Aplicación de registro y reporte automático de parámetros fisiológicos para la trazabilidad psicofisiológica en personas post COVID-19

El presente trabajo hace parte de un proyecto general que busca desarrollar una hermaienta para identificar las secuelas neuropsicológicas y sobre el sistema nervioso autónomo en pacientes que se han recuperado de COVID-19. El objetivo del presente trabajo fue desarrollar una aplicación de escritori...

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Autores:: Moreno Escobar, Juan Sebastián

Tipo de recurso:: Trabajo de grado de pregrado

Fecha de publicación:: 2021

Institución:: Escuela Colombiana de Ingeniería Julio Garavito

Repositorio:: Repositorio Institucional ECI

Idioma:: spa

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network_acronym_str	ESCUELAIG2
network_name_str	Repositorio Institucional ECI
repository_id_str
dc.title.spa.fl_str_mv	Aplicación de registro y reporte automático de parámetros fisiológicos para la trazabilidad psicofisiológica en personas post COVID-19
title	Aplicación de registro y reporte automático de parámetros fisiológicos para la trazabilidad psicofisiológica en personas post COVID-19
spellingShingle	Aplicación de registro y reporte automático de parámetros fisiológicos para la trazabilidad psicofisiológica en personas post COVID-19 Procesamiento de señales Sofware Psicofisiología Trazabilidad Ingeniería Biomédica Procesamiento de señales COVID-19 Psicofisiología Sofware Signal processing Software COVID-19 Psychophysiology
title_short	Aplicación de registro y reporte automático de parámetros fisiológicos para la trazabilidad psicofisiológica en personas post COVID-19
title_full	Aplicación de registro y reporte automático de parámetros fisiológicos para la trazabilidad psicofisiológica en personas post COVID-19
title_fullStr	Aplicación de registro y reporte automático de parámetros fisiológicos para la trazabilidad psicofisiológica en personas post COVID-19
title_full_unstemmed	Aplicación de registro y reporte automático de parámetros fisiológicos para la trazabilidad psicofisiológica en personas post COVID-19
title_sort	Aplicación de registro y reporte automático de parámetros fisiológicos para la trazabilidad psicofisiológica en personas post COVID-19
dc.creator.fl_str_mv	Moreno Escobar, Juan Sebastián
dc.contributor.advisor.none.fl_str_mv	Cifuentes García, Carlos Andrés
dc.contributor.author.none.fl_str_mv	Moreno Escobar, Juan Sebastián
dc.subject.armarc.none.fl_str_mv	Procesamiento de señales Sofware Psicofisiología Trazabilidad Ingeniería Biomédica
topic	Procesamiento de señales Sofware Psicofisiología Trazabilidad Ingeniería Biomédica Procesamiento de señales COVID-19 Psicofisiología Sofware Signal processing Software COVID-19 Psychophysiology
dc.subject.proposal.spa.fl_str_mv	Procesamiento de señales COVID-19 Psicofisiología Sofware
dc.subject.proposal.eng.fl_str_mv	Signal processing Software COVID-19 Psychophysiology
description	El presente trabajo hace parte de un proyecto general que busca desarrollar una hermaienta para identificar las secuelas neuropsicológicas y sobre el sistema nervioso autónomo en pacientes que se han recuperado de COVID-19. El objetivo del presente trabajo fue desarrollar una aplicación de escritorio que permita un seguimiento clínico instrumentado de las variables psicofisiológicas en múltiples pacientes. La aplicación se desarrolló en lenguaje Python 3.8, con base en los requerimientos acordados por los colaboradores del proyecto general. Se definieron 6 objetos, para procesar 7 bioseñales: electrocardiografía (ECG), respiración (RESP), actividad electrodérmica (EDA), temperatura (TEMP), espectroscopia en el infrarrojo cercano (fNIRS), fotopletismografía (PPG), y electromiografía (EMG). Para cada objeto se defieneron diferentes métodos con el fin de extraer características de relevancia psicofisiológica desde las señales: ECG (frecuencia cardiaca y variabilidad de la frecuencia cardiaca), RESP (frecuencia respiratoria), EDA (conductancia basal, amplitud, tiempo de elevación y tiempo de recuperación media), TEMP (máxima, media y mínima), fNIRS (saturación de oxígeno), PPG (frecuencia cardiaca y variabilidad de la frecuencia cardiaca) y EMG (media del valor RMS). Se desarrolló la interfaz de usuario con 3 ventanas: la ventana principal que permite registrar y visualizar los pacientes junto con sus datos, una ventana para adicionar medidas (adquisiciones de bioseñales, lecturas de signos vitales y pruebas psicológicas) sobre los pacientes, y una ventana para observar dichas medidas. Se validó la interfaz procesando las señales adquiridas de un voluntario sano, y particularmente se validó la detección de complejos QRS en el procesamiento de ECG, y la detección de respiraciones en el procesamiento de RESP. Se logró almacenar correctamente la información del voluntario y se extrajeron satisfactoriamente las características de las bioseñales adquiridas. Por otro lado, la detección de complejos QRS en ECG obtuvo una sensibilidad del 97.50 %, una precisión del 99.00 % y un valor-F1 del 98.24 % en la base de datos MIT/BIH Arrhythmia (n=109461 latidos). La detección de respiraciones obtuvo una sensibilidad del 94.78 %, precisión del 92.02 % y valor-F1 del 93.38 % en la base de datos BDIMC (n=7381 respiraciones)
publishDate	2021
dc.date.accessioned.none.fl_str_mv	2021-10-20T02:16:52Z
dc.date.available.none.fl_str_mv	2021-10-20T02:16:52Z
dc.date.issued.none.fl_str_mv	2021
dc.type.spa.fl_str_mv	Trabajo de grado - Pregrado
dc.type.coarversion.fl_str_mv	http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/publishedVersion
dc.type.coar.spa.fl_str_mv	http://purl.org/coar/resource_type/c_7a1f
dc.type.content.spa.fl_str_mv	Text
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/bachelorThesis
dc.type.redcol.spa.fl_str_mv	https://purl.org/redcol/resource_type/TP
format	http://purl.org/coar/resource_type/c_7a1f
status_str	publishedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.escuelaing.edu.co/handle/001/1741
dc.identifier.url.none.fl_str_mv	https://catalogo.escuelaing.edu.co/cgi-bin/koha/opac-detail.pl?biblionumber=22759
url	https://repositorio.escuelaing.edu.co/handle/001/1741 https://catalogo.escuelaing.edu.co/cgi-bin/koha/opac-detail.pl?biblionumber=22759
dc.language.iso.spa.fl_str_mv	spa
language	spa
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spelling	Cifuentes García, Carlos Andrésac39f7ea8b4745156739c85c7425fde0Moreno Escobar, Juan Sebastián9b24eb87915c4e45c3f49ecd837ce9fd6002021-10-20T02:16:52Z2021-10-20T02:16:52Z2021https://repositorio.escuelaing.edu.co/handle/001/1741https://catalogo.escuelaing.edu.co/cgi-bin/koha/opac-detail.pl?biblionumber=22759El presente trabajo hace parte de un proyecto general que busca desarrollar una hermaienta para identificar las secuelas neuropsicológicas y sobre el sistema nervioso autónomo en pacientes que se han recuperado de COVID-19. El objetivo del presente trabajo fue desarrollar una aplicación de escritorio que permita un seguimiento clínico instrumentado de las variables psicofisiológicas en múltiples pacientes. La aplicación se desarrolló en lenguaje Python 3.8, con base en los requerimientos acordados por los colaboradores del proyecto general. Se definieron 6 objetos, para procesar 7 bioseñales: electrocardiografía (ECG), respiración (RESP), actividad electrodérmica (EDA), temperatura (TEMP), espectroscopia en el infrarrojo cercano (fNIRS), fotopletismografía (PPG), y electromiografía (EMG). Para cada objeto se defieneron diferentes métodos con el fin de extraer características de relevancia psicofisiológica desde las señales: ECG (frecuencia cardiaca y variabilidad de la frecuencia cardiaca), RESP (frecuencia respiratoria), EDA (conductancia basal, amplitud, tiempo de elevación y tiempo de recuperación media), TEMP (máxima, media y mínima), fNIRS (saturación de oxígeno), PPG (frecuencia cardiaca y variabilidad de la frecuencia cardiaca) y EMG (media del valor RMS). Se desarrolló la interfaz de usuario con 3 ventanas: la ventana principal que permite registrar y visualizar los pacientes junto con sus datos, una ventana para adicionar medidas (adquisiciones de bioseñales, lecturas de signos vitales y pruebas psicológicas) sobre los pacientes, y una ventana para observar dichas medidas. Se validó la interfaz procesando las señales adquiridas de un voluntario sano, y particularmente se validó la detección de complejos QRS en el procesamiento de ECG, y la detección de respiraciones en el procesamiento de RESP. Se logró almacenar correctamente la información del voluntario y se extrajeron satisfactoriamente las características de las bioseñales adquiridas. Por otro lado, la detección de complejos QRS en ECG obtuvo una sensibilidad del 97.50 %, una precisión del 99.00 % y un valor-F1 del 98.24 % en la base de datos MIT/BIH Arrhythmia (n=109461 latidos). La detección de respiraciones obtuvo una sensibilidad del 94.78 %, precisión del 92.02 % y valor-F1 del 93.38 % en la base de datos BDIMC (n=7381 respiraciones)The present work is part of an overall project to develop a tool to identify neuropsychological and autonomic nervous system sequelae in patients who have recovered from COVID-19. The objective of the present work was to develop a desktop application that allows clinically instrumented monitoring of psychophysiological variables in multiple patients. The application was developed in Python 3.8 language, based on the requirements agreed upon by the collaborators of the overall project. Six objects were defined to process seven biosignals: electrocardiography (ECG), respiration (RESP), electrodermal activity (EDA), temperature (TEMP), near-infrared spectroscopy (fNIRS), photoplethysmography (PPG), and electromyography (EMG). For each object, different methods were used to extract psychophysiologically relevant features from the signals: ECG (heart rate and heart rate variability), RESP (respiratory rate), EDA (basal conductance, amplitude, rise time and mean recovery time), TEMP (maximum, mean and minimum), fNIRS (oxygen saturation), PPG (heart rate and heart rate variability) and EMG (mean RMS value). The user interface was developed with 3 windows: the main window to register and visualize patients and their data, a window to add measurements (biosignal acquisitions, vital sign readings and psychological tests) on patients, and a window to observe these measurements. The interface was validated by processing the signals acquired from a healthy volunteer, and particularly validated the detection of QRS complexes in ECG processing, and the detection of respirations in RESP processing. The information from the volunteer was correctly stored and the characteristics of the acquired biosignals were successfully extracted. On the other hand, the detection of QRS complexes in ECG obtained a sensitivity of 97.50 %, an accuracy of 99.00 % and an F1-value of 98.24 % in the MIT/BIH Arrhythmia database (n=109461 beats). Breath detection obtained a sensitivity of 94.78 %, accuracy of 92.02 % and F1-value of 93.38 % in the BDIMC database (n=7381 breaths).PregradoIngeniero(a) Biomédico(a)92 páginasapplication/pdfspaAplicación de registro y reporte automático de parámetros fisiológicos para la trazabilidad psicofisiológica en personas post COVID-19Trabajo de grado - Pregradoinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_7a1fTextinfo:eu-repo/semantics/bachelorThesishttps://purl.org/redcol/resource_type/TPhttp://purl.org/coar/version/c_970fb48d4fbd8a85Colombia, BogotáIngeniería BiomédicaZ. Allam, Surveying the Covid-19 Pandemic and its Implications. 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Eckert, «An automated and reliable method for breath detection during variable mask pressures in awake and sleeping humans,» PLoS ONE, vol. 12, n.o 6, págs. 1-21, 2017, issn: 19326203. doi: 10.1371/journal.pone.0179030.info:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbProcesamiento de señalesSofwarePsicofisiologíaTrazabilidadIngeniería BiomédicaProcesamiento de señalesCOVID-19PsicofisiologíaSofwareSignal processingSoftwareCOVID-19PsychophysiologyORIGINALMoreno Escobar, Juan Sebastián -2021.pdfMoreno Escobar, Juan Sebastián -2021.pdfapplication/pdf1615250https://repositorio.escuelaing.edu.co/bitstream/001/1741/1/Moreno%20Escobar%2c%20Juan%20Sebasti%c3%a1n%20-2021.pdfb6c3fd95d69db4ea34f5e4fae4108233MD51open accessAutorización.pdfAutorización.pdfapplication/pdf839125https://repositorio.escuelaing.edu.co/bitstream/001/1741/2/Autorizaci%c3%b3n.pdf1030f089df1c08cbc09950f2dd39b3faMD52metadata only accessLICENSElicense.txtlicense.txttext/plain; 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