Identification of Electrocardiographic Patterns Related to Mortality with COVID-19

COVID-19 is an infectious disease that has greatly affected worldwide healthcare systems, due to the high number of cases and deaths. As COVID-19 patients may develop cardiac comorbidities that can be potentially fatal, electrocardiographic monitoring can be crucial. This work aims to identify elect...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Applied sciences Ročník 14; číslo 2; s. 817
Hlavní autoři: Sbrollini, Agnese, Leoni, Chiara, Morettini, Micaela, Rivolta, Massimo W., Swenne, Cees A., Mainardi, Luca, Burattini, Laura, Sassi, Roberto
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.01.2024
Témata:
Advanced Repeated Structuring and Learning Procedure
Algorithms
Cardiac patients
Cardiology
Clinical medicine
COVID-19
Deep learning
Disease transmission
Electric properties
Electrocardiography
Health aspects
Heart failure
Hospitalization
Infections
Ischemia
local-interpretable model-agnostic explanations
Machine learning
Morphology
Mortality
neural network
Neural networks
Neurons
Pandemics
Respiratory system
ISSN:2076-3417, 2076-3417
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Abstract COVID-19 is an infectious disease that has greatly affected worldwide healthcare systems, due to the high number of cases and deaths. As COVID-19 patients may develop cardiac comorbidities that can be potentially fatal, electrocardiographic monitoring can be crucial. This work aims to identify electrocardiographic and vectorcardiographic patterns that may be related to mortality in COVID-19, with the application of the Advanced Repeated Structuring and Learning Procedure (AdvRS&LP). The procedure was applied to data from the “automatic computation of cardiovascular arrhythmic risk from electrocardiographic data of COVID-19 patients” (COVIDSQUARED) project to obtain neural networks (NNs) that, through 254 electrocardiographic and vectorcardiographic features, could discriminate between COVID-19 survivors and deaths. The NNs were validated by a five-fold cross-validation procedure and assessed in terms of the area under the curve (AUC) of the receiver operating characteristic. The features’ contribution to the classification was evaluated through the Local-Interpretable Model-Agnostic Explanations (LIME) algorithm. The obtained NNs properly discriminated between COVID-19 survivors and deaths (AUC = 84.31 ± 2.58% on hold-out testing datasets); the classification was mainly affected by the electrocardiographic-interval-related features, thus suggesting that changes in the duration of cardiac electrical activity might be related to mortality in COVID-19 cases.
AbstractList COVID-19 is an infectious disease that has greatly affected worldwide healthcare systems, due to the high number of cases and deaths. As COVID-19 patients may develop cardiac comorbidities that can be potentially fatal, electrocardiographic monitoring can be crucial. This work aims to identify electrocardiographic and vectorcardiographic patterns that may be related to mortality in COVID-19, with the application of the Advanced Repeated Structuring and Learning Procedure (AdvRS&LP). The procedure was applied to data from the “automatic computation of cardiovascular arrhythmic risk from electrocardiographic data of COVID-19 patients” (COVIDSQUARED) project to obtain neural networks (NNs) that, through 254 electrocardiographic and vectorcardiographic features, could discriminate between COVID-19 survivors and deaths. The NNs were validated by a five-fold cross-validation procedure and assessed in terms of the area under the curve (AUC) of the receiver operating characteristic. The features’ contribution to the classification was evaluated through the Local-Interpretable Model-Agnostic Explanations (LIME) algorithm. The obtained NNs properly discriminated between COVID-19 survivors and deaths (AUC = 84.31 ± 2.58% on hold-out testing datasets); the classification was mainly affected by the electrocardiographic-interval-related features, thus suggesting that changes in the duration of cardiac electrical activity might be related to mortality in COVID-19 cases.
Audience Academic
Author Mainardi, Luca
Leoni, Chiara
Burattini, Laura
Sassi, Roberto
Rivolta, Massimo W.
Swenne, Cees A.
Morettini, Micaela
Sbrollini, Agnese
Author_xml – sequence: 1
  givenname: Agnese
  orcidid: 0000-0002-9152-7216
  surname: Sbrollini
  fullname: Sbrollini, Agnese
– sequence: 2
  givenname: Chiara
  surname: Leoni
  fullname: Leoni, Chiara
– sequence: 3
  givenname: Micaela
  orcidid: 0000-0002-8327-8379
  surname: Morettini
  fullname: Morettini, Micaela
– sequence: 4
  givenname: Massimo W.
  orcidid: 0000-0002-8553-2414
  surname: Rivolta
  fullname: Rivolta, Massimo W.
– sequence: 5
  givenname: Cees A.
  orcidid: 0000-0001-9801-2760
  surname: Swenne
  fullname: Swenne, Cees A.
– sequence: 6
  givenname: Luca
  orcidid: 0000-0002-6276-6314
  surname: Mainardi
  fullname: Mainardi, Luca
– sequence: 7
  givenname: Laura
  orcidid: 0000-0002-9474-7046
  surname: Burattini
  fullname: Burattini, Laura
– sequence: 8
  givenname: Roberto
  orcidid: 0000-0001-9729-2641
  surname: Sassi
  fullname: Sassi, Roberto
BookMark eNptkVtLHEEQhZtgIGp88g8M-BhG-zZ9eZTVxAWDQdTXpqYvay-z05OeFvHfp-NG1GDXQxfFOR9FnT20M6bRI3RI8DFjGp_ANBGOKVZEfkK7FEvRMk7kzpv-CzqY5zWuTxOmCN5F10vnxxJDtFBiGpsUmvPB25KThexiWmWY7qNtfkEpPo9zc-0HKN41JTU_Uy4wxPLUPMZy3yyu7pZnLdFf0ecAw-wP_v376Pb7-c3ior28-rFcnF62ljNSWmoFDo47qbnDVKkgay-7HvqeBawsZn1dWStLfSeVEE70vfW2d7LX2HPC9tFyy3UJ1mbKcQP5ySSI5nmQ8spALtEO3ojgJMdSg1OBhw4ryXnHBAghmKQdrqyjLWvK6feDn4tZp4c81vUN1UR1giopX1UrqNA4hlQy2E2crTmVqlIJ1V1VHX-gquX8JtqaWYh1_s5Atgab0zxnH4yN5TmOaoyDIdj8Ddi8Cbh6vv3neTnAR-o_xXSlEA
CitedBy_id crossref_primary_10_3390_app14135816
crossref_primary_10_3390_diagnostics14171839
Cites_doi 10.1055/a-1269-1405
10.1093/oxfordjournals.pan.a004868
10.1016/j.jelectrocard.2015.05.002
10.1111/pace.14247
10.1093/oxfordjournals.eurheartj.a059647
10.1002/jgm.3303
10.1016/j.jelectrocard.2022.10.003
10.1186/s12938-019-0630-9
10.1016/j.hrthm.2020.06.009
10.1177/1179548421992327
10.1016/j.ejim.2020.06.015
10.1080/14787210.2020.1797487
10.1145/2939672.2939778
10.1161/JAHA.120.016812
10.1007/978-0-387-84858-7
10.1093/cid/ciaa1578
10.1016/j.cvdhj.2021.12.003
10.3390/s20123570
10.1016/j.jelectrocard.2021.03.001
10.1109/TBME.1985.325532
10.1016/j.jelectrocard.2008.01.012
10.1109/JBHI.2020.3035191
10.4103/jcecho.jcecho_71_20
10.1016/j.cmpb.2021.106006
10.1007/s11517-023-02773-7
10.3389/fmed.2022.1005920
10.1038/s41598-023-42252-5
10.1016/S0893-6080(05)80056-5
10.1111/anec.12806
10.1016/j.dib.2021.106762
10.1002/clc.23628
10.1016/j.jelectrocard.2021.04.001
10.1186/s12911-021-01521-x
10.1155/2021/8874450
10.5152/akd.2015.5875
10.1054/jelc.2000.20296
ContentType Journal Article
Copyright COPYRIGHT 2024 MDPI AG
2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml – notice: COPYRIGHT 2024 MDPI AG
– notice: 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID AAYXX
CITATION
ABUWG
AFKRA
AZQEC
BENPR
CCPQU
DWQXO
PHGZM
PHGZT
PIMPY
PKEHL
PQEST
PQQKQ
PQUKI
DOA
DOI 10.3390/app14020817
DatabaseName CrossRef
ProQuest Central (Alumni)
ProQuest Central UK/Ireland
ProQuest Central Essentials
ProQuest Central
ProQuest One Community College
ProQuest Central
ProQuest Central Premium
ProQuest One Academic
Publicly Available Content Database
ProQuest One Academic Middle East (New)
ProQuest One Academic Eastern Edition (DO NOT USE)
ProQuest One Academic (retired)
ProQuest One Academic UKI Edition
DOAJ Directory of Open Access Journals
DatabaseTitle CrossRef
Publicly Available Content Database
ProQuest Central
ProQuest One Academic Middle East (New)
ProQuest One Academic UKI Edition
ProQuest Central Essentials
ProQuest Central Korea
ProQuest One Academic Eastern Edition
ProQuest Central (Alumni Edition)
ProQuest One Community College
ProQuest Central (New)
ProQuest One Academic
ProQuest One Academic (New)
DatabaseTitleList
Publicly Available Content Database
CrossRef
Database_xml – sequence: 1
  dbid: DOA
  name: DOAJ Directory of Open Access Journals
  url: https://www.doaj.org/
  sourceTypes: Open Website
– sequence: 2
  dbid: PIMPY
  name: Publicly Available Content Database
  url: http://search.proquest.com/publiccontent
  sourceTypes: Aggregation Database
DeliveryMethod fulltext_linktorsrc
Discipline Engineering
Sciences (General)
EISSN 2076-3417
ExternalDocumentID oai_doaj_org_article_6fd74079ad8f4f508744536a66637250
A780871295
10_3390_app14020817
GroupedDBID .4S
2XV
5VS
7XC
8CJ
8FE
8FG
8FH
AADQD
AAFWJ
AAYXX
ADBBV
ADMLS
AFFHD
AFKRA
AFPKN
AFZYC
ALMA_UNASSIGNED_HOLDINGS
APEBS
ARCSS
BCNDV
BENPR
CCPQU
CITATION
CZ9
D1I
D1J
D1K
GROUPED_DOAJ
IAO
IGS
ITC
K6-
K6V
KC.
KQ8
L6V
LK5
LK8
M7R
MODMG
M~E
OK1
P62
PHGZM
PHGZT
PIMPY
PROAC
TUS
ABUWG
AZQEC
DWQXO
PKEHL
PQEST
PQQKQ
PQUKI
ID FETCH-LOGICAL-c431t-2c60fd4d794d0288f74d775babb3f08c03b34198c2e57866d6bbcecbd7b90e413
IEDL.DBID BENPR
ISICitedReferencesCount 2
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001148924100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN 2076-3417
IngestDate Tue Oct 14 19:09:13 EDT 2025
Mon Jun 30 14:36:02 EDT 2025
Tue Nov 11 11:11:37 EST 2025
Tue Nov 04 18:29:43 EST 2025
Tue Nov 18 21:59:34 EST 2025
Sat Nov 29 07:14:52 EST 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Issue 2
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c431t-2c60fd4d794d0288f74d775babb3f08c03b34198c2e57866d6bbcecbd7b90e413
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ORCID 0000-0002-8553-2414
0000-0002-6276-6314
0000-0001-9729-2641
0000-0002-8327-8379
0000-0002-9152-7216
0000-0001-9801-2760
0000-0002-9474-7046
OpenAccessLink https://www.proquest.com/docview/2918562877?pq-origsite=%requestingapplication%
PQID 2918562877
PQPubID 2032433
ParticipantIDs doaj_primary_oai_doaj_org_article_6fd74079ad8f4f508744536a66637250
proquest_journals_2918562877
gale_infotracmisc_A780871295
gale_infotracacademiconefile_A780871295
crossref_citationtrail_10_3390_app14020817
crossref_primary_10_3390_app14020817
PublicationCentury 2000
PublicationDate 2024-01-01
PublicationDateYYYYMMDD 2024-01-01
PublicationDate_xml – month: 01
  year: 2024
  text: 2024-01-01
  day: 01
PublicationDecade 2020
PublicationPlace Basel
PublicationPlace_xml – name: Basel
PublicationTitle Applied sciences
PublicationYear 2024
Publisher MDPI AG
Publisher_xml – name: MDPI AG
References Gomes (ref_18) 2023; 61
Pan (ref_26) 1985; 32
ref_36
ref_35
(ref_31) 1993; 6
ref_34
King (ref_33) 2001; 9
Kors (ref_29) 1990; 11
ref_30
Lee (ref_12) 2021; 44
ref_17
Han (ref_8) 2021; 2021
ref_16
Kassis (ref_41) 2022; 75
ref_37
Pavri (ref_42) 2020; 17
Vedovati (ref_11) 2020; 30
Sridhar (ref_20) 2022; 3
Angeli (ref_40) 2020; 78
Marx (ref_5) 2022; 130
ref_24
ref_23
ref_44
Habas (ref_2) 2020; 18
Yang (ref_13) 2021; 44
Kung (ref_15) 2020; 25
ref_21
Baek (ref_22) 2023; 13
Yildirim (ref_10) 2021; 66
Santoro (ref_38) 2020; 73
ref_1
Khan (ref_45) 2021; 34
Nawaz (ref_19) 2022; 9
Liu (ref_4) 2020; 9
Moga (ref_43) 2015; 15
Parwani (ref_7) 2021; 66
Sit (ref_9) 2021; 25
Man (ref_27) 2015; 48
Mohamadian (ref_3) 2021; 23
Nelwan (ref_28) 2000; 33
Schijvenaars (ref_14) 2008; 41
Wang (ref_39) 2020; 25
Prechelt (ref_32) 2012; Volume 7700
Koeppen (ref_6) 2021; 15
Sbrollini (ref_25) 2023; 24
References_xml – ident: ref_30
– volume: Volume 7700
  start-page: 53
  year: 2012
  ident: ref_32
  article-title: Early stopping—But when
  publication-title: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
– volume: 130
  start-page: 178
  year: 2022
  ident: ref_5
  article-title: COVID-19 and Cardiovascular Comorbidities
  publication-title: Exp. Clin. Endocrinol. Diabetes
  doi: 10.1055/a-1269-1405
– volume: 9
  start-page: 137
  year: 2001
  ident: ref_33
  article-title: Logistic Regression in Rare Events Data
  publication-title: Polit. Anal.
  doi: 10.1093/oxfordjournals.pan.a004868
– volume: 48
  start-page: 463
  year: 2015
  ident: ref_27
  article-title: Vectorcardiographic diagnostic & prognostic information derived from the 12-lead electrocardiogram: Historical review and clinical perspective
  publication-title: J. Electrocardiol.
  doi: 10.1016/j.jelectrocard.2015.05.002
– volume: 44
  start-page: 1062
  year: 2021
  ident: ref_12
  article-title: Arrhythmias and electrocardiographic findings in Coronavirus disease 2019: A systematic review and meta-analysis
  publication-title: Pacing Clin. Electrophysiol.
  doi: 10.1111/pace.14247
– volume: 11
  start-page: 1083
  year: 1990
  ident: ref_29
  article-title: Reconstruction of the Frank vectorcardiogram from standard electrocardiographic leads: Diagnostic comparison of different methods
  publication-title: Eur. Heart J.
  doi: 10.1093/oxfordjournals.eurheartj.a059647
– volume: 23
  start-page: e3303
  year: 2021
  ident: ref_3
  article-title: COVID-19: Virology, biology and novel laboratory diagnosis
  publication-title: J. Gene Med.
  doi: 10.1002/jgm.3303
– volume: 75
  start-page: 1
  year: 2022
  ident: ref_41
  article-title: Prognostic value of initial electrocardiography in predicting long-term all-cause mortality in COVID-19
  publication-title: J. Electrocardiol.
  doi: 10.1016/j.jelectrocard.2022.10.003
– ident: ref_23
  doi: 10.1186/s12938-019-0630-9
– volume: 17
  start-page: 1434
  year: 2020
  ident: ref_42
  article-title: Behavior of the pr interval with increasing heart rate in patients with COVID-19
  publication-title: Heart Rhythm
  doi: 10.1016/j.hrthm.2020.06.009
– volume: 25
  start-page: 3272
  year: 2021
  ident: ref_9
  article-title: Prognostic significance of Tp-e interval and Tp-e/QTc ratio in patients with COVID-19
  publication-title: Eur. Rev. Med. Pharmacol. Sci.
– ident: ref_37
– volume: 15
  start-page: 1
  year: 2021
  ident: ref_6
  article-title: COVID-19 Related Cardiovascular Comorbidities and Complications in Critically Ill Patients: A Systematic Review and Meta-analysis
  publication-title: Clin. Med. Insights Circ. Respir. Pulm. Med.
  doi: 10.1177/1179548421992327
– volume: 78
  start-page: 101
  year: 2020
  ident: ref_40
  article-title: Electrocardiographic features of patients with COVID-19 pneumonia
  publication-title: Eur. J. Intern. Med.
  doi: 10.1016/j.ejim.2020.06.015
– ident: ref_1
– ident: ref_44
– volume: 18
  start-page: 1201
  year: 2020
  ident: ref_2
  article-title: Resolution of coronavirus disease 2019 (COVID-19)
  publication-title: Expert Rev. Anti Infect. Ther.
  doi: 10.1080/14787210.2020.1797487
– ident: ref_35
  doi: 10.1145/2939672.2939778
– volume: 9
  start-page: e016812
  year: 2020
  ident: ref_4
  article-title: Prevalence of Cardiovascular Comorbidities in Coronavirus Disease 2019, Severe Acute Respiratory Syndrome, and Middle East Respiratory Syndrome: Pooled Analysis of Published Data
  publication-title: J. Am. Heart Assoc.
  doi: 10.1161/JAHA.120.016812
– ident: ref_21
– ident: ref_34
  doi: 10.1007/978-0-387-84858-7
– volume: 73
  start-page: e4031
  year: 2020
  ident: ref_38
  article-title: Qtc interval prolongation and life-threatening arrhythmias during hospitalization in patients with COVID-19. Results from a multi-center prospective registry
  publication-title: Clin. Infect. Dis.
  doi: 10.1093/cid/ciaa1578
– volume: 3
  start-page: 62
  year: 2022
  ident: ref_20
  article-title: Identifying risk of adverse outcomes in COVID-19 patients via artificial intelligence-powered analysis of 12-lead intake electrocardiogram
  publication-title: Cardiovasc. Digit. Health J.
  doi: 10.1016/j.cvdhj.2021.12.003
– ident: ref_24
  doi: 10.3390/s20123570
– volume: 66
  start-page: 108
  year: 2021
  ident: ref_10
  article-title: Fragmented QRS on surface electrocardiography as a predictor of cardiac mortality in patients with SARS-CoV-2 infection
  publication-title: J. Electrocardiol.
  doi: 10.1016/j.jelectrocard.2021.03.001
– volume: 32
  start-page: 230
  year: 1985
  ident: ref_26
  article-title: A real-time QRS detection algorithm
  publication-title: IEEE Trans. Biomed. Eng.
  doi: 10.1109/TBME.1985.325532
– volume: 41
  start-page: 190
  year: 2008
  ident: ref_14
  article-title: Intraindividual variability in electrocardiograms
  publication-title: J. Electrocardiol.
  doi: 10.1016/j.jelectrocard.2008.01.012
– volume: 25
  start-page: 1904
  year: 2020
  ident: ref_15
  article-title: An Efficient ECG Classification System using Resource-Saving Architecture and Random Forest
  publication-title: IEEE J. Biomed. Health Inf.
  doi: 10.1109/JBHI.2020.3035191
– volume: 30
  start-page: 223
  year: 2020
  ident: ref_11
  article-title: Shock and Diffuse ST-elevation in a Patient with Coronavirus Disease-2019 Disease
  publication-title: J. Cardiovasc. Echogr.
  doi: 10.4103/jcecho.jcecho_71_20
– ident: ref_16
  doi: 10.1016/j.cmpb.2021.106006
– volume: 61
  start-page: 1057
  year: 2023
  ident: ref_18
  article-title: COVID-19’s influence on cardiac function: A machine learning perspective on ECG analysis
  publication-title: Med. Biol. Eng. Comput.
  doi: 10.1007/s11517-023-02773-7
– volume: 9
  start-page: 1005920
  year: 2022
  ident: ref_19
  article-title: Efficient-ECGNet framework for COVID-19 classification and correlation prediction with the cardio disease through electrocardiogram medical imaging
  publication-title: Front. Med.
  doi: 10.3389/fmed.2022.1005920
– volume: 13
  start-page: 15187
  year: 2023
  ident: ref_22
  article-title: Artificial intelligence-enhanced electrocardiography for early assessment of coronavirus disease 2019 severity
  publication-title: Sci. Rep.
  doi: 10.1038/s41598-023-42252-5
– volume: 6
  start-page: 525
  year: 1993
  ident: ref_31
  article-title: A scaled conjugate gradient algorithm for fast supervised learning
  publication-title: Neural Netw.
  doi: 10.1016/S0893-6080(05)80056-5
– volume: 25
  start-page: 12806
  year: 2020
  ident: ref_39
  article-title: Electrocardiogram analysis of patients with different types of COVID-19
  publication-title: Ann. Noninvasive Electrocardiol.
  doi: 10.1111/anec.12806
– volume: 34
  start-page: 106762
  year: 2021
  ident: ref_45
  article-title: ECG Images dataset of Cardiac and COVID-19 Patients
  publication-title: Data Brief
  doi: 10.1016/j.dib.2021.106762
– volume: 44
  start-page: 963
  year: 2021
  ident: ref_13
  article-title: The prognostic significance of electrocardiography findings in patients with coronavirus disease 2019: A retrospective study
  publication-title: Clin. Cardiol.
  doi: 10.1002/clc.23628
– ident: ref_36
– volume: 66
  start-page: 102
  year: 2021
  ident: ref_7
  article-title: Cardiac arrhythmias in patients with COVID-19: Lessons from 2300 telemetric monitoring days on the intensive care unit
  publication-title: J. Electrocardiol.
  doi: 10.1016/j.jelectrocard.2021.04.001
– ident: ref_17
  doi: 10.1186/s12911-021-01521-x
– volume: 2021
  start-page: 8874450
  year: 2021
  ident: ref_8
  article-title: Atrial Arrhythmias in Patients with Severe COVID-19
  publication-title: Cardiol. Res. Pract.
  doi: 10.1155/2021/8874450
– volume: 24
  start-page: 44
  year: 2023
  ident: ref_25
  article-title: Advanced repeated structuring and learning procedure to detect acute myocardial ischemia in serial 12-lead ECGs
  publication-title: Physiol. Meas.
– volume: 15
  start-page: 255
  year: 2015
  ident: ref_43
  article-title: QT interval variations and mortality risk: Is. there any relationship?
  publication-title: Anatol. J. Cardiol.
  doi: 10.5152/akd.2015.5875
– volume: 33
  start-page: 163
  year: 2000
  ident: ref_28
  article-title: Minimal lead sets for reconstruction of 12-lead electrocardiograms
  publication-title: J. Electrocardiol.
  doi: 10.1054/jelc.2000.20296
SSID ssj0000913810
Score 2.284919
Snippet COVID-19 is an infectious disease that has greatly affected worldwide healthcare systems, due to the high number of cases and deaths. As COVID-19 patients may...
SourceID doaj
proquest
gale
crossref
SourceType Open Website
Aggregation Database
Enrichment Source
Index Database
StartPage 817
SubjectTerms Advanced Repeated Structuring and Learning Procedure
Algorithms
Cardiac patients
Cardiology
Clinical medicine
COVID-19
Deep learning
Disease transmission
Electric properties
Electrocardiography
Health aspects
Heart failure
Hospitalization
Infections
Ischemia
local-interpretable model-agnostic explanations
Machine learning
Morphology
Mortality
neural network
Neural networks
Neurons
Pandemics
Respiratory system
SummonAdditionalLinks – databaseName: DOAJ Directory of Open Access Journals
  dbid: DOA
  link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LS8QwEB5EPOhBXB-4vshhwQcU2zTbNMf1sejBB6Kyt5AnCLIr7urvd5LUdQXFi7fSTmGazCTflybfAHR8rnFkZD5ztnIZc6bIlKvLrPBF1wscKwVVsdgEv76uBwNxO1PqK-wJS_LAqeGOK285kg6hbO2ZRzjBGeuWlULYXXKa2DqinhkyFcdgUQTpqnQgr0ReH_4HF4Er1bE02dcUFJX6fxuP4yTTX4HlBh2SXvKqBXNuuApLM5qBq9BqsnFMDhrJ6MM1uEvnbX2zAEdGnpyn-jYm7jeNstRPhtxGNc3hmMQ9cM6SyYhcRQCOYJyENVlyevN4eZYVYh0e-uf3pxdZUy0hMwgCJhk1Ve4ts5hgFkFD7Tle865WWpc-r01e6qDdVhvqMEurylZaG2e05VrkDueyDZgfjoZuE4jz1KuCKZcrywwzygTiZalCLkJNIdpw9NmA0jRS4qGixbNEShFaW860dhs6U-OXpKDxs9lJ6ImpSZC9jjcwGGQTDPKvYGjDfuhHGZITHULH0xkD_KwgcyV7vMZ3MAC7bdj5ZolJZb4__owE2ST1WFKB4KZCism3_sPZbVikiJDSes4OzE9e39wuLJj3ydP4dS_G8wc22vaK
  priority: 102
  providerName: Directory of Open Access Journals
Title Identification of Electrocardiographic Patterns Related to Mortality with COVID-19
URI https://www.proquest.com/docview/2918562877
https://doaj.org/article/6fd74079ad8f4f508744536a66637250
Volume 14
WOSCitedRecordID wos001148924100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVAON
  databaseName: DOAJ Directory of Open Access Journals
  customDbUrl:
  eissn: 2076-3417
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000913810
  issn: 2076-3417
  databaseCode: DOA
  dateStart: 20110101
  isFulltext: true
  titleUrlDefault: https://www.doaj.org/
  providerName: Directory of Open Access Journals
– providerCode: PRVHPJ
  databaseName: ROAD: Directory of Open Access Scholarly Resources
  customDbUrl:
  eissn: 2076-3417
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000913810
  issn: 2076-3417
  databaseCode: M~E
  dateStart: 20110101
  isFulltext: true
  titleUrlDefault: https://road.issn.org
  providerName: ISSN International Centre
– providerCode: PRVPQU
  databaseName: Proquest Central Journals
  customDbUrl:
  eissn: 2076-3417
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000913810
  issn: 2076-3417
  databaseCode: BENPR
  dateStart: 20110101
  isFulltext: true
  titleUrlDefault: https://www.proquest.com/central
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: Publicly Available Content Database
  customDbUrl:
  eissn: 2076-3417
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000913810
  issn: 2076-3417
  databaseCode: PIMPY
  dateStart: 20110101
  isFulltext: true
  titleUrlDefault: http://search.proquest.com/publiccontent
  providerName: ProQuest
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3NTxQxFH_BhYMeRFDDKpAeSESTxvnoTjsnArgEDqwTAgZPTT8NCdnBndW_39dOd4EEvXibjzdJJ--jv_fa_h7Ans80RkbmqbOVo8yZnConSpr7fORrjJV1oWKzCT6ZiOvrukkFty5tq1zExBiobWtCjfxzUePMUiG-5wd3P2noGhVWV1MLjWewGpjK2ABWj8aT5mJZZQmslyLP-oN5Jeb3YV04DzmTiC3K7qeiyNj_t7gcJ5uT9f8d5it4mWAmOeztYgNW3HQTXjwgH9yEjeTWHdlP3NMfX8NFf3DXp0oeaT0Z941yTNy4GvmtbwxpIi3ntCNxM52zZN6S84jkEdWTUNwlx1-_nX2hef0Grk7Gl8enNLVdoAbRxJwWpsq8ZRY91SL6EJ7jNR9ppXXpM2GyUgcSOGEKh-5eVbbS2jijLdd15nBSfAuDaTt1W0CcL7zKmXKZsswwo0zI4GyhMKkpTF4P4dNCA9IkTvLQGuNWYm4S1CUfqGsIe0vhu56K42mxo6DKpUjgz44P2tkPmdxRVt5yTGVrZYVnHkEqZ2xUVgqTuZIjKhzCh2AIMng5DggH3h9WwN8KfFnykAv8Bi15NITtR5Lonebx64WdyBQdOnlvJO_-_fo9PC8QRPUln20YzGe_3A6smd_zm262m4x9N9YR8K45O2--_wHrhwpv
linkProvider ProQuest
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1bb9MwFD4aGxLsAdgYojDAD0NcpIjEcWPnAaFdOq3aVqppoL15ji9oEmq2poD4U_xGjh2n2yTgbQ-8VY1b5fL5O-c7sb8DsOHSCpmRucSawibM6ixRVuRJ5rK-K5ErS6pCswk-GomTk3K8AL-6vTB-WWXHiYGoTa19jfwdLTGyFJjf8w_nF4nvGuXfrnYtNFpY7NufP1CyNe-HO_h8X1K6Ozje3ktiV4FEY7CcJVQXqTPMIBANBlfhOH7m_UpVVe5SodO88h5nQlOLaC4KU1SVtroyvCpTi5yP_3sLlljOOM6rpa3BaHw0r-p4l02Rpe1GwDwvU_8eOvMaTYSWaJehL3QI-FscCMFt9_7_dlsewL2YRpPNFvcrsGAnq7B8xVxxFVYibTXkdfTWfvMQjtqNyS5WKkntyKBtBKTDwtzg332myTjYjk4aEhYLWkNmNTkMSgVVC_HFa7L98fNwJ8nKNfh0I1f6CBYn9cQ-BmIddSpjyqbKMM200l6hGqpQtFGdlT142z1xqaPnum_98VWi9vLwkFfg0YON-eDz1mrkz8O2PHTmQ7w_ePiinn6RkW5k4QxHqV4qIxxzmIRzxvp5oVCs5hyz3h688sCTnsXwhPDE280YeFneD0xucoG_wZna78H6tZHIPvr64Q6XMrJfIy9B-eTfh1_Anb3jwwN5MBztP4W7FBPGtry1Douz6Tf7DG7r77OzZvo8TjQCpzcN4t-_XGWR
linkToPdf http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Nb9NAEB2VFiE4AC0gAgX2UMSHZNVeb-z1AaHSNCIqDRaCqpyW_USVUFziAOKv8euYXa_TVgJuPXCL4k3kj7dv5o133wBsuVQhMzKXWFPYhFmdJdLyPMlcNnQVcmVFZWg2UU6n_OioqlfgV78Xxi-r7DkxELVptK-Rb9MKI0uB-X257eKyiHo0fnnyNfEdpPyb1r6dRgeRffvzB8q39sVkhM_6MaXjvfe7r5PYYSDRGDgXCdVF6gwzCEqDgZa7Ej-XQyWVyl3KdZor73fGNbWI7KIwhVLaamVKVaUW-R__9xKsYUrOcI6t1ZOD-uOywuMdN3mWdpsC87xK_TvpzOs1HtqjnYbB0C3gbzEhBLrxjf_5Ft2E6zG9JjvdfFiHFTvbgGtnTBc3YD3SWUueRs_tZ7fgXbdh2cUKJmkc2esaBOmwYDf4eh9rUgc70llLwiJCa8iiIQdBwaCaIb6oTXbfHk5GSVbdhg8XcqV3YHXWzOxdINZRJzMmbSoN00xL7ZWroRLFHNVZNYDn_dMXOnqx-5YgXwRqMg8VcQYqA9haDj7pLEj-POyVh9FyiPcND180888i0pAonClRwlfScMccJuclY8O8kChi8xKz4QE88SAUnt3whPDEu00aeFneJ0zslBx_gzN4OIDNcyORlfT5wz1GRWTFVpwC9N6_Dz-CK4hc8WYy3b8PVynmkV3VaxNWF_Nv9gFc1t8Xx-38YZxzBD5dNIZ_AzxeboM
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Identification+of+Electrocardiographic+Patterns+Related+to+Mortality+with+COVID-19&rft.jtitle=Applied+sciences&rft.au=Sbrollini%2C+Agnese&rft.au=Leoni%2C+Chiara&rft.au=Morettini%2C+Micaela&rft.au=Rivolta%2C+Massimo+W&rft.date=2024-01-01&rft.pub=MDPI+AG&rft.issn=2076-3417&rft.eissn=2076-3417&rft.volume=14&rft.issue=2&rft_id=info:doi/10.3390%2Fapp14020817&rft.externalDocID=A780871295
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2076-3417&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2076-3417&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2076-3417&client=summon