Realtime Atrial Fibrillation Detection System For IoMT Using Hybrid Machine Learning Classification

Atrial fibrillation (AF) is a type of cardiac arrhythmia in which the atria contract rapidly and irregularly, resulting in an irregular heartbeat. AF can lead to decreased blood flow to the body, blood clots, stroke, heart failure, and other cardiovascular complications. This paper proposes a detection system that would provide real-time AF detection by capitalizing on the power of Deep Convolutional Neural Networks and XGBoost classification models for the IoMT (Internet of Medical Things), which refers to medical devices and applications with Internet connectivity. This paper aims to improve patient outcomes, reduce healthcare costs, and increase access to healthcare services through the proposed detection system.