Automated Medical Care: Bradycardia Detection and Cardiac Monitoring of Preterm Infants

<i>Background and Objectives</i>: Prematurity of birth occurs before the 37th week of gestation and affects up to 10% of births worldwide. It is correlated with critical outcomes; therefore, constant monitoring in neonatal intensive care units or home environments is required. The aim of...

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Autores principales:	Beatrice Arvinti, Emil Radu Iacob, Alexandru Isar, Daniela Iacob, Marius Costache
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	remote monitoring preterm birth electrocardiogram arrhythmia Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/86562ce011f642e396155ac1d5ba176d
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spelling	oai:doaj.org-article:86562ce011f642e396155ac1d5ba176d2021-11-25T18:18:31ZAutomated Medical Care: Bradycardia Detection and Cardiac Monitoring of Preterm Infants10.3390/medicina571111991648-91441010-660Xhttps://doaj.org/article/86562ce011f642e396155ac1d5ba176d2021-11-01T00:00:00Zhttps://www.mdpi.com/1648-9144/57/11/1199https://doaj.org/toc/1010-660Xhttps://doaj.org/toc/1648-9144<i>Background and Objectives</i>: Prematurity of birth occurs before the 37th week of gestation and affects up to 10% of births worldwide. It is correlated with critical outcomes; therefore, constant monitoring in neonatal intensive care units or home environments is required. The aim of this work was to develop solutions for remote neonatal intensive supervision systems, which should assist medical diagnosis of premature infants and raise alarm at cardiac abnormalities, such as bradycardia. Additionally, the COVID-19 pandemic has put a worldwide stress upon the medical staff and the management of healthcare units. <i>Materials and Methods</i>: A traditional medical diagnosing scheme was set up, implemented with the aid of powerful mathematical operators. The algorithm was tailored to the infants’ personal ECG characteristics and was tested on real ECG data from the publicly available PhysioNet database “Preterm Infant Cardio-Respiratory Signals Database”. Different processing problems were solved: noise filtering, baseline drift removal, event detection and compression of medical data using the à trous wavelet transform. <i>Results</i>: In all 10 available clinical cases, the bradycardia events annotated by the physicians were correctly detected using the RR intervals. Compressing the ECG signals for remote transmission, we obtained compression ratios (CR) varying from 1.72 to 7.42, with the median CR value around 3. <i>Conclusions</i>: We noticed that a significant amount of noise can be added to a signal while monitoring using standard clinical sensors. We tried to offer solutions for these technical problems. Recent studies have shown that persons infected with the COVID-19 disease are frequently reported to develop cardiovascular symptoms and cardiac arrhythmias. An automatic surveillance system (both for neonates and adults) has a practical medical application. The proposed algorithm is personalized, no fixed reference value being applied, and the algorithm follows the neonate’s cardiac rhythm changes. The performance depends on the characteristics of the input ECG. The signal-to-noise ratio of the processed ECG was improved, with a value of up to 10 dB.Beatrice ArvintiEmil Radu IacobAlexandru IsarDaniela IacobMarius CostacheMDPI AGarticleremote monitoringpreterm birthelectrocardiogramarrhythmiaMedicine (General)R5-920ENMedicina, Vol 57, Iss 1199, p 1199 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	remote monitoring preterm birth electrocardiogram arrhythmia Medicine (General) R5-920
spellingShingle	remote monitoring preterm birth electrocardiogram arrhythmia Medicine (General) R5-920 Beatrice Arvinti Emil Radu Iacob Alexandru Isar Daniela Iacob Marius Costache Automated Medical Care: Bradycardia Detection and Cardiac Monitoring of Preterm Infants
description	<i>Background and Objectives</i>: Prematurity of birth occurs before the 37th week of gestation and affects up to 10% of births worldwide. It is correlated with critical outcomes; therefore, constant monitoring in neonatal intensive care units or home environments is required. The aim of this work was to develop solutions for remote neonatal intensive supervision systems, which should assist medical diagnosis of premature infants and raise alarm at cardiac abnormalities, such as bradycardia. Additionally, the COVID-19 pandemic has put a worldwide stress upon the medical staff and the management of healthcare units. <i>Materials and Methods</i>: A traditional medical diagnosing scheme was set up, implemented with the aid of powerful mathematical operators. The algorithm was tailored to the infants’ personal ECG characteristics and was tested on real ECG data from the publicly available PhysioNet database “Preterm Infant Cardio-Respiratory Signals Database”. Different processing problems were solved: noise filtering, baseline drift removal, event detection and compression of medical data using the à trous wavelet transform. <i>Results</i>: In all 10 available clinical cases, the bradycardia events annotated by the physicians were correctly detected using the RR intervals. Compressing the ECG signals for remote transmission, we obtained compression ratios (CR) varying from 1.72 to 7.42, with the median CR value around 3. <i>Conclusions</i>: We noticed that a significant amount of noise can be added to a signal while monitoring using standard clinical sensors. We tried to offer solutions for these technical problems. Recent studies have shown that persons infected with the COVID-19 disease are frequently reported to develop cardiovascular symptoms and cardiac arrhythmias. An automatic surveillance system (both for neonates and adults) has a practical medical application. The proposed algorithm is personalized, no fixed reference value being applied, and the algorithm follows the neonate’s cardiac rhythm changes. The performance depends on the characteristics of the input ECG. The signal-to-noise ratio of the processed ECG was improved, with a value of up to 10 dB.
format	article
author	Beatrice Arvinti Emil Radu Iacob Alexandru Isar Daniela Iacob Marius Costache
author_facet	Beatrice Arvinti Emil Radu Iacob Alexandru Isar Daniela Iacob Marius Costache
author_sort	Beatrice Arvinti
title	Automated Medical Care: Bradycardia Detection and Cardiac Monitoring of Preterm Infants
title_short	Automated Medical Care: Bradycardia Detection and Cardiac Monitoring of Preterm Infants
title_full	Automated Medical Care: Bradycardia Detection and Cardiac Monitoring of Preterm Infants
title_fullStr	Automated Medical Care: Bradycardia Detection and Cardiac Monitoring of Preterm Infants
title_full_unstemmed	Automated Medical Care: Bradycardia Detection and Cardiac Monitoring of Preterm Infants
title_sort	automated medical care: bradycardia detection and cardiac monitoring of preterm infants
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/86562ce011f642e396155ac1d5ba176d
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Automated Medical Care: Bradycardia Detection and Cardiac Monitoring of Preterm Infants

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