect abnormalities is greatly enhanced using CNNs, which provide automated feature extraction and pattern detection. To do this, we must first transform the electrocardiogram (ECG) data into a visual format. These wearables with built-in CNN analyze ECGs in real-time, alerting users to potential arrhythmias and giving them valuable information about their heart health. Models improve with more data, leading to a comprehensive approach to managing cardiac health. This revolutionary method not only allows people to take charge of their heart health but also makes it possible for medical professionals to monitor patients remotely, ushering in what may be a new age of preventative and individualized cardiological treatment.
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Copyright (c) 2024 Durai Allwin, Dhaniyasravani. M, Rakesh Thoppaen Suresh Babu
This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
