Machine learning estimation of an arterial pressure model using electrical impedance
Cardiovascular System Diseases (CVD) are among the most common causes of death and illness in the world. Arterial pressures is a good indicator of CVD existence. However, measuring arterial pressure commonly involves either invasive techniques that require catheter insertion, or noninvasive oscillom...
- Autores:
-
Romero Beltrán, César Augusto
Murillo Riascos, Yan Carlos
González Vargas, Andrés Mauricio
Cabrera Lopez, John Jairo
- Tipo de recurso:
- Conferencia (Ponencia)
- Fecha de publicación:
- 2022
- Institución:
- Universidad Autónoma de Occidente
- Repositorio:
- RED: Repositorio Educativo Digital UAO
- Idioma:
- eng
- OAI Identifier:
- oai:red.uao.edu.co:10614/14803
- Acceso en línea:
- https://hdl.handle.net/10614/14803
https://red.uao.edu.co/
- Palabra clave:
- Ingeniería biomédica
Biomedical engineering
Arterial pressure
Machine learning
Regression
Bioinstrumentation
Physiological models
- Rights
- openAccess
- License
- Derechos reservados - IEEE, 2022
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Machine learning estimation of an arterial pressure model using electrical impedance |
| title |
Machine learning estimation of an arterial pressure model using electrical impedance |
| spellingShingle |
Machine learning estimation of an arterial pressure model using electrical impedance Ingeniería biomédica Biomedical engineering Arterial pressure Machine learning Regression Bioinstrumentation Physiological models |
| title_short |
Machine learning estimation of an arterial pressure model using electrical impedance |
| title_full |
Machine learning estimation of an arterial pressure model using electrical impedance |
| title_fullStr |
Machine learning estimation of an arterial pressure model using electrical impedance |
| title_full_unstemmed |
Machine learning estimation of an arterial pressure model using electrical impedance |
| title_sort |
Machine learning estimation of an arterial pressure model using electrical impedance |
| dc.creator.fl_str_mv |
Romero Beltrán, César Augusto Murillo Riascos, Yan Carlos González Vargas, Andrés Mauricio Cabrera Lopez, John Jairo |
| dc.contributor.author.none.fl_str_mv |
Romero Beltrán, César Augusto Murillo Riascos, Yan Carlos González Vargas, Andrés Mauricio Cabrera Lopez, John Jairo |
| dc.subject.armarc.spa.fl_str_mv |
Ingeniería biomédica |
| topic |
Ingeniería biomédica Biomedical engineering Arterial pressure Machine learning Regression Bioinstrumentation Physiological models |
| dc.subject.armarc.eng.fl_str_mv |
Biomedical engineering |
| dc.subject.proposal.eng.fl_str_mv |
Arterial pressure Machine learning Regression Bioinstrumentation Physiological models |
| description |
Cardiovascular System Diseases (CVD) are among the most common causes of death and illness in the world. Arterial pressures is a good indicator of CVD existence. However, measuring arterial pressure commonly involves either invasive techniques that require catheter insertion, or noninvasive oscillometric techniques that require inflating a cuff around the arm and don’t provide continuous information. Recently, new methods are being develop to provide continuous, reliable and comfortable measuring of arterial pressure. One promising technique involves using Electrical Impedance (EI) in a highly vascularized segment of the body (such as an arm) to estimate the arterial pressure in that segment. In this paper, we present an experimental setup which includes a gelatin model that emulates some physical and electrical properties of the forearm, an automated system to control pressure and measure EI in such model, and a computational method that makes use of regression algorithms in order to predict the pressure value based on the EI magnitude and phase values |
| publishDate |
2022 |
| dc.date.issued.none.fl_str_mv |
2022-07 |
| dc.date.accessioned.none.fl_str_mv |
2023-05-29T15:16:46Z |
| dc.date.available.none.fl_str_mv |
2023-05-29T15:16:46Z |
| dc.type.spa.fl_str_mv |
Documento de Conferencia |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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http://purl.org/coar/resource_type/c_8544 |
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Text |
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info:eu-repo/semantics/lecture |
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http://purl.org/coar/resource_type/c_8544 |
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publishedVersion |
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https://hdl.handle.net/10614/14803 |
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Universidad Autónoma de Occidente |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio Educativo Digital UAO |
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https://red.uao.edu.co/ |
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https://hdl.handle.net/10614/14803 https://red.uao.edu.co/ |
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Universidad Autónoma de Occidente Repositorio Educativo Digital UAO |
| dc.language.iso.spa.fl_str_mv |
eng |
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eng |
| dc.relation.cites.spa.fl_str_mv |
Romero Beltrán, C.A., Murillo Riascos, Y.C., González Vargas, A.M., Cabrera López, J.J. Machine learning estimation of an arterial pressure model using electrical impedance. 2022 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI), pp. 1-6 . doi: 10.1109/ColCACI56938.2022.9905315. |
| dc.relation.conferencedate.spa.fl_str_mv |
27-29 julio 2022 |
| dc.relation.conferenceplace.spa.fl_str_mv |
Cali |
| dc.relation.ispartofconference.eng.fl_str_mv |
2022 IEEE Colombian Conference on Applications of computational Intelligence |
| dc.relation.references.none.fl_str_mv |
"Technical package for cardiovascular disease management in primary health care", Report, pp. 73, 2016. J. Schneider, M. Schroth, M. Holzhey, T. Blocher and W. Stork, "An approach to improve impedance plethysmography on the wrist by using adaptive feedback control", 2017 IEEE Sensors Applications Symposium (SAS), pp. 1-6, 2017 M. Metshein, P. Annus, R. Land, M. Min and A. Aabloo, "Availability and variations of cardiac activity in the case of measuring the bioimpedance of wrist", 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), pp. 1-5, 2018. H. Ji Jer, H. Yang Min and C. Ming Wen, "Using bioimpedance plethysmography for measuring the pulse wave velocity of peripheral vascular", 2016 13th International Conference on Electrical Engineering/Electronics Computer Telecommunications and Information Technology (ECTI-CON), pp. 1-5, Jun. 2016 E. Piuzzi et al., "Impedance plethysmography system with inertial measurement units for motion artefact reduction: Application to continuous breath activity monitoring", 2015 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Proceedings, pp. 386-390, May 2015 P. U. P. K. Ram, N. PM. R. K. V. J. Joseph and M. Sivaprakasam, "Blood Pressure Estimation using Arterial Diameter: Exploring Different Machine Learning Methods", 2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA), pp. 1-6, Jun. 2020 M. Kachuee, M. M. Kiani, H. Mohammadzade and M. Shabany, "Cuff- less high-accuracy calibration-free blood pressure estimation using pulse transit time", 2015 IEEE International Symposium on Circuits and Systems (ISCAS), vol. 2015, no. 2, pp. 1006-1009, May 2015 J. J. Cabrera Lopez and J. Velasco Medina, "Structured Approach and Impedance Spectroscopy Microsystem for Fractional-Order Electrical Characterization of Vegetable Tissues", IEEE Trans. Instrum. Meas, vol. 69, no. 2, pp. 469-478, Feb. 2020. Ø. G. Grimnes and Sverre Martinsen, Bioimpedance and Bioelectricity Basics, 2015. |
| dc.rights.spa.fl_str_mv |
Derechos reservados - IEEE, 2022 |
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http://purl.org/coar/access_right/c_abf2 |
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https://creativecommons.org/licenses/by-nc-nd/4.0/ |
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info:eu-repo/semantics/openAccess |
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Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0) |
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Derechos reservados - IEEE, 2022 https://creativecommons.org/licenses/by-nc-nd/4.0/ Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0) http://purl.org/coar/access_right/c_abf2 |
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10.1109/ColCACI56938.2022.9905315 |
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Universidad Autónoma de Occidente |
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Romero Beltrán, César Augustoaee993912526e61fa10eec873c015177Murillo Riascos, Yan Carlose3e32cbede3365ef537d535e6d7b338cGonzález Vargas, Andrés Mauriciovirtual::2106-1Cabrera Lopez, John Jairovirtual::746-12023-05-29T15:16:46Z2023-05-29T15:16:46Z2022-07https://hdl.handle.net/10614/14803Universidad Autónoma de OccidenteRepositorio Educativo Digital UAOhttps://red.uao.edu.co/Cardiovascular System Diseases (CVD) are among the most common causes of death and illness in the world. Arterial pressures is a good indicator of CVD existence. However, measuring arterial pressure commonly involves either invasive techniques that require catheter insertion, or noninvasive oscillometric techniques that require inflating a cuff around the arm and don’t provide continuous information. Recently, new methods are being develop to provide continuous, reliable and comfortable measuring of arterial pressure. One promising technique involves using Electrical Impedance (EI) in a highly vascularized segment of the body (such as an arm) to estimate the arterial pressure in that segment. In this paper, we present an experimental setup which includes a gelatin model that emulates some physical and electrical properties of the forearm, an automated system to control pressure and measure EI in such model, and a computational method that makes use of regression algorithms in order to predict the pressure value based on the EI magnitude and phase values6 páginasapplication/pdfengIEEECaliDerechos reservados - IEEE, 2022https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)http://purl.org/coar/access_right/c_abf210.1109/ColCACI56938.2022.9905315Machine learning estimation of an arterial pressure model using electrical impedanceDocumento de Conferenciahttp://purl.org/coar/resource_type/c_8544Textinfo:eu-repo/semantics/lectureinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Ingeniería biomédicaBiomedical engineeringArterial pressureMachine learningRegressionBioinstrumentationPhysiological modelsRomero Beltrán, C.A., Murillo Riascos, Y.C., González Vargas, A.M., Cabrera López, J.J. Machine learning estimation of an arterial pressure model using electrical impedance. 2022 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI), pp. 1-6 . doi: 10.1109/ColCACI56938.2022.9905315.27-29 julio 2022Cali2022 IEEE Colombian Conference on Applications of computational Intelligence"Technical package for cardiovascular disease management in primary health care", Report, pp. 73, 2016.J. Schneider, M. Schroth, M. Holzhey, T. Blocher and W. Stork, "An approach to improve impedance plethysmography on the wrist by using adaptive feedback control", 2017 IEEE Sensors Applications Symposium (SAS), pp. 1-6, 2017M. Metshein, P. Annus, R. Land, M. Min and A. Aabloo, "Availability and variations of cardiac activity in the case of measuring the bioimpedance of wrist", 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), pp. 1-5, 2018.H. Ji Jer, H. Yang Min and C. Ming Wen, "Using bioimpedance plethysmography for measuring the pulse wave velocity of peripheral vascular", 2016 13th International Conference on Electrical Engineering/Electronics Computer Telecommunications and Information Technology (ECTI-CON), pp. 1-5, Jun. 2016E. Piuzzi et al., "Impedance plethysmography system with inertial measurement units for motion artefact reduction: Application to continuous breath activity monitoring", 2015 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Proceedings, pp. 386-390, May 2015P. U. P. K. Ram, N. PM. R. K. V. J. Joseph and M. Sivaprakasam, "Blood Pressure Estimation using Arterial Diameter: Exploring Different Machine Learning Methods", 2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA), pp. 1-6, Jun. 2020M. Kachuee, M. M. Kiani, H. Mohammadzade and M. Shabany, "Cuff- less high-accuracy calibration-free blood pressure estimation using pulse transit time", 2015 IEEE International Symposium on Circuits and Systems (ISCAS), vol. 2015, no. 2, pp. 1006-1009, May 2015J. J. Cabrera Lopez and J. Velasco Medina, "Structured Approach and Impedance Spectroscopy Microsystem for Fractional-Order Electrical Characterization of Vegetable Tissues", IEEE Trans. Instrum. Meas, vol. 69, no. 2, pp. 469-478, Feb. 2020.Ø. G. Grimnes and Sverre Martinsen, Bioimpedance and Bioelectricity Basics, 2015.Comunidad generalPublication6053e64e-a34d-4652-8fa2-6c0440556f15virtual::2106-15f003138-bfcd-4407-904b-9b9a0010990cvirtual::746-15f003138-bfcd-4407-904b-9b9a0010990cvirtual::746-16053e64e-a34d-4652-8fa2-6c0440556f15virtual::2106-1https://scholar.google.com.co/citations?user=oj5Tle8AAAAJ&hl=esvirtual::2106-1https://scholar.google.com/citations?user=dkpsiDsAAAAJ&hl=esvirtual::746-10000-0001-6393-7130virtual::2106-10000-0002-2608-755Xvirtual::746-1https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001345355virtual::2106-1https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000821276virtual::746-1LICENSElicense.txtlicense.txttext/plain; charset=utf-81665https://red.uao.edu.co/bitstreams/dbb721e7-5cbd-4112-8cb5-5b17aaa272b3/download20b5ba22b1117f71589c7318baa2c560MD5210614/14803oai:red.uao.edu.co:10614/148032024-04-15 14:18:20.896https://creativecommons.org/licenses/by-nc-nd/4.0/Derechos reservados - IEEE, 2022metadata.onlyhttps://red.uao.edu.coRepositorio Digital Universidad Autonoma de Occidenterepositorio@uao.edu.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 |