Abstract

This paper presents an innovative method for multiple lead electrocardiogram (ECG) monitoring based on Compressed Sensing (CS). The proposed method extends to multiple leads signals, a dynamic Compressed Sensing method, that were previously developed on a single lead. The dynamic sensing method makes use of a sensing matrix in which its elements are dynamically obtained from the signal to be compressed. In this method, for the application to multiple leads, it is proposed to use a single sensing matrix for which its elements are obtained from a combination of multiple leads. The proposed method is evaluated on a wide set of signals and acquired on healthy subjects and on subjects affected by different pathologies, such as myocardial infarction, cardiomyopathy, and bundle branch block. The experimental results demonstrated that the proposed method can be adopted for a Compression Ratio () up to 10, without compromising signal quality. In particular, for 10, it exhibits a percentage of root-mean-squared difference average among a wide set of ECG signals lower than 3%.

Highlights

  • The results demonstrate that the adopted method achieves good reconstruction quality, i.e., percentage of root-mean-squared difference (PRD) lower than 9%, for Compression Ratio (CR) around 3

  • This section illustrates the performance of the proposed method for multi-lead reconstruction

  • The performance was evaluated in terms of PRD by several investigations on wide sets of signals from Physikalisch-Technische Bundesanstalt (PTB) database

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Summary

Introduction

The proposed method extends to multiple leads signals, a dynamic Compressed Sensing method, that were previously developed on a single lead. The dynamic sensing method makes use of a sensing matrix in which its elements are dynamically obtained from the signal to be compressed. In this method, for the application to multiple leads, it is proposed to use a single sensing matrix for which its elements are obtained from a combination of multiple leads. The proposed method is evaluated on a wide set of signals and acquired on healthy subjects and on subjects affected by different pathologies, such as myocardial infarction, cardiomyopathy, and bundle branch block.

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