ECG Monitoring Based on Dynamic Compressed Sensing of Multi-Lead Signals

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 make...

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Autores principales:	Pasquale Daponte, Luca De Vito, Grazia Iadarola, Francesco Picariello
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	electrocardiogram Compressed Sensing multiple measurement vector reconstruction signal recovery biomedical measurement system wearable devices Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/2f1f09ecea2e4f7d892e492ee8eec8bb
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spelling	oai:doaj.org-article:2f1f09ecea2e4f7d892e492ee8eec8bb2021-11-11T19:02:40ZECG Monitoring Based on Dynamic Compressed Sensing of Multi-Lead Signals10.3390/s212170031424-8220https://doaj.org/article/2f1f09ecea2e4f7d892e492ee8eec8bb2021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7003https://doaj.org/toc/1424-8220This 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 (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>C</mi><mi>R</mi></mrow></semantics></math></inline-formula>) up to 10, without compromising signal quality. In particular, for <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>C</mi><mi>R</mi><mo>=</mo></mrow></semantics></math></inline-formula> 10, it exhibits a percentage of root-mean-squared difference average among a wide set of ECG signals lower than 3%.Pasquale DaponteLuca De VitoGrazia IadarolaFrancesco PicarielloMDPI AGarticleelectrocardiogramCompressed Sensingmultiple measurement vector reconstructionsignal recoverybiomedical measurement systemwearable devicesChemical technologyTP1-1185ENSensors, Vol 21, Iss 7003, p 7003 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	electrocardiogram Compressed Sensing multiple measurement vector reconstruction signal recovery biomedical measurement system wearable devices Chemical technology TP1-1185
spellingShingle	electrocardiogram Compressed Sensing multiple measurement vector reconstruction signal recovery biomedical measurement system wearable devices Chemical technology TP1-1185 Pasquale Daponte Luca De Vito Grazia Iadarola Francesco Picariello ECG Monitoring Based on Dynamic Compressed Sensing of Multi-Lead Signals
description	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 (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>C</mi><mi>R</mi></mrow></semantics></math></inline-formula>) up to 10, without compromising signal quality. In particular, for <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>C</mi><mi>R</mi><mo>=</mo></mrow></semantics></math></inline-formula> 10, it exhibits a percentage of root-mean-squared difference average among a wide set of ECG signals lower than 3%.
format	article
author	Pasquale Daponte Luca De Vito Grazia Iadarola Francesco Picariello
author_facet	Pasquale Daponte Luca De Vito Grazia Iadarola Francesco Picariello
author_sort	Pasquale Daponte
title	ECG Monitoring Based on Dynamic Compressed Sensing of Multi-Lead Signals
title_short	ECG Monitoring Based on Dynamic Compressed Sensing of Multi-Lead Signals
title_full	ECG Monitoring Based on Dynamic Compressed Sensing of Multi-Lead Signals
title_fullStr	ECG Monitoring Based on Dynamic Compressed Sensing of Multi-Lead Signals
title_full_unstemmed	ECG Monitoring Based on Dynamic Compressed Sensing of Multi-Lead Signals
title_sort	ecg monitoring based on dynamic compressed sensing of multi-lead signals
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/2f1f09ecea2e4f7d892e492ee8eec8bb
work_keys_str_mv	AT pasqualedaponte ecgmonitoringbasedondynamiccompressedsensingofmultileadsignals AT lucadevito ecgmonitoringbasedondynamiccompressedsensingofmultileadsignals AT graziaiadarola ecgmonitoringbasedondynamiccompressedsensingofmultileadsignals AT francescopicariello ecgmonitoringbasedondynamiccompressedsensingofmultileadsignals
_version_	1718431633695047680

ECG Monitoring Based on Dynamic Compressed Sensing of Multi-Lead Signals

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