Herramienta tecnológica portátil para favorecer el monitoreo y control de la presión arterial

datacite.rightshttp://purl.org/coar/access_right/c_16ecspa
dc.contributor.advisorMéndez Torrenegra, Fernando
dc.contributor.advisorOchoa Pertuz, Carlos
dc.contributor.authorRodríguez Barrios, Vanessa Susana
dc.date.accessioned2022-01-28T18:40:28Z
dc.date.available2022-01-28T18:40:28Z
dc.date.issued2022
dc.description.abstractSe observa que la hipertensión arterial es una enfermedad de alto riesgo, pues es una de las principales causas de muerte en el mundo, ya que al ser una enfermedad circulatoria puede causar daños en diferentes órganos del cuerpo, siendo los más comunes el cerebro y el corazón, por lo que un hipertenso puede tener ataques cerebro o cardiovasculares. Asimismo, la hipertensión arterial tampoco tiene cura, pero debe ser tratada minuciosamente, por medio de monitoreo de los signos vitales (principalmente la presión arterial), y de incluso más variables. También se considera dentro del tratamiento que el paciente pueda realizar autocuidado y telemonitoreo en casa, pues existe una alta tasa de ausentismo en los centros de salud. Para lo anterior se han realizado investigaciones recientes que han creado y probado diferentes herramientas tecnológicas como dispositivos digitales o aplicaciones móviles que puedan mantener al paciente monitoreado y controlado diariamente, en algunos incluso en tiempo real. Nótese que la tendencia actual está en migrar el procesamiento hacia el dispositivo, sin embargo, los algoritmos actuales requieren niveles de procesamiento y memoria con exigencias superiores a la que pueden brindar una herramienta portátil. Desde el enfoque del diseño, es necesario determinar las arquitecturas algorítmicas adecuadas, en el contexto de las restricciones de funcionamiento que manejan los dispositivos wearables de salud.spa
dc.description.abstractIt is observed that arterial hypertension is a high-risk disease, and of the main causes of death in the world, it’s classified as a circulatory disease and it can cause damage to different organs of the body, most commonly being the brain and the heart, so that a hypertensive person can suffer from strokes or cardiovascular attacks. Arterial hypertension has no cure, it must be treated thoroughly by monitoring vital signs (mainly blood pressure) and even more variables. It is also considered within the treatment that the patient can perform self-care and telemonitoring at home, since there is a high rate of absenteeism in health centers. For this, a recent research has been carried out and also tested different technological tools such as digital devices or mobile applications that can keep the patient monitored and controlled daily, and in some cases, in real time. Nowadays the current trend is to migrate the processing of data to the device, however, current algorithms require levels of processing and memory with higher capacities than a portable tool can provide. From the design approach, it is necessary to determine the appropriate algorithmic architectures, in the context of the operating restrictions handled by wearable health devices. This proposal presents the design and development of an alternative solution, which, from computer science, allows estimating the presence of arterial hypertension. The design context is the use of low-cost hardware, easy access and limited level of processingeng
dc.format.mimetypepdfspa
dc.identifier.urihttps://hdl.handle.net/20.500.12442/9324
dc.language.isospaspa
dc.publisherEdiciones Universidad Simón Bolívarspa
dc.publisherFacultad de Ingenieríasspa
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacionaleng
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccessspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectPresión arterialspa
dc.subjectHipertensiónspa
dc.subjectMonitoreospa
dc.subjectControlspa
dc.subjectHerramienta Portátilspa
dc.subjectDeep Learningeng
dc.subjectCNNeng
dc.subjectBlood Pressureeng
dc.subjectHypertensioneng
dc.subjectMonitoringeng
dc.subjectControleng
dc.subjectPortable Tooleng
dc.titleHerramienta tecnológica portátil para favorecer el monitoreo y control de la presión arterialspa
dc.type.driverinfo:eu-repo/semantics/masterThesisspa
dc.type.spaTrabajo de grado másterspa
dcterms.referencesAcosta, G. A. (2018). de riesgo cardiovascular en pacientes con hipertensión arterial Evaluación de vitamina D ,. 1. https://search.proquest.com/openview/c540a3f8aaaa8d901f075b7e57ea9d51/1?pqorigsite=gscholar&cbl=1216408spa
dcterms.referencesAlberto, V. S. J., & Gutiérrez Rubby Casallas. (2006). Fundamentos de Programación: Aprendizaje Activo Basado en Casos: Un enfoque Moderno Usando Java, Uml, objetos y Eclipse. Pearson Educación.spa
dcterms.referencesAlkhatib, M., Hafiane, A., & Vieyres, P. (2021). Merged 1D-2D Deep Convolutional Neural Networks for Nerve Detection in Ultrasound Images. 2020 25th International Conference on Pattern Recognition (ICPR), 4774–4780. https://doi.org/10.1109/ICPR48806.2021.9412988eng
dcterms.referencesAzizjon, M., Jumabek, A., & Kim, W. (2020). 1D CNN based network intrusion detection with normalization on imbalanced data. In 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), 218–224. https://doi.org/10.1109/ICAIIC48513.2020.9064976eng
dcterms.referencesBaek, S., Jang, J., & Yoon, S. (2019). End-to-end blood pressure prediction via fully convolutional networks. IEEE Access, 7, 185458–185468. https://doi.org/10.1109/ACCESS.2019.2960844eng
dcterms.referencesBaque Quimis, Y. R., & Tomala Cantos, K. F. (2018). Tesis. Recuperado a partir de http://repositorio.ug.edu.ec/handle/redug/33824eng
dcterms.referencesBernal, E., Campa, S., Carlos, G., & Espinilla, M. (2018). IoT y Ambientes Inteligentes – Aplicación a pacientes diabéticos e hipertensos en modalidad domiciliaria IoT by Smart Enviroments – Aplication to diabetic and hypertensive patients in domiciliary modality Technological model of monitoring and follow-up.eng
dcterms.referencesBerrondo, A. (2020). DETECCIÓN DE CARRETERAS EN IMÁGENES DE RECONOCIMIENTO REMOTO MEDIANTE DEEP LEARNING. Grado de Ingeniería Informática. Computación. Facultad de Informática. Universidad del País Vasco. España.spa
dcterms.referencesBriot, A., Viswanath, P., & Yogamani, S. (2018). Analysis of Efficient CNN Design Techniques for Semantic Segmentation. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 776–77609. https://doi.org/10.1109/CVPRW.2018.00109eng
dcterms.referencesBui, K., Oh, H., Yi, H. (2020). "Traffic Density Classification Using Sound Datasets: An Empirical Study on Traffic Flow at Asymmetric Roads," in IEEE Access, vol. 8, pp. 125671-125679, 2020, doi: 10.1109/ACCESS.2020.30079eng
dcterms.referencesBui, N., Pham, N., Barnitz, J. J., Zou, Z., Nguyen, P., Truong, H., Kim, T., Farrow, N., Nguyen, A., Xiao, J., Deterding, R., Dinh, T., & Vu, T. (2019). eBP : A Wearable System For Frequent and Comfortable Blood Pressure Monitoring From User ’ s Ear. The 25th Annual International Conference on Mobile Computing and Networking, 53.eng
dcterms.referencesBuldakova, T. I., & Sokolova, A. V. (2019). Network Services for Interaction of the Telemedicine System Users. 2019 1st International Conference on Control Systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA), 387–391. https://doi.org/10.1109/SUMMA48161.2019.8947552eng
dcterms.referencesBurton, W. (2019). 2D or 3D? A Simple Comparison of Convolutional Neural Networks for Automatic Segmentation of Cardiac Imaging. Towards Data Science. Obtenido de https://towardsdatascience.com/2d-or-3d-a-simple-comparison-of-convolutional-neuralnetworks-for-automatic-segmentation-of-625308f52aa7eng
dcterms.referencesCardona Arias, J., & LLanes Agudelo, O. (2013). Hipertensión arterial y sus factores de riesgo en indígenas Emberá-Cham. Revista CES Medicina, 27(1), 31–43.spa
dcterms.referencesCarek, A. M., & Inan, O. T. (2017). Robust Sensing of Distal Pulse Waveforms on a Modified Weighing Scale for Ubiquitous Pulse Transit Time Measurement. IEEE Transactions on Biomedical Circuits and Systems, 11(4), 765–772. https://doi.org/10.1109/TBCAS.2017.2683801eng
dcterms.referencesCarek, A. M., Jung, H., & Inan, O. T. (2020). A Reflective Photoplethysmogram Array and Channel Selection Algorithm for Weighing Scale Based Blood Pressure Measurement. IEEE Sensors Journal, 20(7), 3849–3858. https://doi.org/10.1109/JSEN.2019.2960063eng
dcterms.referencesCarrizo, E. (2018, August 30). Cómo la tecnología y la conectividad cambiará a la medicina en los próximos años. La Tercera. http://ezproxy.unisimon.edu.co/newspapers/cómo-latecnología-y-conectividad-cambiará/docview/2289221964/se-2?accountid=45648spa
dcterms.referencesCastillo, O. D. D., Ayala, L. J. P., Perez, A. L. C., Jimenez, J. M. C., Castano, Y. L. C., & Munoz-Sarmiento, D. M. (2019). Supervised learning system for detection of cardiac arrhythmias based on electrocardiographic data. 2019 IEEE International Conference on E-Health Networking, Application & Services (HealthCom), 1–4. https://doi.org/10.1109/HealthCom46333.2019.9009601eng
dcterms.referencesCaulfield, B. M., and S. C. Donnelly. May, 2013. “What Is Connected Health and Why Will It Change Your Practice?” Quarterly Journal of Medicine 106 (8): 703–707.eng
dcterms.referencesCerrato, P., & Halamka, J. (2019). Mobile Apps. The Transformative Power of Mobile Medicine, 69–88. https://doi.org/10.1016/b978-0-12-814923-2.00004-0eng
dcterms.referencesChan, Cooper, Hosanee, Welykholowa, Kyriacou, Zheng, Allen, Abbott, Lovell, Fletcher, & Elgendi. (2019). Multi-Site Photoplethysmography Technology for Blood Pressure Assessment: Challenges and Recommendations. Journal of Clinical Medicine, 8(11), 1827. https://doi.org/10.3390/jcm8111827eng
dcterms.referencesChandrasekhar, A., Yavarimanesh, M., Natarajan, K., Hahn, J. O., & Mukkamala, R. (2020). PPG Sensor Contact Pressure Should Be Taken into Account for Cuff-Less Blood Pressure Measurement. IEEE Transactions on Biomedical Engineering, 67(11), 3134– 3140. https://doi.org/10.1109/TBME.2020.2976989eng
dcterms.referencesChaubey, V., Nair, M. S., & Pillai, G. N. (2019). Gene Expression Prediction Using a Deep 1D Convolution Neural Network. 2019 IEEE Symposium Series on Computational Intelligence (SSCI), 1383–1389. https://doi.org/10.1109/SSCI44817.2019.9002669eng
dcterms.referencesChe, X., Li, M., Kang, W., Lai, F., & Wang, J. (2019). Continuous Blood Pressure Estimation from Two-Channel PPG Parameters by XGBoost. 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2707–2712. https://doi.org/10.1109/ROBIO49542.2019.8961600eng
dcterms.referencesChen, M. (2018). “Presión Sanguínea”. A.D.A.M. Inc. Biblioteca Nacional de Medicina de Estados Unidos (NIH). Obtenido de MedlinePlus: https://medlineplus.gov/spanish/ency/esp_imagepages/9124.htmspa
dcterms.referencesChen, X., Zhu, J., Jiang, J., & Tsui, C. Y. (2020). Tight compression: Compressing CNN model tightly through unstructured pruning and simulated annealing based permutation. Proceedings - Design Automation Conference, 2020-July, 1–6. https://doi.org/10.1109/DAC18072.2020.9218701eng
dcterms.referencesChen, Y., & Chen, W. (2018). Finger ECG based Two-phase Authentication Using 1D Convolutional Neural Networks. 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 336–339. https://doi.org/10.1109/EMBC.2018.8512263eng
dcterms.referencesCheuk, K. W., Anderson, H., Agres, K., & Herremans, D. (2020). nnAudio: An on-the-Fly GPU Audio to Spectrogram Conversion Toolbox Using 1D Convolutional Neural Networks. IEEE Access, 8, 161981–162003. https://doi.org/10.1109/ACCESS.2020.3019084eng
dcterms.referencesClara, F., Casarini, A., Blanco G., Corral, P., Tusman, G., Scandura, A., Meschino, G. (2010). Identificación de hipertensos recientes mediante análisis de onda de pulso radial. Revista de la Federación Argentina de Cardiología. Recuperado el 2 de diciembre de 2021 de ResearchGate en https://www.researchgate.net/publication/224048693_IDENTIFICACION_DE_HIPERTE NSOS_RECIENTES_MEDIANTE_ANALISIS_DE_ONDA_DE_PULSO_RADIALspa
dcterms.referencesCoelho, J. C., Ferretti-Rebustini, R. E. de L., Suemoto, C. K., Leite, R. E. P., Jacob-Filho, W., & Pierin, A. M. G. (2019). Hypertension is the underlying cause of death assessed at the autopsy of individuals. Revista Da Escola de Enfermagem Da U S P, 53, e03457. https://doi.org/10.1590/S1980-220X2018006103457eng
dcterms.referencesCuidemos Nuestro Corazón. (2019). Liga Colombiana Contra El Infarto y La Hipertensión. https://colombiacorazon.com/2019/08/28/cuidemos-nuestro-corazon-2/spa
dcterms.referencesDe, I., Newswire, P. R., America, S., York, N., & York, N. (2005). Los principales expertos en enfermedades cardiovasculares instan a la comunidad médica a practicar una gestión de riesgo cardiovascular completa. 3–5.spa
dcterms.referencesDEGBEDZUI, D. K., YÜKSEL, M. E., & Farah Malik, A. E. (2020). Preterm Birth Prediction by Classification of Spectral Features of Electrohysterography Signals using 1D Convolutional Neural Network: Preliminary Results. 2020 28th Signal Processing and Communications Applications Conference (SIU), 1–4. https://doi.org/10.1109/SIU49456.2020.9302195eng
dcterms.referencesDeng, Y. (2019). Deep Learning on Mobile Devices-A Review. https://doi.org/10.13140/RG.2.2.15012.12167eng
dcterms.referencesDi Rienzo, M., Vaini, E., Castiglioni, P., Merati, G., Meriggi, P., Parati, G., Faini, A., & Rizzo, F. (2013). Wearable seismocardiography: Towards a beat-by-beat assessment of cardiac mechanics in ambulant subjects. Autonomic Neuroscience, 178(1–2), 50–59. https://doi.org/10.1016/j.autneu.2013.04.005eng
dcterms.referencesDodge, Y. (2003). The Oxford Dictionary of Statistical Terms, OUP. ISBN 0-19-920613-9eng
dcterms.referencesEch-Cherif, A., Misbhauddin, M., & Ech-Cherif, M. (2019). Deep Neural Network Based Mobile Dermoscopy Application for Triaging Skin Cancer Detection. 2nd International Conference on Computer Applications and Information Security, ICCAIS 2019, 1–6. https://doi.org/10.1109/CAIS.2019.8769517eng
dcterms.referencesElnour, M., Meskin, N., & Khan, K. M. (2020). Hybrid Attack Detection Framework for Industrial Control Systems using 1D-Convolutional Neural Network and Isolation Forest. 2020 IEEE Conference on Control Technology and Applications (CCTA), 877–884. https://doi.org/10.1109/CCTA41146.2020.9206394eng
dcterms.referencesEren, L. (2017). Bearing Fault Detection by One-Dimensional Convolutional Neural Networks. Mathematical Problems in Engineering, 2017, 1–9. https://doi.org/10.1155/2017/8617315eng
dcterms.referencesEsmeralda Vazquez, Maria Guadalupe Treviño, Cinthia Patricia Ibarra Gonzalez, Olga Lidia Banda Gonzalez, Vásquez Salazar, María Guadalupe Rangel trejo, N. E. (2011). Control metabólico de los adultos mayores con padecimientos de. 1, 22–29.spa
dcterms.referencesFarmacéutico, G. C., Mar, M. M., Práctica, G., Implantar, P., En, S., Farmacia, S. U., La, C., & La, V. O. Z. D. E. (2013). Ofrecer servicios para generar demanda. 1–3.spa
dcterms.referencesFerroukhi, M., Acef, L., Attari, M., & Ouahabi, A. (2019). Robust and reliable PPG and ECG integrated biosensor. 2019 6th International Conference on Image and Signal Processing and Their Applications (ISPA), 1–5. https://doi.org/10.1109/ISPA48434.2019.8966922eng
dcterms.referencesFranquis, B. (2017). Hipertensos están más expuestos a los infartos. Nacional, 1–2spa
dcterms.referencesGallardo, A., Franco, P., Urtubey, X. (2019). “Experiencia de pacientes con diabetes e hipertensión que participan en un programa de telemonitoreo”. Rev CES Med 2019; 33(1): 31-41.spa
dcterms.referencesGalván Vargas, C. G., Hernández Jiménez, L., Martín Hernández, P., Silva Rendón, A. J., Ramírez, J. M., Romo Cordero, X., & Rapalo Carbajal, D. N. (2019). Valoración de la variabilidad de la frecuencia cardiaca en pacientes con hipertensión arterial sistémica tratados mediante ablación renal por radiofrecuencia. Serie de casos. In Rev. sanid. mil (pp. 5–6).spa
dcterms.referencesGarg, P., Davenport, E., Murugesan, G., Wagner, B., Whitlow, C., Maldjian, J., & Montillo, A. (2017). Automatic 1D convolutional neural network-based detection of artifacts in MEG acquired without electrooculography or electrocardiography. 2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI), 1–4. https://doi.org/10.1109/PRNI.2017.7981506eng
dcterms.referencesGhoshachandra, P., Limkriengkrai, C., Wimonsakcharoen, P., & Tangsripairoj, S. (2017). oHealth: A self-care android application for senior citizens with hypertension. 6th ICT International Student Project Conference: Elevating Community Through ICT, ICT-ISPC 2017, 2017-Janua, 1–5. https://doi.org/10.1109/ICT-ISPC.2017.8075318eng
dcterms.referencesGiannakakis, G., Trivizakis, E., Tsiknakis, M., & Marias, K. (2019). A novel multi-kernel 1D convolutional neural network for stress recognition from ECG. 2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos, ACIIW 2019, 273–276. https://doi.org/10.1109/ACIIW.2019.8925020eng
dcterms.referencesGoldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C.-K., & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet. Circulation, 101(23), e215–e220. https://doi.org/10.1161/01.CIR.101.23.e215eng
dcterms.referencesGómez García, M. T., Troncoso Acevedo, M. F., Rodriguez Guzmán, M., Alegre de Montaner, R., Fernández Fernández, B., del Río Camacho, G., & González-Mangado, N. (2014). ¿Puede ser el tiempo de tránsito de pulso útil para detectar hipertensión arterial en pacientes remitidos a la unidad de sueño? Archivos de Bronconeumología, 50(7), 278– 284. https://doi.org/10.1016/j.arbres.2013.12.001eng
dcterms.referencesHan, Q., Liu, L., Zhao, Y., & Zhao, Y. (2020). Ecological Big Data Adaptive Compression Method Combining 1D Convolutional Neural Network and Switching Idea. IEEE Access, 8, 20270–20278. https://doi.org/10.1109/ACCESS.2020.2969216eng
dcterms.referencesHarvard Men’s Health Watch. (2019). Blood pressure and your brain. Harvard Health Publishing. Harvard Medical School. https://www.health.harvard.edu/heart-health/bloodpressure-and-your-braineng
dcterms.referencesHasan, M. A., Munia, E. J., Pritom, S. K., Setu, M. H., Ali, M. T., & Fahim, S. C. (2020). Cardiac Arrhythmia Detection in an ECG Beat Signal Using 1D Convolution Neural Network. 2020 IEEE Region 10 Symposium (TENSYMP), 352–357. https://doi.org/10.1109/TENSYMP50017.2020.9230581eng
dcterms.referencesHelsinki (2018). “Crean en Finlandia una aplicación móvil que detecta infartos de miocardio”. EEE, La Razón Digital. Obtenido de http://www.larazon.com/index.php?_url=/sociedad/Ciencia_tecnologia/Aplicacion-movil-detectainfartos-miocardio-Finlandia_0_2590540937.htmlspa
dcterms.referencesHendrick, H., Zhi-Hao, W., Hsien-I, C., Pei-Lun, C., & Gwo-Jia, J. (2019). IOS mobile APP for tuberculosis detection based on chest X-ray image. Proceedings of ICAITI 2019 - 2nd International Conference on Applied Information Technology and Innovation: Exploring the Future Technology of Applied Information Technology and Innovation, 122–125. https://doi.org/10.1109/ICAITI48442.2019.8982152eng
dcterms.referencesHermida, A.; López, J., Calvo, C. (2012). Medida no invasiva de la presión arterial central mediante tonometría por aplanamiento. Análisis de la onda de pulso. Unidad de Hipertensión y Riesgo Vascular. Servicio de Medicina Interna. Hospital Clínico Universitario de Santiago de Compostela (CHUS). SERGAS. Santiago de Compostela. Galicia Clínica, Sociedade Galega de Medicina Interna. Galicia Clin 2012; 73 (4): 161- 168. Recuperado el 2 de diciembre de 2021 de https://galiciaclinica.info/pdf/21/389.pdfspa
dcterms.referencesHeydari, F., Ebrahim, M. P., Redoute, J.-M., Joe, K., Walker, K., & Rasit Yuce, M. (2020). A chest-based continuous cuffless blood pressure method: Estimation and evaluation using multiple body sensors. Information Fusion, 54, 119–127. https://doi.org/10.1016/j.inffus.2019.07.001eng
dcterms.referencesHjellvik, V., Sakshaug, S., & Strøm, H. (2012). Body mass index, triglycerides, glucose, and blood pressure as predictors of type 2 diabetes in a middle-aged Norwegian cohort of men and women. Clinical epidemiology, 4, 213–224. https://doi.org/10.2147/CLEP.S31830eng
dcterms.referencesHoang, N. S., Cai, Y., Lee, C.-W., Yang, Y. O., Chui, C.-K., & Heng Chua, M. C. (2019). Gait classification for Parkinson’s Disease using Stacked 2D and 1D Convolutional Neural Network. 2019 International Conference on Advanced Technologies for Communications (ATC), 44–49. https://doi.org/10.1109/ATC.2019.8924567eng
dcterms.referencesHooman Sedghamiz (2021). Complete Pan Tompkins Implementation ECG QRS detector (https://www.mathworks.com/matlabcentral/fileexchange/45840-complete-pan-tompkinsimplementation-ecg-qrs-detector), MATLAB Central File Exchange. Retrieved September 2, 2021.eng
dcterms.referencesHosanee, M., Chan, G., Welykholowa, K., Cooper, R., Kyriacou, P. A., Zheng, D., Allen, J., Abbott, D., Menon, C., Lovell, N. H., Howard, N., Chan, W.-S., Lim, K., Fletcher, R., Ward, R., & Elgendi, M. (2020). Cuffless Single-Site Photoplethysmography for Blood Pressure Monitoring. Journal of Clinical Medicine, 9(3), 723. https://doi.org/10.3390/jcm9030723eng
dcterms.referencesHossain Shuvo, M. M., Ahmed, N., Nouduri, K., & Palaniappan, K. (2020). A hybrid approach for human activity recognition with support vector machine and 1d convolutional neural network. Proceedings - Applied Imagery Pattern Recognition Workshop, 2020-Octob. https://doi.org/10.1109/AIPR50011.2020.9425332eng
dcterms.referencesHui, Y., Yin, Z., Wu, M., & Li, D. (2021). Wearable Devices Acquired ECG Signals Detection Method Using 1D Convolutional Neural Network. 2021 15th International Symposium on Medical Information and Communication Technology (ISMICT), 81–85. https://doi.org/10.1109/ISMICT51748.2021.9434935eng
dcterms.referencesHurtado de Barrera, Jacqueline & Morales, Marcos. (2000). Metodología de la investigación holísticaspa
dcterms.referencesHurtado de Barrera, J. (2010). Metodología de la investigación: guía para la comprensión holística de la ciencia (4 ed.). Caracas: Quirón Edicionesspa
dcterms.referencesHurtado de Barrera, J. (2016). Metodología de la Investigacion Holistica. Caracas,(Venezuela): Quiron Editores-CIEA Sypal.spa
dcterms.referencesIbrahim, B., & Jafari, R. (2019). Cuffless Blood Pressure Monitoring from an Array of Wrist Bio-Impedance Sensors Using Subject-Specific Regression Models: Proof of Concept. IEEE Transactions on Biomedical Circuits and Systems, 13(6), 1723–1735. https://doi.org/10.1109/TBCAS.2019.2946661eng
dcterms.referencesImran, A. S., Kastrati, Z., Svendsen, T. K., & Kurti, A. (2019). Text-Independent Speaker ID Employing 2D-CNN for Automatic Video Lecture Categorization in a MOOC Setting. 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), 273–277. https://doi.org/10.1109/ICTAI.2019.00046eng
dcterms.referencesInce, T., Kiranyaz, S., Eren, L., Askar, M., & Gabbouj, M. (2016). Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks. IEEE Transactions on Industrial Electronics, 63(11), 7067–7075. https://doi.org/10.1109/TIE.2016.2582729eng
dcterms.referencesJanjua, G., Guldenring, D., Finlay, D., & McLaughlin, J. (2017). Wireless chest wearable vital sign monitoring platform for hypertension. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 676201, 821–824. https://doi.org/10.1109/EMBC.2017.8036950eng
dcterms.referencesJeong I., Bychkov D., Searson P. C. (2019). "Wearable Devices for Precision Medicine and Health State Monitoring," in IEEE Transactions on Biomedical Engineering, vol. 66, no. 5, pp. 1242-1258, doi: 10.1109/TBME.2018.2871638.eng
dcterms.referencesJia, F., Majumdar, S., & Ginsburg, B. (2021). MarbleNet: Deep 1D Time-Channel Separable Convolutional Neural Network for Voice Activity Detection. ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 6818– 6822. https://doi.org/10.1109/ICASSP39728.2021.9414470eng
dcterms.referencesJohn M. (2012). “Cómo medir la presión arterial en casa”. Las Guías Sumarias de los Consumidores. Eisenberg Center for Clinical Decisions and Communications Science. Obtenido de https://www.ncbi.nlm.nih.gov/books/NBK97819/spa
dcterms.referencesJohnson AEW, Pollard TJ, Shen L, Lehman L, Feng M, Ghassemi M, Moody B, Szolovits P, Celi LA, and Mark RG. MIMIC-III, a freely accessible critical care database. Scientific Data (2016). DOI: 10.1038/sdata.2016.35. Recuperado el 2 de diciembre de 2021 de MIMIIC en https://archive.physionet.org/physiobank/database/mimic3wdb/eng
dcterms.referencesJulius S, Kaciroti N, Egan BM, Nesbitt S, Michelson EL. (2008) Trial of Preventing Hypertension (TROPHY) Investigators. J Am Soc Hypertens. 2008 Jan-Feb;2(1):39- 43.eng
dcterms.referencesKim, C.-S., Carek, A. M., Mukkamala, R., Inan, O. T., & Hahn, J.-O. (2015). Ballistocardiogram as Proximal Timing Reference for Pulse Transit Time Measurement: Potential for Cuffless Blood Pressure Monitoring. IEEE Transactions on Biomedical Engineering, 62(11), 2657–2664. https://doi.org/10.1109/TBME.2015.2440291eng
dcterms.referencesKim, S.-H., & Han, G.-T. (2019). 1D CNN Based Human Respiration Pattern Recognition using Ultra Wideband Radar. 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), 411–414. https://doi.org/10.1109/ICAIIC.2019.8669000eng
dcterms.referencesKiranyaz, S., Gastli, A., Ben-Brahim, L., Al-Emadi, N., & Gabbouj, M. (2019). Real-Time Fault Detection and Identification for MMC Using 1-D Convolutional Neural Networks. IEEE Transactions on Industrial Electronics, 66(11), 8760–8771. https://doi.org/10.1109/TIE.2018.2833045eng
dcterms.referencesKiranyaz, S., Ince, T., & Gabbouj, M. (2016). Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks. IEEE Transactions on Biomedical Engineering, 63(3), 664–675. https://doi.org/10.1109/TBME.2015.2468589eng
dcterms.referencesKiranyaz, S., Ince, T., Hamila, R., & Gabbouj, M. (2015). Convolutional Neural Networks for patient-specific ECG classification. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2608–2611. https://doi.org/10.1109/EMBC.2015.7318926eng
dcterms.referencesKirtana, R. N., & Lokeswari, Y. V. (2017). An IoT based remote HRV monitoring system for hypertensive patients. 2017 International Conference on Computer, Communication and Signal Processing (ICCCSP), 1–6. https://doi.org/10.1109/ICCCSP.2017.7944086eng
dcterms.referencesKurowski, A., Zaporowski, S., & Czyzewski, A. (2020). 1D convolutional context-aware architectures for acoustic sensing and recognition of passing vehicle type. 2020 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), 142–145. https://doi.org/10.23919/SPA50552.2020.9241256eng
dcterms.referencesKvedar, J., Coye, M. J., & Everett, W. (2014). Connected health: a review of technologies and strategies to improve patient care with telemedicine and telehealth. Health affairs (Project Hope), 33(2), 194–199. https://doi.org/10.1377/hlthaff.2013.0992eng
dcterms.referencesLa Organización Panamericana de la Salud y La Liga Mundial de la Hipertensión. (2018). Marco de Monitoreo y Evaluación para Programas de Control de Hipertensión. PAHO, iris. Obtenido de http://iris.paho.org/xmlui/handle/123456789/34910spa
dcterms.referencesLamonaca, F., Carni, D. L., Spagnuolo, V., Grimaldi, G., Bonavolonta, F., Liccardo, A., Moriello, R. S. Lo, & Colaprico, A. (2019). A New Measurement System to Boost the IoMT for the Blood Pressure Monitoring. 2019 IEEE International Symposium on Measurements and Networking, M and N 2019 - Proceedings, 1–6. https://doi.org/10.1109/IWMN.2019.8805016eng
dcterms.referencesLandry C., Peterson S. D., Arami A. (2019). "Estimation of the Blood Pressure Waveform using Electrocardiography*," 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany, pp. 7060-7063, doi: 10.1109/EMBC.2019.8856399eng
dcterms.referencesLeón, G., López, M., & Díaz, C. (2016). Técnica para una correcta toma de la presión arterial en el paciente ambulatorio. Revista de La Facultad de Medicina (México, 59(3), 49–55.spa
dcterms.referencesLiau, J. C., & Ho, C. Y. (2019). Intelligence IoT(Internal of Things) Telemedicine Health Care Space System for the Elderly Living Alone. Proceedings of 2019 IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability, ECBIOS 2019, 13–14. https://doi.org/10.1109/ECBIOS.2019.8807821eng
dcterms.referencesLim, C., Kim, J.-Y., & Nam, Y. (2020). ECG Signal Analysis for Patient with Metabolic Syndrome based on 1D-Convolution Neural Network. 2020 International Conference on Computational Science and Computational Intelligence (CSCI), 731–733. https://doi.org/10.1109/CSCI51800.2020.00134eng
dcterms.referencesLiu J., Yan B. P., Zhang Y., Ding X., Su P., Zhao N. (2019). "Multi-Wavelength Photoplethysmography Enabling Continuous Blood Pressure Measurement With Compact Wearable Electronics," in IEEE Transactions on Biomedical Engineering, vol. 66, no. 6, pp. 1514-1525, doi: 10.1109/TBME.2018.2874957eng
dcterms.referencesLo Giudice, M., Varone, G., Ieracitano, C., Mammone, N., Bruna, A. R., Tomaselli, V., & Morabito, F. C. (2020). 1D Convolutional Neural Network approach to classify voluntary eye blinks in EEG signals for BCI applications. Proceedings of the International Joint Conference on Neural Networks, Ccd. https://doi.org/10.1109/IJCNN48605.2020.9207195eng
dcterms.referencesManrique-Abril, F. G., Herrera-Amaya, G. M., Manrique-Abril, R. A., & Beltrán-Morera, J. (2018). Costos de un programa de atención primaria en salud para manejo dela hipertensión arterial en Colombia. Revista de Salud Pública, 20(4), 465–471. https://doi.org/10.15446/rsap.v20n4.64679spa
dcterms.referencesManurung, B. E., Munggaran, H. R., Ramadhan, G. F., & Koesoema, A. P. (2019). Noninvasive blood glucose monitoring using near-infrared spectroscopy based on internet of things using machine learning. IEEE Region 10 Humanitarian Technology Conference, R10-HTC, 2019-Novem, 5–11. https://doi.org/10.1109/R10-HTC47129.2019.9042479eng
dcterms.referencesMartín F., Poo R. (2010). “Generalidades sobre domotica e inmótica” .Sensores para Domótica e Inmótica. Ingeniería de Sistemas y Automática, Universidad de Oviedo, España. Obtenido de http://isa.uniovi.es/docencia/AutomEdificios/transparencias/sensores.pdfspa
dcterms.referencesMartínez Bello, M. (1995). Efectos del ruido por exposición laboral. Salud de Los Trabajadores, 3(2), 93–101.spa
dcterms.referencesMcLuhan M.l y B.R. Powers. (1993). La Aldea Global. (2 ed.) Barcelona: Gedisa, 203 p.spa
dcterms.referencesMehmood, K., Imran, H. A., & Latif, U. (2020). HARDenseNet: A 1D DenseNet Inspired Convolutional Neural Network for Human Activity Recognition with Inertial Sensors. 2020 IEEE 23rd International Multitopic Conference (INMIC), 1–6. https://doi.org/10.1109/INMIC50486.2020.9318067eng
dcterms.referencesMicrosoft (2019). Seeing AI: Making the visual world more accessible. Recuperado el 11 de diciembre de 2021 en https://www.youtube.com/watch?v=DybczEDGKE&ab_channel=Microsoft)eng
dcterms.referencesMinisterio de Salud Pública del Ecuador (2019). Hipertensión Arterial. Guía de Práctica Clínica (GPC). Quito, Ecuador. Obtenido de https://www.salud.gob.ec/wpcontent/uploads/2019/06/gpc_hta192019.pdfspa
dcterms.referencesMinisterio de Salud. (2017). DÍA MUNDIAL DE L A HIPERTENSIÓN ARTERIAL Colombia. 4, 11. https://www.minsalud.gov.co/sites/rid/Lists/BibliotecaDigital/RIDE/VS/PP/ENT/diamundial-hipertension-2017.pdfspa
dcterms.referencesMINTIC. (2017). Cifras Primer Trimestre de 2017. Ministerio de Las Telecomunicaciones, Colombia., 1–49. https://colombiatic.mintic.gov.co/602/articles-55212_archivo_pdf.pdfspa
dcterms.referencesMishra, A. (2018). Metrics to Evaluate your Machine Learning Algorithm. Towards Data Science. Recuperado el 2 de diciembre de 2021 de https://towardsdatascience.com/metrics-to-evaluate-your-machine-learning-algorithmf10ba6e38234spa
dcterms.referencesMishra, B., & Thakkar, N. (2017). Cuffless blood pressure monitoring using PTT and PWV methods. 2017 International Conference on Recent Innovations in Signal Processing and Embedded Systems (RISE), 395–401. https://doi.org/10.1109/RISE.2017.8378188eng
dcterms.referencesMollura, M., Maria Polo, E., Lehman, L., & Barbieri, R. (2020). Assessment of Heart Rate Variability derived from Blood Pressure Pulse Recordings in Intensive Care Unit Patients. 2020 Computing in Cardiology Conference (CinC), 47, 1–4. https://doi.org/10.22489/cinc.2020.423eng
dcterms.referencesMoody, B., Moody, G., Villarroel, M., Clifford, G., & Silva, I. (2020). MIMIC-III Waveform Database Matched Subset (version 1.0). PhysioNet. https://doi.org/10.13026/c2294beng
dcterms.referencesMoody, G. (2019). WFDB Applications Guide. Tenth Edition. Harvard-MIT Division of Health Sciences and Technology. MIT Room E25-505A, Cambridge, MA 02139, USA. Recuperado de https://physionet.org/physiotools/wag/eng
dcterms.referencesMoshkova, A., Samorodov, A., Ivanova, E., & Fedotova, E. (2020). High Accuracy Discrimination of Parkinson’s Disease from Healthy Controls by Hand Movements Analysis Using LeapMotion Sensor and 1D Convolutional Neural Network. 2020 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT), 62–65. https://doi.org/10.1109/USBEREIT48449.2020.9117736eng
dcterms.referencesMousavi S. S., Charmi M., Firouzmand M., Hemmati M., Moghadam M. (2019). "A New Approach Based on Dynamical Model of The ECG Signal to Blood Pressure Estimation," 4th International Conference on Pattern Recognition and Image Analysis (IPRIA), Tehran, Iran, pp. 210-215, doi: 10.1109/PRIA.2019.8786016.eng
dcterms.referencesMoya, L. (2018). Prologue. Revista Colombiana de Cardiologia, 25, 2–3. https://doi.org/10.1016/j.rccar.2018.10.002spa
dcterms.referencesMuralidhar, E. S., Gowtham, T. S., Jain, A., & Padmaveni, K. (2020). Development of Health Monitoring Application using Machine Learning on Android Platform. 2020 5th International Conference on Communication and Electronics Systems (ICCES), Icces, 1076–1085. https://doi.org/10.1109/ICCES48766.2020.9137969eng
dcterms.referencesNaciones Unidas (2020). "Envejecimiento". Asuntos que nos importan. Obtenido de las Naciones Unidas: https://www.un.org/es/sections/issues-depth/ageing/index.htmlspa
dcterms.referencesNewman, J. L., Phillips, J. S., & Cox, S. J. (2021). 1D Convolutional Neural Networks for Detecting Nystagmus. IEEE Journal of Biomedical and Health Informatics, 25(5), 1814– 1823. https://doi.org/10.1109/JBHI.2020.3025381eng
dcterms.referencesNidigattu, G. R., Mattela, G., & Jana, S. (2020). Non-invasive modeling of heart rate and blood pressure from a photoplethysmography by using machine learning techniques. 2020 International Conference on COMmunication Systems and NETworkS, COMSNETS 2020, 7–12. https://doi.org/10.1109/COMSNETS48256.2020.9027457eng
dcterms.referencesNishikawa, K., Hirakawa, R., Kawano, H., Nakashi, K., & Nakatoh, Y. (2021). Detecting System Alzheimer’s Dementia by 1d CNN-LSTM in Japanese Speech. 2021 IEEE International Conference on Consumer Electronics (ICCE), 1–3. https://doi.org/10.1109/ICCE50685.2021.9427692eng
dcterms.referencesNotimex, A., City, M., & City, M. (2018). Telemedicina combina salud y tecnología. May, 1–2.spa
dcterms.referencesNovack, G. (2020). Building a One Hot Encoding Layer with TensorFlow. Towards Data Science. Recuperado el 2 de diciembre de 2021 de https://towardsdatascience.com/building-a-one-hot-encoding-layer-with-tensorflowf907d686bf39eng
dcterms.referencesOrganización Mundial de la Salud. (2015). "Organización Mundial de la Salud. Informe mundial sobre el envejecimiento y la salud. Ginebra: OMS; 2015. Disponible en: http://apps.who.int/iris/bitstream/10665/186466/1/9789240694873_spa.pdfspa
dcterms.referencesOrganización Mundial de la Salud. (2017, 17 mayo). Enfermedades cardiovasculares. https://www.who.int/es/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)spa
dcterms.referencesPalatini P., Rosei EA, Casiglia E., Chalmers J., Ferrari R., Grassi G., Inoue T., Jelakovic B., Jensen M. T., Julius S., Kjeldsen S. G., Mancia G., Parati G., Pauletto P., Stella A., Zanchetti A. (2016). Management of the hypertensive patient with elevated heart rate: Statement of the Second Consensus Conference endorsed by the European Society of Hypertension. J Hypertens;34(5):813-821. doi:10.1097/HJH.0000000000000865eng
dcterms.referencesPallas, R., Casanella, R., Gómez, J. (2017). Método y aparato para estimar el tiempo de tránsito del pulso aórtico a partir de intervalos temporales medidos entre puntos fiduciales del balistocardiograma. Oficina Española de Patentes y Marcas. España. Número de publicación 2 607 721. UNIVERSITAT POLITÉCNICA DE CATALUNYA. Recuperado de Google Patents el 31 de agosto de 2021 en https://patents.google.com/patent/ES2607721A1/esspa
dcterms.referencesPardasani, R., & Awasthi, N. (2020). Classification of 12 Lead ECG Signal Using 1DConvolutional Neural Network With Class Dependent Threshold. 2020 Computing in Cardiology, 1–4. https://doi.org/10.22489/CinC.2020.277eng
dcterms.referencesPatel, P., Ordunez, P., DiPette, D., Escobar, M. C., Hassell, T., Wyss, F., Hennis, A., Asma, S., & Angell, S. (2016). Improved Blood Pressure Control to Reduce Cardiovascular Disease Morbidity and Mortality: The Standardized Hypertension Treatment and Prevention Project. Journal of Clinical Hypertension, 18(12), 1284–1294. https://doi.org/10.1111/jch.12861eng
dcterms.referencesPeco, R. (2020). ¿QUÉ ES UNA ONDA DE PRESIÓN ARTERIAL? EL DR. JOSÉ MIGUEL ALONSO IÑIGO RESPONDE LA PREGUNTA. ANESTESIA Y CUIDADOS INTENSIVOS,ARTÍCULOS, CUIDADOS INTENSIVOS. VYGON. Campus VYGON. Recuperado el 2 de diciembre de 2021 de https://campusvygon.com/onda-de-presionarterial-dr-alonso/spa
dcterms.referencesPelletier, C., Webb, G., & Petitjean, F. (2019). Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series. Remote Sensing, 11, 523. https://doi.org/10.3390/rs11050523eng
dcterms.referencesPerez, A. D., Perez, A. R., Diaz, G. O., Gil, P. C., & Quiroz, E. N. (2017). Interacción dinámica de factores de riesgo epidemiológicos presentes en los trastornos hipertensivos del embarazo: Un estudio piloto. Salud Uninorte, 33(1), 27–38.spa
dcterms.referencesPerti, A., Singh, M. P., Panwar, H., & Tyagi, H. (2020). Face Recognition from Surveillance Using Sequnetial CNN-Model. 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), 372–376. https://doi.org/10.1109/ICRITO48877.2020.9197916eng
dcterms.referencesPhysionet (s.f.). Overview. Open databases. Recuperado de Physionet el 14/08/2021 de https://www.physionet.org/about/database/eng
dcterms.referencesPlazas, M. (2008). "Uso de ambientes virtuales y selección de parámetros de medidas en la aplicación para el tratamiento de fobias". Ingeniería y Desarrollospa
dcterms.referencesProskurov, V., Kurmukov, A., Pisov, M., & Belyaev, M. (2021). Fast Lung Localization in Computed Tomography by a 1D Detection Network. 2021 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT), 173–176. https://doi.org/10.1109/USBEREIT51232.2021.9455083eng
dcterms.referencesPuig M., Moreno C. (2011). “Cuidados y calidad de vida en Vilafranca del Penedès: los mayores de 75 y más años atendidos por el servicio de atención domiciliaria y sus cuidadores familiares”. Valoración de enfermería a una persona mayor atendida en atención domiciliaria. Obtenido de http://scielo.isciii.es/scielo.php?script=sci_arttext&pid=S1134- 928X2011000300005&lang=esspa
dcterms.referencesPuuronen S., Vasilyeva E., Pechenizkiy M. and Tesanovic A. (2010), "A holistic framework for understanding acceptance of Remote Patient Management (RPM) systems by nonprofessional users," IEEE 23rd International Symposium on Computer-Based Medical Systems (CBMS), Perth, WA, 2010, pp. 426-431, doi: 10.1109/CBMS.2010.6042682.eng
dcterms.referencesQayyum, A., Khan, M. K. A. A., Mazher, M., & Suresh, M. (2018). Classification of EEG Learning and Resting States using 1D-Convolutional Neural Network for Cognitive Load Assesment. 2018 IEEE Student Conference on Research and Development (SCOReD), 1– 5. https://doi.org/10.1109/SCORED.2018.8711150eng
dcterms.referencesQihang Yao, Ruxin Wang, Xiaomao Fan, Jikui Liu, Ye Li. (2020). Multi-class Arrhythmia detection from 12-lead varied-length ECG using Attention-based Time-Incremental Convolutional Neural Network, Information Fusion, Volume 53, 2020, Pages 174-182, ISSN 1566-2535, https://doi.org/10.1016/j.inffus.2019.06.024. (https://www.sciencedirect.com/science/article/pii/S1566253518307632)eng
dcterms.referencesQiu, C., Wu, T., Redoute, J. M., & Yuce, M. R. (2019). A Wireless Wearable Sensor Patch for the Real-Time Estimation of Continuous Beat-to-Beat Blood Pressure. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 6842–6845. https://doi.org/10.1109/EMBC.2019.8857446eng
dcterms.referencesQosim, A. L., Kurniawan, F., Bahruddin, U., Mubaraq, Z., Suhartono, & Faisal, M. (2021). Analysis Classification Opinion of Policy Government Announces Cabinet Reshuffle on YouTube Comments Using 1D Convolutional Neural Networks. 3rd 2021 East Indonesia Conference on Computer and Information Technology, EIConCIT 2021, 30–35. https://doi.org/10.1109/EIConCIT50028.2021.9431884eng
dcterms.referencesQuintero-Cruz, M. V., Mantilla-Morrón, M., & Urina-Triana, M. (2018). La importancia de la evaluación de la fragilidad en el adulto mayor con enfermedad cardiovascular. LATINOAMERICANA DE HIPERTENSIÓN, 13(4), 368–373. ttp://saber.ucv.ve/ojs/index.php/rev_lh/article/view/15732spa
dcterms.referencesRAE (2020). Paciente. Real Academia Española. DeL. Asociación de Academias de la Lengua Española. Diccionario de la lengua española. Edición Tricentenario. Actualización 2020. Obtenido de https://dle.rae.es/pacientespa
dcterms.referencesRastegar, S., GholamHosseini, H., & Lowe, A. (2020). Non-invasive continuous blood pressure monitoring systems: current and proposed technology issues and challenges. Physical and Engineering Sciences in Medicine, 43(1), 11–28. https://doi.org/10.1007/s13246-019- 00813-xeng
dcterms.referencesReal Academia Española. Paciente, 2020. Diccionario de la lengua española. Consultado en https://dle.rae.es/pacientespa
dcterms.referencesRICHTEK (2018). ECG/PPG Measurement Solution. Technical Documents, Design Support. AN057. RICHTEK Designer. Obtenido de https://www.richtek.com/Design%20Support/Technical%20Document/AN057eng
dcterms.referencesSabree Al-Gayar, S. M., Marin, I., Almalchy, M., Goga, N., Al-Habeeb, N., & Taslitschi, C. (2020). Implementation of MediCare Social Media System. 2020 12th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), 1–8. https://doi.org/10.1109/ECAI50035.2020.9223255eng
dcterms.referencesSadat Fazel, A. (2019). Procesado de señales biomédicas para la estimación de la presión arterial. Grado en Ingeniería en Tecnologías de la Telecomunicación. Universidad de Alcalá. Escuela Politécnica Superior. Biblioteca Universidad de Alcalá: http://hdl.handle.net/10017/39426spa
dcterms.referencesSagga, D., Echtioui, A., Khemakhem, R., & Ghorbel, M. (2020). Epileptic Seizure Detection using EEG Signals based on 1D-CNN Approach. 2020 20th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA), 51–56. https://doi.org/10.1109/STA50679.2020.9329321eng
dcterms.referencesSánchez, B., González, G. (2017). “Ausentismo y complicaciones de salud en usuarios de programas de hipertensión arterial de Santa Marta (Colombia)”. Salud Uninorte, Barranquilla, Colombia. Obtenido de: http://www.scielo.org.co/pdf/sun/v33n2/2011- 7531-sun-33-02-00178.pdfspa
dcterms.referencesSánchez Serrano, Brigith, & González Ruiz, Gisela. (2017). Ausentismo y complicaciones de salud en usuarios de programas de hipertensión arterial de Santa Marta (Colombia). Revista Salud Uninorte, 33(2), 178-186. Retrieved December 14, 2021, from http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0120- 55522017000200178&lng=en&tlng=es.eng
dcterms.referencesSantiago Garcia, M. (2017). Estudio e implementación de algoritmos de Pulse Transit Time (PTT) para la obtención de medidas de presión arterial ambulatoriaspa
dcterms.referencesSchlesinger, O., Vigderhouse, N., Eytan, D., & Moshe, Y. (2020). Blood Pressure Estimation from PPG Signals Using Convolutional Neural Networks and Siamese Network. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2020-May, 1135–1139. https://doi.org/10.1109/ICASSP40776.2020.9053446eng
dcterms.referencesSesso, H. D. , Stampfer, M. J., Rosner, B., Hennekens, C. H., Gaziano, J. M. , Manson, J. E., Glynn R. J. (2000). Systolic and Diastolic Blood Pressure, Pulse Pressure, and Mean Arterial Pressure as Predictors of Cardiovascular Disease Risk in Men. AHA Journals, Hypertension, Vol. 36, No. 5. Hypertension. 2000;36:801–807 de https://doi.org/10.1161/01.HYP.36.5.801eng
dcterms.referencesShaikh, S., Waghole, D., Kumbhar, P., Kotkar, V., & Awaghade, P. (2017). Patient monitoring system using IoT. 2017 International Conference on Big Data, IoT and Data Science (BID), 2018-Janua, 177–181. https://doi.org/10.1109/BID.2017.8336594eng
dcterms.referencesShimazaki, S., Kawanaka, H., Ishikawa, H., Inoue, K., & Oguri, K. (2019). Cuffless Blood Pressure Estimation from only the Waveform of Photoplethysmography using CNN. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 5042–5045. https://doi.org/10.1109/EMBC.2019.8856706eng
dcterms.referencesSociedad Española de Nefrología (2016). “Hipertensión arterial esencial”. Nefrología al Día. Obtenido de: https://www.nefrologiaaldia.org/es-articulo-articulo-hipertension-arterialesencial-23spa
dcterms.referencesSong, K., Chung, K. Y., & Chang, J. H. (2020). Cuffless Deep Learning-Based Blood Pressure Estimation for Smart Wristwatches. IEEE Transactions on Instrumentation and Measurement, 69(7), 4292–4302. https://doi.org/10.1109/TIM.2019.2947103eng
dcterms.referencesSood, S.K., Mahajan, I. (2019). IoT-fog-based healthcare framework to identify and control hypertension attack. IEEE Internet Things J. 6(2), 1920–1927. https://doi.org/10.1109/JIOT.2018.2871630eng
dcterms.referencesSuarez Leon, A. A., & Nunez Alvarez, J. R. (2019). 1D Convolutional Neural Network for Detecting Ventricular Heartbeats. IEEE Latin America Transactions, 17(12), 1970–1977. https://doi.org/10.1109/TLA.2019.9011541eng
dcterms.referencesSuriyal, S., Druzgalski, C., & Gautam, K. (2018). Mobile assisted diabetic retinopathy detection using deep neural network. 2018 Global Medical Engineering Physics Exchanges/Pan American Health Care Exchanges, GMEPE/PAHCE 2018, 562, 1–4. https://doi.org/10.1109/GMEPE-PAHCE.2018.8400760eng
dcterms.referencesTan, P. S., Lim, K. M., Lee, C. P., & Tan, C. H. (2020). Acoustic Event Detection with MobileNet and 1D-Convolutional Neural Network. 2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), 1–6. https://doi.org/10.1109/IICAIET49801.2020.9257865eng
dcterms.referencesTechentin, R. W., Felton, C. L., Schlotman, T. E., Gilbert, B. K., Joyner, M. J., Curry, T. B., Convertino, V. A., Holmes, D. R., & Haider, C. R. (2019). 1D Convolutional Neural Networks for Estimation of Compensatory Reserve from Blood Pressure Waveforms. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2169–2173. https://doi.org/10.1109/EMBC.2019.8857116eng
dcterms.referencesTorres, J. (s.f.). “Hacer Estimaciones Estadísticas”. Uso de técnicas estadísticas para el análisis de datos. Coursera. Tecnológico de Monterrey, México.spa
dcterms.referencesTrejo Ortíz, P. M., Araujo Espino, R., Orozco Gómez, C., Mollinedo Montaño, F. E., Piña Fernández, H. D., Hernández Barrios, F., & Barrios Calderón, J. M. (2012). Factores de riesgo cardiovascular según la etapa de cambio conductual en personal de enfermería. Revista CUIDARTE, 3(1). https://doi.org/10.15649/cuidarte.v3i1.31spa
dcterms.referencesVan Dalen, D. B., & Meyer, W. J. (2006). Síntesis de Estrategia de la investigación descriptiva. Manual de técnica de la investigación educacional.spa
dcterms.referencesVillamarín P. (s.f.). Árboles de decisión – Clasificación. ISIS3301: Inteligencia de Negocios. Departamento de Ingeniería de Sistemas y Computación. Universidad de los Andes. Bogotá, Colombia.spa
dcterms.referencesWan, J., Chen, B., Xu, B., Liu, H., & Jin, L. (2019). Convolutional neural networks for radar HRRP target recognition and rejection. EURASIP Journal on Advances in Signal Processing, 2019(1), 5. https://doi.org/10.1186/s13634-019-0603-yeng
dcterms.referencesWang, D., Yang, X., Liu, X., Fang, S., Ma, L., & Li, L. (2020). Photoplethysmography based stratification of blood pressure using multi information fusion artificial neural network. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2020-June, 1113–1119. https://doi.org/10.1109/CVPRW50498.2020.00146eng
dcterms.referencesWorld Health Organization Global Observatory for eHealth. (2010). Telemedicine: Opportunities and developments in Member States. Observatory, 2, 96. https://doi.org/10.4258/hir.2012.18.2.153eng
dcterms.referencesWorld Health Organization. (2020). ESPECIFICACIONES TÉCNICAS DE LA OMS PARA DISPOSITIVOS AUTOMÁTICOS DE MEDICIÓN DE LA PRESIÓN ARTERIAL NO INVASIVOS Y CON BRAZALETE. https://iris.paho.org/bitstream/handle/10665.2/53145/9789275323052_spa.pdf?sequence= 1&isAllowed=yspa
dcterms.referencesXiaolin, L., Cardiff, B., & John, D. (2020). A 1D Convolutional Neural Network for Heartbeat Classification from Single Lead ECG. 2020 27th IEEE International Conference on Electronics, Circuits and Systems (ICECS), 1–2. https://doi.org/10.1109/ICECS49266.2020.9294838eng
dcterms.referencesYadhav, S. Y., Senthilkumar, T., Jayanthy, S., & Kovilpillai, J. J. A. (2020). Plant Disease Detection and Classification using CNN Model with Optimized Activation Function. 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC), 564–569. https://doi.org/10.1109/ICESC48915.2020.9155815eng
dcterms.referencesYang, C., & Tavassolian, N. (2018). Pulse transit time measurement using seismocardiogram, photoplethysmogram, and acoustic recordings: Evaluation and comparison. IEEE Journal of Biomedical and Health Informatics, 22(3), 733–740. https://doi.org/10.1109/JBHI.2017.2696703eng
dcterms.referencesYetisen, A. K., Martinez-Hurtado, J. L., Ünal, B., Khademhosseini, A., Butt, H. (2018). Wearables in medicine. Advanced Materials, 30(33), 1706910eng
dcterms.referencesYousefian, P., Shin, S., Mousavi, A., Kim, C.-S., Mukkamala, R., Jang, D.-G., Ko, B.-H., Lee, J., Kwon, U. K., Kim, Y. H., & Hahn, J.-O. (2019). The Potential of Wearable Limb Ballistocardiogram in Blood Pressure Monitoring via Pulse Transit Time. Scientific Reports, 9(1), 10666. https://doi.org/10.1038/s41598-019-46936-9eng
dcterms.referencesYu, J., Zhang, C., Wang, S. (2021). Multichannel one-dimensional convolutional neural network-based feature learning for fault diagnosis of industrial processes. Neural Comput & Applic 33, 3085–3104 (2021). https://doi.org/10.1007/s00521-020-05171-4eng
dcterms.referencesYury & Joel (2018). Pulse Transit Time. Tutorial Pulse Sensor. Arduino. PulseSensor. World Famous Electronics llc. Obtenido de https://pulsesensor.com/pages/pulse-transit-timeeng
dcterms.referencesZahid, M. U., Kiranyaz, S., Ince, T., Devecioglu, O. C., Chowdhury, M. E. H., Khandakar, A., Tahir, A., & Gabbouj, M. (2021). Robust R-Peak Detection in Low-Quality Holter ECGs using 1D Convolutional Neural Network. IEEE Transactions on Biomedical Engineering, 1. https://doi.org/10.1109/TBME.2021.3088218eng
dcterms.referencesZamarrón-López, E. I., Guerrero-Gutiérrez, M. A., Nieto-Pérez, O. R., Ramírez-Gutiérrez, Á. E., & Hernández-Villalón, C. E. (2019). Electrocardiografía dirigida para áreas críticas II. In Intensive Qare (Issue June). https://doi.org/10.13140/RG.2.2.34672.51206eng
dcterms.referencesZhang, A., Lipton, Z., Li, M., Smola, A., Werness, B., Hu, R., Zhang, S., Tay, Y., Dagar, A., Tang, Y. (2021). “15.3. Sentiment Analysis: Using Convolutional Neural Networks”. Dive into Deep Learning. Obtenido de https://d2l.ai/chapter_natural-language-processingapplications/sentiment-analysis-cnn.htmleng
dcterms.referencesZhang, Q., Zhou, D., & Zeng, X. (2017). Highly wearable cuff-less blood pressure and heart rate monitoring with single-arm electrocardiogram and photoplethysmogram signals. BioMedical Engineering OnLine, 16(1), 23. https://doi.org/10.1186/s12938-017-0317-zeng
dcterms.referencesZhong, D., Yian, Z., Lanqing, W., Junhua, D., & Jiaxuan, H. (2020). Continuous blood pressure measurement platform: A wearable system based on multidimensional perception data. IEEE Access, 8, 10147–10158. https://doi.org/10.1109/ACCESS.2020.2965245eng
dcterms.referencesМартыненко, Э. (2020). How does Keras 1d convolution layer work with word embeddings - text classification problem? (Filters, kernel size, and all hyperparameter). Java. Javaer101. Obtenido de https://www.javaer101.com/en/article/994241.htmleng
oaire.versioninfo:eu-repo/semantics/acceptedVersionspa
sb.programaMaestría en Ingeniería de Sistemas y Computaciónspa
sb.sedeSede Barranquillaspa

Archivos

Bloque original
Mostrando 1 - 2 de 2
No hay miniatura disponible
Nombre:
PDF.pdf
Tamaño:
3.85 MB
Formato:
Adobe Portable Document Format
Cargando...
Miniatura
Nombre:
PDF_Resumen.pdf
Tamaño:
175.29 KB
Formato:
Adobe Portable Document Format

Colecciones