Energy-Efficient Real-Time Heart Monitoring on Edge-Fog-Cloud Internet-of-Medical-Things
Berken Utku Demirel, Islam Abdelsalam Bayoumy, Mohammad Abdullah Al, Faruque

TL;DR
This paper introduces an energy-efficient, multi-layered ECG monitoring system using a distributed CNN architecture for wearable IoMT devices, achieving high accuracy and significant energy savings.
Contribution
It proposes a novel multi-layered ECG monitoring methodology with a distributed CNN architecture optimized for low-power wearable devices.
Findings
Achieves 99.2% accuracy on MIT-BIH dataset.
Provides 7x energy efficiency over existing methods.
Suitable for devices with at least 32KB RAM.
Abstract
The recent developments in wearable devices and the Internet of Medical Things (IoMT) allow real-time monitoring and recording of electrocardiogram (ECG) signals. However, continuous monitoring of ECG signals is challenging in low-power wearable devices due to energy and memory constraints. Therefore, in this paper, we present a novel and energy-efficient methodology for continuously monitoring the heart for low-power wearable devices. The proposed methodology is composed of three different layers: 1) a Noise/Artifact detection layer to grade the quality of the ECG signals; 2) a Normal/Abnormal beat classification layer to detect the anomalies in the ECG signals, and 3) an Abnormal beat classification layer to detect diseases from ECG signals. Moreover, a distributed multi-output Convolutional Neural Network (CNN) architecture is used to decrease the energy consumption and latency…
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Taxonomy
TopicsECG Monitoring and Analysis
