A Fast Compressed Sensing Decoding Technique for Remote ECG Monitoring Systems

Ahmad M Rateb

doi:10.1109/access.2020.3035423

Abstract

Compressed Sensing (CS) has been proposed as a low-complexity ECG data compression scheme for wearable wireless bio-sensor devices. However, CS decoding is characterized by high computational complexity. As a result, it represents a burden to the computational and energy resources of the network gateway node, where decoding is performed. In this article, we propose a Fast Compressive Electrocardiography (FCE) technique to address this problem. CS decoding in FCE is based on Weighted Regularized Least-Squares (WRLS), rather than the standard approach based on ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> norm minimization. The WRLS formulation takes into account prior knowledge of ECG signal properties to estimate an optimally compact and accurate representation of ECG signals. Numerical results show that decoding by FCE is on average 33 times faster than the fastest tested CS-based ECG decoding technique. In addition, high-quality ECG signal reconstruction by FCE is achieved at 32% higher compression ratio. Therefore, FCE can contribute to improving the overall energy and computational resource efficiency of CS-based remote ECG monitoring systems.

Highlights

Remote health monitoring systems have recently gained significant importance, due to their role in treatment, prevention and early detection of diseases
The results shown are computed at Compression Ratio (CR) = 75% and N = 512 for the entire record number 200 of the MIT-BIH database
Fast Compressive Electrocardiography (FCE) is a low-complexity Compressed Sensing (CS) decoding technique that has been tailored for optimal reconstruction of compressed ECG signals

Summary

Introduction

Remote health monitoring systems have recently gained significant importance, due to their role in treatment, prevention and early detection of diseases. A gateway node receives, reconstructs and processes the ECG signals to extract useful medical data, such as heart rate, rhythm and various indicative intervals [3], [4]. It forwards these data to a cloud-based database that can be accessed by medical specialists. Recent works have shown that ECG data processing and information extraction at the gateway node is more energy-efficient than blindly forwarding raw ECG data to the cloud It reduces the traffic load on the network [1], [5], [6]. Compression performance is quantified by the Compression Ratio (CR), which is defined as:

Methods

Results

Conclusion