Automatic Cardiac Rhythm Classification with Concurrent Manual Chest Compressions

Electrocardiogram (EKG) based classification of out-of-hospital cardiac arrest (OHCA) rhythms is important to guide treatment and to retrospectively elucidate the effects of therapy on patient response. OHCA rhythms are grouped into five categories: ventricular fibrillation (VF) and tachycardia (VT)...

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Autores: Isasi Liñero, Iraia, Irusta Zarandona, Unai, Bahrami Rad, Ali, Aramendi Ecenarro, Elisabete, Zabihi, Morteza, Eftestøl, Trygve, Kramer-Johansen, Jo, Wik, Lars
Tipo de recurso: artículo
Fecha de publicación:2019
País:España
Institución:Universidad del País Vasco
Repositorio:Addi. Archivo Digital para la Docencia y la Investigación
OAI Identifier:oai:addi.ehu.eus:10810/65125
Acceso en línea:http://hdl.handle.net/10810/65125
Access Level:acceso abierto
Palabra clave:out-of-hospital cardiac arrest (OHCA)
electrocardiogram (EKG)
cardiopulmonary resuscitation (CPR)
adaptive filter
stationary wavelet transform (SWT)
random forest (RF) classifier
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spelling Automatic Cardiac Rhythm Classification with Concurrent Manual Chest CompressionsIsasi Liñero, IraiaIrusta Zarandona, UnaiBahrami Rad, AliAramendi Ecenarro, ElisabeteZabihi, MortezaEftestøl, TrygveKramer-Johansen, JoWik, Larsout-of-hospital cardiac arrest (OHCA)electrocardiogram (EKG)cardiopulmonary resuscitation (CPR)adaptive filterstationary wavelet transform (SWT)random forest (RF) classifierElectrocardiogram (EKG) based classification of out-of-hospital cardiac arrest (OHCA) rhythms is important to guide treatment and to retrospectively elucidate the effects of therapy on patient response. OHCA rhythms are grouped into five categories: ventricular fibrillation (VF) and tachycardia (VT), asystole (AS), pulseless electrical activity (PEA), and pulse-generating rhythms (PR). Clinically these rhythms are grouped into broader categories like shockable (VF/VT), non-shockable (AS/PEA/PR), or organized (ORG, PEA/PR). OHCA rhythm classification is further complicated because EKGs are corrupted by cardiopulmonary resuscitation (CPR) artifacts. The objective of this study was to demonstrate a framework for automatic multiclass OHCA rhythm classification in the presence of CPR artifacts. In total, 2133 EKG segments from 272 OHCA patients were used: 580 AS, 94 PR, 953 PEA, 479 VF, and 27 VT. CPR artifacts were adaptively filtered, 93 features were computed from the stationary wavelet transform analysis, and random forests were used for classification. A repeated stratified nested cross-validation procedure was used for feature selection, parameter tuning, and model assessment. Data were partitioned patient-wise. The classifiers were evaluated using per class sensitivity, and the unweighted mean of sensitivities (UMS) as a global performance metric. Four levels of clinical detail were studied: shock/no-shock, shock/AS/ORG, VF/VT/AS/ORG, and VF/VT/AS/PEA/PR. The median UMS (interdecile range) for the 2, 3, 4, and 5-class classifiers were: 95.4% (95.1-95.6), 87.6% (87.3-88.1), 80.6% (79.3-81.8), and 71.9% (69.5-74.6), respectively. For shock/no-shock decisions sensitivities were 93.5% (93.0-93.9) and 97.2% (97.0-97.4), meeting clinical standards for artifact-free EKG. The UMS for five classes with CPR artifacts was 5.8-points below that of the best algorithms without CPR artifacts, but improved the UMS of latter by over 19-points for EKG with CPR artifacts. A robust and accurate approach for multiclass OHCA rhythm classification during CPR has been demonstrated, improving the accuracy of the current state-of-the-art methodsThis work was supported by the Spanish Ministerio de Ciencia Innovación y Universidades through grant RTI2018-101475-B100, jointly with the Fondo Europeo de Desarrollo Regional (FEDER), and by the Basque Government through grants IT-1229-19 and pre-2018-2-0137.IEEE202420242019info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10810/65125reponame:Addi. Archivo Digital para la Docencia y la Investigacióninstname:Universidad del País VascoInglésinfo:eu-repo/grantAgreement/MICIU/RTI2018-101475-B100/https://ieeexplore.ieee.org/document/8795467info:eu-repo/semantics/openAccess(c) 2019 IEEEoai:addi.ehu.eus:10810/651252026-06-18T09:23:17Z
dc.title.none.fl_str_mv Automatic Cardiac Rhythm Classification with Concurrent Manual Chest Compressions
title Automatic Cardiac Rhythm Classification with Concurrent Manual Chest Compressions
spellingShingle Automatic Cardiac Rhythm Classification with Concurrent Manual Chest Compressions
Isasi Liñero, Iraia
out-of-hospital cardiac arrest (OHCA)
electrocardiogram (EKG)
cardiopulmonary resuscitation (CPR)
adaptive filter
stationary wavelet transform (SWT)
random forest (RF) classifier
title_short Automatic Cardiac Rhythm Classification with Concurrent Manual Chest Compressions
title_full Automatic Cardiac Rhythm Classification with Concurrent Manual Chest Compressions
title_fullStr Automatic Cardiac Rhythm Classification with Concurrent Manual Chest Compressions
title_full_unstemmed Automatic Cardiac Rhythm Classification with Concurrent Manual Chest Compressions
title_sort Automatic Cardiac Rhythm Classification with Concurrent Manual Chest Compressions
dc.creator.none.fl_str_mv Isasi Liñero, Iraia
Irusta Zarandona, Unai
Bahrami Rad, Ali
Aramendi Ecenarro, Elisabete
Zabihi, Morteza
Eftestøl, Trygve
Kramer-Johansen, Jo
Wik, Lars
author Isasi Liñero, Iraia
author_facet Isasi Liñero, Iraia
Irusta Zarandona, Unai
Bahrami Rad, Ali
Aramendi Ecenarro, Elisabete
Zabihi, Morteza
Eftestøl, Trygve
Kramer-Johansen, Jo
Wik, Lars
author_role author
author2 Irusta Zarandona, Unai
Bahrami Rad, Ali
Aramendi Ecenarro, Elisabete
Zabihi, Morteza
Eftestøl, Trygve
Kramer-Johansen, Jo
Wik, Lars
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv out-of-hospital cardiac arrest (OHCA)
electrocardiogram (EKG)
cardiopulmonary resuscitation (CPR)
adaptive filter
stationary wavelet transform (SWT)
random forest (RF) classifier
topic out-of-hospital cardiac arrest (OHCA)
electrocardiogram (EKG)
cardiopulmonary resuscitation (CPR)
adaptive filter
stationary wavelet transform (SWT)
random forest (RF) classifier
description Electrocardiogram (EKG) based classification of out-of-hospital cardiac arrest (OHCA) rhythms is important to guide treatment and to retrospectively elucidate the effects of therapy on patient response. OHCA rhythms are grouped into five categories: ventricular fibrillation (VF) and tachycardia (VT), asystole (AS), pulseless electrical activity (PEA), and pulse-generating rhythms (PR). Clinically these rhythms are grouped into broader categories like shockable (VF/VT), non-shockable (AS/PEA/PR), or organized (ORG, PEA/PR). OHCA rhythm classification is further complicated because EKGs are corrupted by cardiopulmonary resuscitation (CPR) artifacts. The objective of this study was to demonstrate a framework for automatic multiclass OHCA rhythm classification in the presence of CPR artifacts. In total, 2133 EKG segments from 272 OHCA patients were used: 580 AS, 94 PR, 953 PEA, 479 VF, and 27 VT. CPR artifacts were adaptively filtered, 93 features were computed from the stationary wavelet transform analysis, and random forests were used for classification. A repeated stratified nested cross-validation procedure was used for feature selection, parameter tuning, and model assessment. Data were partitioned patient-wise. The classifiers were evaluated using per class sensitivity, and the unweighted mean of sensitivities (UMS) as a global performance metric. Four levels of clinical detail were studied: shock/no-shock, shock/AS/ORG, VF/VT/AS/ORG, and VF/VT/AS/PEA/PR. The median UMS (interdecile range) for the 2, 3, 4, and 5-class classifiers were: 95.4% (95.1-95.6), 87.6% (87.3-88.1), 80.6% (79.3-81.8), and 71.9% (69.5-74.6), respectively. For shock/no-shock decisions sensitivities were 93.5% (93.0-93.9) and 97.2% (97.0-97.4), meeting clinical standards for artifact-free EKG. The UMS for five classes with CPR artifacts was 5.8-points below that of the best algorithms without CPR artifacts, but improved the UMS of latter by over 19-points for EKG with CPR artifacts. A robust and accurate approach for multiclass OHCA rhythm classification during CPR has been demonstrated, improving the accuracy of the current state-of-the-art methods
publishDate 2019
dc.date.none.fl_str_mv 2019
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10810/65125
url http://hdl.handle.net/10810/65125
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/MICIU/RTI2018-101475-B100/
https://ieeexplore.ieee.org/document/8795467
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
(c) 2019 IEEE
eu_rights_str_mv openAccess
rights_invalid_str_mv (c) 2019 IEEE
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv IEEE
publisher.none.fl_str_mv IEEE
dc.source.none.fl_str_mv reponame:Addi. Archivo Digital para la Docencia y la Investigación
instname:Universidad del País Vasco
instname_str Universidad del País Vasco
reponame_str Addi. Archivo Digital para la Docencia y la Investigación
collection Addi. Archivo Digital para la Docencia y la Investigación
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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