Key result
Novel MobileNet-based ECG monitoring system detects atrial fibrillation with ~90% accuracy.
Why the study?
Telemedicine and monitoring systems are useful and low-cost tools in cardiology, and automatic arrhythmia detection can complement physician diagnosis to improve therapeutic decision-making.
An IoT-based monitoring system using deep learning can automatically classify single-lead ECG records with 90% accuracy for atrial fibrillation, potentially aiding clinical diagnosis.
May enable remote AF monitoring in limited settings; leaves open need for prospective clinical validation.
Telemedicine and all types of monitoring systems have proven to be a useful and low-cost tool with a high level of applicability in cardiology. The objective of this work is to present an IoT-based monitoring system for cardiovascular patients. The system sends the ECG signal to a Fog layer service by using the LoRa communication protocol. Also, it includes an AI algorithm based on deep learning for the detection of Atrial Fibrillation and other heart rhythms. The automatic detection of arrhythmias can be complementary to the diagnosis made by the physician, achieving a better clinical vision that improves therapeutic decision making. The performance of the proposed system is evaluated on a dataset of 8.528 short single-lead ECG records using two merge MobileNet networks that classify data with an accuracy of 90% for atrial fibrillation.
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Rincon et al. (2020) studied Atrial Fibrillation (n=8,528). IoT and Fog Computing-Based Monitoring System was evaluated on Accuracy for atrial fibrillation classification. The proposed IoT and Fog computing-based monitoring system using two merged MobileNet networks classified atrial fibrillation from single-lead ECG records with an accuracy of 90%.
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