Energy-Efficient Real-Time Heart Monitoring on Edge-Fog-Cloud Internet-of-Medical-Things

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 between the edge-fog/cloud. Our methodology reaches an accuracy of 99.2% on the well-known MIT-BIH Arrhythmia dataset. Evaluation on real hardware shows that our methodology is suitable for devices having a minimum RAM of 32KB. Moreover, the proposed methodology achieves $7\times$ more energy efficiency compared to state-of-the-art works.