Análisis de nuevos índices de las señales electrocardiográficas y de flujo respiratorio para predecir el éxito o fracaso del proceso de extubación de pacientes asistidos por ventilación mecánica
La insuficiencia respiratoria aguda (IRA) es una afección en la que los pulmones no pueden realizar un intercambio adecuado de gases, lo que frecuentemente requiere el uso de ventilación mecánica (VM). El proceso de extubación, o retiro de la ventilación, es delicado y debe realizarse en el momento...
- Autores:
-
González Acevedo, Hernando
- Tipo de recurso:
- Doctoral thesis
- Fecha de publicación:
- 2025
- Institución:
- Universidad Autónoma de Bucaramanga - UNAB
- Repositorio:
- Repositorio UNAB
- Idioma:
- spa
- OAI Identifier:
- oai:repository.unab.edu.co:20.500.12749/29727
- Acceso en línea:
- http://hdl.handle.net/20.500.12749/29727
- Palabra clave:
- Engineering
Mechanical ventilation
Spontaneous breathing test
Time-frequency analysis
Poincaré diagram
Machine learning
Deep learning
Neural networks (Computer science)
Signal processing
Patient monitoring
Vital signs
Artificial respiration (Equipment and supplies)
Respirators (Medical Equipment)
Ingeniería
Redes neuronales (Computadores)
Procesamiento de señales
Monitoreo del paciente
Signos vitales
Respiración artificial (Equipo y accesorios)
Respiradores (Equipo médico)
Ventilación mecánica
Prueba de respiración espontánea
Análisis tiempo-frecuencia
Diagrama de poincaré
- Rights
- License
- http://creativecommons.org/licenses/by-nc-nd/2.5/co/
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| dc.title.spa.fl_str_mv |
Análisis de nuevos índices de las señales electrocardiográficas y de flujo respiratorio para predecir el éxito o fracaso del proceso de extubación de pacientes asistidos por ventilación mecánica |
| dc.title.translated.spa.fl_str_mv |
Analysis of new indices of electrocardiographic and respiratory flow signals to predict the success or failure of the extubation process in mechanically ventilated patients |
| title |
Análisis de nuevos índices de las señales electrocardiográficas y de flujo respiratorio para predecir el éxito o fracaso del proceso de extubación de pacientes asistidos por ventilación mecánica |
| spellingShingle |
Análisis de nuevos índices de las señales electrocardiográficas y de flujo respiratorio para predecir el éxito o fracaso del proceso de extubación de pacientes asistidos por ventilación mecánica Engineering Mechanical ventilation Spontaneous breathing test Time-frequency analysis Poincaré diagram Machine learning Deep learning Neural networks (Computer science) Signal processing Patient monitoring Vital signs Artificial respiration (Equipment and supplies) Respirators (Medical Equipment) Ingeniería Redes neuronales (Computadores) Procesamiento de señales Monitoreo del paciente Signos vitales Respiración artificial (Equipo y accesorios) Respiradores (Equipo médico) Ventilación mecánica Prueba de respiración espontánea Análisis tiempo-frecuencia Diagrama de poincaré |
| title_short |
Análisis de nuevos índices de las señales electrocardiográficas y de flujo respiratorio para predecir el éxito o fracaso del proceso de extubación de pacientes asistidos por ventilación mecánica |
| title_full |
Análisis de nuevos índices de las señales electrocardiográficas y de flujo respiratorio para predecir el éxito o fracaso del proceso de extubación de pacientes asistidos por ventilación mecánica |
| title_fullStr |
Análisis de nuevos índices de las señales electrocardiográficas y de flujo respiratorio para predecir el éxito o fracaso del proceso de extubación de pacientes asistidos por ventilación mecánica |
| title_full_unstemmed |
Análisis de nuevos índices de las señales electrocardiográficas y de flujo respiratorio para predecir el éxito o fracaso del proceso de extubación de pacientes asistidos por ventilación mecánica |
| title_sort |
Análisis de nuevos índices de las señales electrocardiográficas y de flujo respiratorio para predecir el éxito o fracaso del proceso de extubación de pacientes asistidos por ventilación mecánica |
| dc.creator.fl_str_mv |
González Acevedo, Hernando |
| dc.contributor.advisor.none.fl_str_mv |
Arizmendi Pereira, Carlos Julio Giraldo Giraldo, Beatriz |
| dc.contributor.author.none.fl_str_mv |
González Acevedo, Hernando |
| dc.contributor.cvlac.spa.fl_str_mv |
González Acevedo, Hernando [0000544655] Arizmendi Pereira, Carlos Julio [1381550] |
| dc.contributor.googlescholar.spa.fl_str_mv |
González Acevedo, Hernando [V8tga0cAAAAJ] Arizmendi Pereira, Carlos Julio [JgT_je0AAAAJ] |
| dc.contributor.orcid.spa.fl_str_mv |
González Acevedo, Hernando [0000-0001-6242-3939] Giraldo Giraldo, Beatriz [0000-0002-9910-8577] |
| dc.contributor.scopus.spa.fl_str_mv |
González Acevedo, Hernando [55821231500] |
| dc.contributor.researchgate.spa.fl_str_mv |
González Acevedo, Hernando [Hernando-Gonzalez] Arizmendi Pereira, Carlos Julio [Carlos_Arizmendi2] |
| dc.contributor.researchgroup.spa.fl_str_mv |
Grupo de Investigación Control y Mecatrónica - GICYM |
| dc.contributor.apolounab.spa.fl_str_mv |
Gonzalez Acevedo, Hernando [hernando-gonzalez-acevedo-2] Arizmendi Pereira, Carlos Julio [carlos-julio-arizmendi-pereira] |
| dc.subject.keywords.spa.fl_str_mv |
Engineering Mechanical ventilation Spontaneous breathing test Time-frequency analysis Poincaré diagram Machine learning Deep learning Neural networks (Computer science) Signal processing Patient monitoring Vital signs Artificial respiration (Equipment and supplies) Respirators (Medical Equipment) |
| topic |
Engineering Mechanical ventilation Spontaneous breathing test Time-frequency analysis Poincaré diagram Machine learning Deep learning Neural networks (Computer science) Signal processing Patient monitoring Vital signs Artificial respiration (Equipment and supplies) Respirators (Medical Equipment) Ingeniería Redes neuronales (Computadores) Procesamiento de señales Monitoreo del paciente Signos vitales Respiración artificial (Equipo y accesorios) Respiradores (Equipo médico) Ventilación mecánica Prueba de respiración espontánea Análisis tiempo-frecuencia Diagrama de poincaré |
| dc.subject.lemb.spa.fl_str_mv |
Ingeniería Redes neuronales (Computadores) Procesamiento de señales Monitoreo del paciente Signos vitales Respiración artificial (Equipo y accesorios) Respiradores (Equipo médico) |
| dc.subject.proposal.spa.fl_str_mv |
Ventilación mecánica Prueba de respiración espontánea Análisis tiempo-frecuencia Diagrama de poincaré |
| description |
La insuficiencia respiratoria aguda (IRA) es una afección en la que los pulmones no pueden realizar un intercambio adecuado de gases, lo que frecuentemente requiere el uso de ventilación mecánica (VM). El proceso de extubación, o retiro de la ventilación, es delicado y debe realizarse en el momento adecuado para evitar complicaciones. Hasta un 25% de los pacientes reintubados, tras una extubación fallida, enfrentan riesgos mayores, como infecciones nosocomiales y atrofia muscular. Dado el impacto de una extubación fallida en los resultados clínicos, surge la necesidad de desarrollar herramientas más precisas para predecir el éxito del destete. El objetivo de esta tesis es proponer nuevos índices basados en señales electrocardiográficas y de flujo respiratorio para mejorar la predicción del éxito o fracaso de la extubación tras una Prueba de Respiración Espontánea (SBT, por sus siglas en inglés Spontaneous Breathing Trial). Para ello, se analizan descriptores extraídos en el dominio del tiempo, frecuencia, diagramas de Poincaré y tiempo-frecuencia, con el fin de caracterizar la dinámica cardiorrespiratoria durante la extubación. Además, se emplean técnicas de procesamiento de señales y algoritmos de clasificación basados en aprendizaje automático (ML, por sus siglas en ingles Machine Learning) y aprendizaje profundo (DL, por sus siglas en inglés Deep Learning) para optimizar la predicción del desenlace del procedimiento. Los índices propuestos constituyen una herramienta de apoyo en entornos clínicos, como soporte a decisiones más objetivas e informadas en el proceso de destete de la VM. |
| publishDate |
2025 |
| dc.date.accessioned.none.fl_str_mv |
2025-06-13T16:34:35Z |
| dc.date.available.none.fl_str_mv |
2025-06-13T16:34:35Z |
| dc.date.issued.none.fl_str_mv |
2025-06-11 |
| dc.type.eng.fl_str_mv |
Thesis |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
| dc.type.local.spa.fl_str_mv |
Tesis |
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http://purl.org/coar/resource_type/c_db06 |
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info:eu-repo/semantics/acceptedVersion |
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http://purl.org/redcol/resource_type/TD |
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http://purl.org/coar/resource_type/c_db06 |
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acceptedVersion |
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http://hdl.handle.net/20.500.12749/29727 |
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instname:Universidad Autónoma de Bucaramanga - UNAB |
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reponame:Repositorio Institucional UNAB |
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repourl:https://repository.unab.edu.co |
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http://hdl.handle.net/20.500.12749/29727 |
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| dc.relation.references.spa.fl_str_mv |
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Giraldo, “Development of a Deep Learning Model for the Prediction of Ventilator Weaning,” International journal of online and biomedical engineering, vol. 20, no. 11, pp. 161–178, Aug. 2024, doi: 10.3991/ijoe.v20i11.49453 Z. Xu et al., “Pathological findings of COVID-19 associated with acute respiratory distress syndrome,” Lancet Respir Med, vol. 8, no. 4, 2020, doi: 10.1016/S2213-2600(20)30076-X. |
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Arizmendi Pereira, Carlos Julio79e0125f-b191-4144-999b-281177ddaaf9Giraldo Giraldo, Beatrizf065668e-e27a-45e4-9e6f-251c3517bdc8González Acevedo, Hernandoa0391c07-7794-4d89-8b62-bd75fbb4534fGonzález Acevedo, Hernando [0000544655]Arizmendi Pereira, Carlos Julio [1381550]González Acevedo, Hernando [V8tga0cAAAAJ]Arizmendi Pereira, Carlos Julio [JgT_je0AAAAJ]González Acevedo, Hernando [0000-0001-6242-3939]Giraldo Giraldo, Beatriz [0000-0002-9910-8577]González Acevedo, Hernando [55821231500]González Acevedo, Hernando [Hernando-Gonzalez]Arizmendi Pereira, Carlos Julio [Carlos_Arizmendi2]Grupo de Investigación Control y Mecatrónica - GICYMGonzalez Acevedo, Hernando [hernando-gonzalez-acevedo-2]Arizmendi Pereira, Carlos Julio [carlos-julio-arizmendi-pereira]ColombiaUNAB Campus Bucaramanga2025-06-13T16:34:35Z2025-06-13T16:34:35Z2025-06-11http://hdl.handle.net/20.500.12749/29727instname:Universidad Autónoma de Bucaramanga - UNABreponame:Repositorio Institucional UNABrepourl:https://repository.unab.edu.coLa insuficiencia respiratoria aguda (IRA) es una afección en la que los pulmones no pueden realizar un intercambio adecuado de gases, lo que frecuentemente requiere el uso de ventilación mecánica (VM). El proceso de extubación, o retiro de la ventilación, es delicado y debe realizarse en el momento adecuado para evitar complicaciones. Hasta un 25% de los pacientes reintubados, tras una extubación fallida, enfrentan riesgos mayores, como infecciones nosocomiales y atrofia muscular. Dado el impacto de una extubación fallida en los resultados clínicos, surge la necesidad de desarrollar herramientas más precisas para predecir el éxito del destete. El objetivo de esta tesis es proponer nuevos índices basados en señales electrocardiográficas y de flujo respiratorio para mejorar la predicción del éxito o fracaso de la extubación tras una Prueba de Respiración Espontánea (SBT, por sus siglas en inglés Spontaneous Breathing Trial). Para ello, se analizan descriptores extraídos en el dominio del tiempo, frecuencia, diagramas de Poincaré y tiempo-frecuencia, con el fin de caracterizar la dinámica cardiorrespiratoria durante la extubación. Además, se emplean técnicas de procesamiento de señales y algoritmos de clasificación basados en aprendizaje automático (ML, por sus siglas en ingles Machine Learning) y aprendizaje profundo (DL, por sus siglas en inglés Deep Learning) para optimizar la predicción del desenlace del procedimiento. Los índices propuestos constituyen una herramienta de apoyo en entornos clínicos, como soporte a decisiones más objetivas e informadas en el proceso de destete de la VM.Programa de Becas de Excelencia Doctoral del BicentenarioINTRODUCCIÓN 15 1. FUNDAMENTACIÓN DE LA INVESTIGACIÓN 17 1.1 PROBLEMA DE INVESTIGACIÓN 17 1.2 PREGUNTA DE INVESTIGACIÓN 18 1.3 HIPÓTESIS DE INVESTIGACIÓN 18 1.4 JUSTIFICACIÓN DE LA INVESTIGACIÓN 19 1.5 ESTADO DEL ARTE 20 2. OBJETIVOS DE LA TESIS 24 2.1 OBJETIVO PRINCIPAL 24 2.2 OBJETIVOS ESPECÍFICOS 24 3. MARCO TEÓRICO 25 3.1 SISTEMA CARDIOVASCULAR 25 3.2 SISTEMA RESPIRATORIO 25 3.3 INSUFICIENCIA RESPIRATORIA AGUDA 28 3.4 VENTILACIÓN MECÁNICA 29 3.5 BASE DE DATOS WEANDB 32 3.6 MARCO NORMATIVO 33 3.7 BENCHMARKING DE EMPRESAS Y TECNOLOGÍAS 34 4. DESCRIPTORES PARA PREDECIR EL ÉXITO O FRACASO DE UN PROCESO DE EXTUBACIÓN 36 4.1 TECNICAS DE INTELIGENCIA ARTIFICIAL IMPLEMENTADAS PARA LA PREDICCIÓN DE LA EXTUBACIÓN 36 4.1.1 Preprocesamiento de la base de datos 36 4.1.2 Preparación de los datos 37 4.1.3 Sistema de clasificación 40 4.2 PROCESAMIENTO DE SEÑALES DE FLUJO RESPIRATORIO Y ELECTROCARDIOGRÁFICAS 45 4.3 SELECCIÓN DE CARACTERÍSTICAS PARA UN SISTEMA DE CLASIFICACIÓN A PARTIR DE DATOS ESTADÍSTICOS DE LAS SERIES TEMPORALES 46 4.4 SELECCIÓN DE CARACTERÍSTICAS PARA UN SISTEMA DE CLASIFICACIÓN A PARTIR DE UN ANÁLISIS EN FRECUENCIA UTILIZANDO LA TRANSFORMADA DE FOURIER NO UNIFORME 48 4.4.1 Características en el dominio de la frecuencia 50 4.4.2 Sistema de clasificación 53 4.5 PREDICCIÓN DEL ÉXITO DE LA RETIRADA DEL RESPIRADOR EN PACIENTES MEDIANTE DIAGRAMA DE POINCARÉ 54 4.6 PREDICCIÓN DEL FRACASO DEL DESTETE MEDIANTE ANÁLISIS DE TIEMPO-FRECUENCIA DE SEÑALES ELECTROCARDIOGRÁFICAS Y DE FLUJO RESPIRATORIO 64 4.6.1 Análisis tiempo-frecuencia: Transformada de Wavelet 67 4.6.2 Sistema de clasificación 70 5. RESULTADOS 72 5.1 MODELO DE CLASIFICACIÓN BASADO EN DATOS ESTADÍSTICOS DE LAS SERIES TEMPORALES 72 5.2 MODELO DE CLASIFICACIÓN BASADO EN CARACTERÍSTICAS DEL ESPECTRO EN FRECUENCIA UTILIZANDO LA TRANSFORMADA DE FOURIER NO UNIFORME 74 5.3 MODELO DE CLASIFICACIÓN BASADO EN CARACTERÍSTICAS DEL DIAGRAMA DE POINCARÉ 75 5.4 MODELO DE CLASIFICACIÓN BASADO EN CARACTERÍSTICAS DEL DIAGRAMA TIEMPO-FRECUENCIA OBTENIDO MEDIANTE LA TRANSFORMADA DE FOURIER NO UNIFORME 78 5.5 MODELO DE CLASIFICACIÓN BASADO EN CARACTERÍSTICAS DEL DIAGRAMA TIEMPO-FRECUENCIA OBTENIDO MEDIANTE LA TRANSFORMADA DE WAVELET 81 5.6 ANALISIS ESTADÍSTICO 85 6. CONCLUSIONES 93 7. RECOMENDACIONES 95 8. REFERENCIAS BIBLIOGRÁFICAS 96 9. ANEXOS 106DoctoradoAcute respiratory failure (ARF) is a condition in which the lungs are unable to perform adequate gas exchange, often necessitating the use of mechanical ventilation (MV). The extubation process, or weaning from ventilation, is delicate and must be performed at the appropriate time to avoid complications. Up to 25% of reintubated patients, after failed extubation, face increased risks, such as nosocomial infections and muscle atrophy. Given the impact of failed extubation on clinical outcomes, there is a need for more accurate tools to predict weaning success. The aim of this thesis is to propose new indexes based on electrocardiographic and respiratory flow signals to improve the prediction of extubation success or failure after a Spontaneous Breathing Trial (SBT). For this purpose, descriptors extracted in the time, frequency, Poincaré diagrams and time-frequency domain are analyzed to characterize cardiorespiratory dynamics during extubation. In addition, signal processing techniques and classification algorithms based on machine learning (ML) and deep learning (DL) are used to optimize the prediction of the outcome of the procedure. The proposed indexes constitute a support tool in the clinical setting, as support for more objective and informed decision making in the MV weaning process.Modalidad Presencialapplication/pdfspahttp://creativecommons.org/licenses/by-nc-nd/2.5/co/Abierto (Texto Completo)Atribución-NoComercial-SinDerivadas 2.5 Colombiahttp://purl.org/coar/access_right/c_abf2Análisis de nuevos índices de las señales electrocardiográficas y de flujo respiratorio para predecir el éxito o fracaso del proceso de extubación de pacientes asistidos por ventilación mecánicaAnalysis of new indices of electrocardiographic and respiratory flow signals to predict the success or failure of the extubation process in mechanically ventilated patientsThesisinfo:eu-repo/semantics/doctoralThesisTesishttp://purl.org/coar/resource_type/c_db06info:eu-repo/semantics/acceptedVersionhttp://purl.org/redcol/resource_type/TDDoctorado en IngenieríaUniversidad Autónoma de Bucaramanga UNABFacultad IngenieríaDoctorado en IngenieríaDING-1502EngineeringMechanical ventilationSpontaneous breathing testTime-frequency analysisPoincaré diagramMachine learningDeep learningNeural networks (Computer science)Signal processingPatient monitoringVital signsArtificial respiration (Equipment and supplies)Respirators (Medical Equipment)IngenieríaRedes neuronales (Computadores)Procesamiento de señalesMonitoreo del pacienteSignos vitalesRespiración artificial (Equipo y accesorios)Respiradores (Equipo médico)Ventilación mecánicaPrueba de respiración espontáneaAnálisis tiempo-frecuenciaDiagrama de poincaréL. D. J. Bos and L. B. Ware, “Acute respiratory distress syndrome: causes, pathophysiology, and phenotypes,” 2022. doi: 10.1016/S0140-6736(22)01485-4N. Rittayamai and L. Brochard, “Recent advances in mechanical ventilation in patients with acute respiratory distress syndrome,” European Respiratory Review, vol. 24, no. 135, 2015, doi: 10.1183/09059180.00012414S. M. Sison, G. K. Sivakumar, C. Caufield-Noll, W. B. Greenough, E. S. Oh, and P. Galiatsatos, “Mortality outcomes of patients on chronic mechanical ventilation in different care settings: A systematic review,” 2021. doi: 10.1016/j.heliyon.2021.e06230W. Windisch et al., “Prolonged weaning from mechanical ventilation results from specialized weaning: Centers—a registry-based study from the WeanNet initiative,” Dtsch Arztebl Int, vol. 117, no. 12, 2020, doi: 10.3238/arztebl.2020.0197F. C. Trudzinski et al., “Risk Factors for Prolonged Mechanical Ventilation and Weaning Failure: A Systematic Review,” Respiration, vol. 101, no. 10, 2022, doi: 10.1159/000525604M. Mezidi et al., “Pressure support and positive end-expiratory pressure versus T-piece during spontaneous breathing trial in difficult weaning from mechanical ventilation: study protocol for the SBT-ICU study,” Trials, vol. 23, no. 1, 2022, doi: 10.1186/s13063-022-06896-4S. S. Batah and A. T. Fabro, “Pulmonary pathology of ARDS in COVID-19: A pathological review for clinicians,” 2021. doi: 10.1016/j.rmed.2020.106239P. C. Hsu et al., “Risk factors for prolonged mechanical ventilation in critically ill patients with influenza-related acute respiratory distress syndrome,” Respir Res, vol. 25, no. 1, 2024, doi: 10.1186/s12931-023-02648-3P. Vignon, “Cardiopulmonary interactions during ventilator weaning,” 2023. doi: 10.3389/fphys.2023.1275100P. Armañac-Julián et al., “Cardiopulmonary coupling indices to assess weaning readiness from mechanical ventilation,” Sci Rep, vol. 11, no. 1, 2021, doi: 10.1038/s41598-021-95282-2P. Manickam et al., “Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare,” 2022. doi: 10.3390/bios12080562J. E. Daza-Arana, H. Lozada-Ramos, D. F. Ávila-Hernández, L. T. Ordoñez-Mora, and D. P. Sánchez, “Prolonged Mechanical Ventilation Following Coronary Artery Bypass Graft in Santiago De Cali, Colombia,” Vasc Health Risk Manag, vol. 18, 2022, doi: 10.2147/VHRM.S367108K. Kaier, T. Heister, J. Wolff, and M. Wolkewitz, “Mechanical ventilation and the daily cost of ICU care,” BMC Health Serv Res, vol. 20, no. 1, 2020, doi: 10.1186/s12913-020-05133-5H. Yadav, B. T. Thompson, and O. Gajic, “Is acute respiratory distress syndrome a preventable disease?,” 2017. doi: 10.1164/rccm.201609-1767CIG. Bellani et al., “Epidemiology, patterns of care, and mortality for patients with acute respiratory distress syndrome in intensive care units in 50 countries,” JAMA - Journal of the American Medical Association, vol. 315, no. 8, 2016, doi: 10.1001/jama.2016.0291K. E. A. Burns et al., “Ventilator Weaning and Discontinuation Practices for Critically Ill Patients,” in JAMA - Journal of the American Medical Association, 2021. doi: 10.1001/jama.2021.2384L. Jiebing et al., “Influencing factors of weaning outcome of intensive care unit patients with planned extubation,” Chinese Critical Care Medicine, vol. 33, no. 5, 2021, doi: 10.3760/cma.j.cn121430-20200410-00280A. W. Thille, F. Boissier, H. Ben Ghezala, K. Razazi, A. Mekontso-Dessap, and C. Brun-Buisson, “Risk factors for and prediction by caregivers of extubation failure in ICU patients: A prospective study,” Crit Care Med, vol. 43, no. 3, 2015, doi: 10.1097/CCM.0000000000000748L. Rose, M. J. Schultz, C. R. Cardwell, P. Jouvet, D. F. 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