dc.contributorGonzález Vargas, Andrés Mauricio
dc.contributorCastro Benavídes, David Alejandro
dc.creatorEscobar Cruz, Juan Nicolás
dc.creatorSolarte Vargas, John Carlos
dc.date.accessioned2018-09-04T16:46:17Z
dc.date.accessioned2022-09-22T18:51:46Z
dc.date.available2018-09-04T16:46:17Z
dc.date.available2022-09-22T18:51:46Z
dc.date.created2018-09-04T16:46:17Z
dc.date.issued2018-05-29
dc.identifierhttp://hdl.handle.net/10614/10283
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/3459900
dc.description.abstractEpilepsy is characterized by the recurrence of epileptic seizures that affect secondary physiological changes in the patient. This leads to a series of adverse events at the time of a seizure in an uncontrolled environment and without medical help, which further increases the risk of the patient, especially in people with refractory epilepsy where modern pharmacology is not able to control the seizures Traditional detection methods based on clinical wired monitoring systems are not adequate for crisis detection and long-term outdoor monitoring. For these reasons, this project proposes a system that allows the detection of generalized tonic-clonic seizures in patients to alert family members or medical personnel for immediate assistance, based on a portable device (bracelet), a mobile application and a Classifier Support Vector Machine deployed in a system based on cloud computing. In the proposed approach we use Accelerometry (ACC), Electromyography (ECG) and Electrodermal Activity (EDA) as measurement signals for the development of the bracelet, an automatic learning algorithm (SVM) is used to discriminate between a seizure and non-convulsion. In addition, the high level architecture of the system is described, as well as the implementation of the ecosystem based on Cloud Computing for the execution of the algorithm and the communication of the mobile application with bracelet. For the development of the system a methodology based on the treatise of Karl T. Ulrich "Design and development of products and methodology of Jesus David Cardona Quiroz" Development of multimedia systems "was proposed. The development of the system can be said to include two major components, the first research component where a state of the art about the symptoms and effects of a tonic-clonic seizure is performed in order to analyze the patients' problems. Also, the necessary technology to develop the system, detection methods and algorithms, and software architectures in the cloud to be taken into account for the design and production of the system. The second component includes everything related to the production of the system where the device used by the user is designed and developed, as well as the development of the algorithm to detect the seizure and the servers needed to host the algorithm and display the alert Finally, the system is put to the test in a sample of volunteers using the prototype during the development of their daily activities, in order to evaluate their performance and integral functioning
dc.languagespa
dc.publisherUniversidad Autónoma de Occidente
dc.publisherIngeniería Biomédica
dc.publisherIngeniería Multimedia
dc.publisherDepartamento de Automática y Electrónica
dc.publisherDepartamento de Operaciones y Sistemas
dc.publisherFacultad de Ingeniería
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
dc.rightsDerechos Reservados - Universidad Autónoma de Occidente
dc.sourceinstname:Universidad Autónoma de Occidente
dc.sourcereponame:Repositorio Institucional UAO
dc.sourceAbout epilepsy [En linea]. U.S Department of veteran affairs. [Consultado 17 enero 2017] Disponible en internet: www.epilepsy.va.gov/Information/about.asp Advance Health Informatics [En linea]. National Academy of Engineering. parr. 8-9. [Consultado: 12 enero 2017], Disponible en internet: http://www.engineeringchallenges.org/8938.aspx AGHAEI, Hoda, KIANI, Mohammad y AGHAJAN, Hamid. Epileptic seizure detection based on video and EEG recordings. En: 2017 IEEE Biomedical Circuits and Systems Conference (BioCAS). Ocubre, 2017. p. 1-4. LASBOO, A, FISHER, RS. Methods for Measuring Seizure Frequency and Severity. En: Neurol Clinics. Mayo, 2016, vol. 34, no. 2, p. 383-394. DALTON, Anthony, et al. Development of a Body Sensor Network to Detect Motor Patterns of Epileptic Seizures. En: IEEE Trans Biomed Engineering. Noviembre, 2012, vol. 59, no. 11, p. 3204 - 3211.A STHON, Kevin. That 'Internet of Things' Thing [En linea]. RFID journal. (22 de Junio de 2009). [Consultado: 12 junio de 2017]. Disponible en internet: https://goo.gl/dW9PQ1 AspireSR Homepage [En línea]. Aspire. [Consultado 22 enero 2017]. Disponible en internet: http://www.aspiresr.com BENICZKY, Sándor, et al. Quantitative analysis of surface electromyography: Biomarkers for convulsive seizures. En: Clinical Neurophysiology. Agosto, 2016, vol. 127, no. 8, p. 2900-2907 Biolert LTDA Homepage [En línea]. Biolert LTDA [Consultado 12 enero 2017]. Disponible en internet: https://goo.gl/LEfH5B Bioserenity Homepage [En línea]. Bioserenity. [Consultado 12 enero 2017]. Disponible en internet: http://www.bioserenity.com/en.html Bradley J. Rhodes. The Wearable Remembrance Agent: A system for augmented memory in Personal Technologies Journal Special Issue on Wearable Computing, Personal Technologies (1997) 1(4) [Consultado: 26 febrero 2017], pp. 218-224. BOLTON, Anastasiya. Store clerk saves baby as mother has seizure [En linea]. En: 9News. 16, marzo, 2016 [Consultado: 9 de enero 2017]. Disponible en internet: https://goo.gl/Efhz15 CAMPOS, A, et al. Automated seizure detection systems and their effectiveness for each type of seizure. En: Elsevier. Agosto, 2016, vol. 40. p. 88-101. CARDONA, Jesús. Apuntes de la clase Diseño Multimedia 1 dictada por el profesor Jesús David Cardona Quiroz, 2015 CHU, Hyunho, et al. Predicting epileptic seizures from scalp EEG based on attractor state analysis. En: Computer Methods and Programs in Biomedicine. Mayo, 2017, vol. 143, p. 75-87, ISSN 0169-2607. Empatica Homepage [En línea]. Empatica. [Consultado 12 enero 2017] Disponible en internet: https://www.empatica.com/product-embrace Epilepsia: Mucho más que convulsion [En linea]. Ministerio de salud Colombia. Bogota. (13 de Febrero de 2017). [Consultado: 09 de marzo de 2017]. Disponible en internet: https://www.minsalud.gov.co/Paginas/Epilepsia-mucho-mas-que-convulsiones.aspx. Epilepsy [En linea]. MedlinePlus. [Consultado 17 enero 2017] disponible en internet: www.nlm.nih.gov/medlineplus/epilepsy.html Epilepsy Health Center [En linea]. WebMD [Consultado 17 enero 2017]. Disponible en internet: www.webmd.com/epilepsy EVANS, Dave. The internet of things. How the next evolution of the internet is changing everything [En linea]. Cisco IBSG. (Abril de 2011). [Consultado: 12 mayo de 2017]. Disponible en internet: https://goo.gl/LLMM4Y FÜRBASS, F, et al. Automatic multimodal detection for long-term seizure documentation in Epilepsy. En: Clinical Neurophysiology. Agosto, 2017, vol. 128, no. 8, p. 1466-1472. GARTNER [En línea]. Hype Cycle for Emerging Technologies [Consultado el 11 de enero de 2016]. Disponible en internet: http://goo.gl/NixFTt GUYON, Isabelle, ELISSEEFF, Andre. An introduction to variable and feature selection. En: The journal of machine learning research. Enero, 2003, vol. 3, p. 1157-1182. ISSN: 1532-4435 CONRADSEN, I, et al. Patterns of muscle activation during generalized tonic and tonic–clonic epileptic seizures. En: Epilepsia, Noviembre, 2011, vol. 52, no. 11, p. 2125-2132. JSEUNGSONG. ComMag-IoT 2015 : Internet of Things/M2M from Research to Standards: The Next Steps - 2 week deadline extension [En linea]. Wikicfp. Parr 1. [Consultado: 14 abril de 2017]. Disponible en internet: https://bit.ly/2K14KkE LOCKMAN, Juliana, FISHER, Robert, OLSON, Donald. Detection of seizure-like movements using a wrist accelerometer. En: Epilepsy & behavior. Abril, 2011, vol. 20, no. 4, p. 638–641. KOBAU, R, et al. Centers for Disease Control and Prevention (CDC): Epilepsy surveillance among adults-19 states, Behavioral Risk Factor Surveillance System. En: MMWR Surveill Summ. Agosto, 2008, vol. 57, no. 6, p. 1–20. KORTUERN, G, SEGALL, Z and BAUER, M. Context-Aware, Adaptive Wearable Computers as Remote Interfaces to Intelligent Environments. En: IEEE. Agosto, 2002. Las epilepsias y las crisis: Esperanza en la investigación [En linea]. National Institute of Neurological Disorders and Stroke. [Consultado 17 enero 2017]. Disponible en internet: español.ninds.nih.gov/trastornos/crisis_epilepticas.htm MANN, Samuel. Wearable computing as means for personal empowerment. Enero, 1994. Mental Health [En linea]. World health organization. [Consultado: 11 de enero 2017] Disponible en internet: https://goo.gl/vjedwa MAYO CLINIC STAFF. Vagus nerve stimulation risks [En línea]. Mayo clinic. [Consultado 22 enero 2017]. Disponible en internet: https://goo.gl/D3sWn4 MILOSEVIC, M, et al. Automated Detection of Tonic-Clonic Seizures Using 3-D Accelerometry and Surface En: IEEE J Biomed Health Informatics. Septiembre, 2015, vol. 20, no. 5, p. 1333-1341. POH, Ming, SWENSON, Nicholas, PICARD, Rosalid. A Wearable Sensor for Unobtrusive, Long-Term Assessment of Electrodermal Activity. En: IEEE transactions on bio-medical engineering. Febrero, 2010, vol. 57, no. 5. p. 1243 – 1252. ISSN: 0018-9294 MOUNT, John. Kernel Methods and Support Vector Machines de-Mystified. Win-vector.(7 de Octubre de 2011). [Consultado 11 de julio 2017]. Disponible en internet: http://www.win-vector.com/blog/2011/10/kernel-methods-and-support-vector-machines-de-mystified/ NG, Andrew, BONEH, Dan. CS229: Machine Learning [En linea]. Stanfor University. [Consultado: 20 febrero de 2017]. Disponible en internet: http://cs229.stanford.edu/. OPHERK, Christian, COROMILAS, James, HIRSCH, Lawrence. Heart rate and EKG changes in 102 seizures: analysis of influencing factors. En: Epilepsy research. Diciembre, 2002, vol. 52, no. 2, p. 117-127 PRESSMAN, Roger. Ingeniería de Software Un enfoque práctico. 7 ed. Mexico: Mc Graw Hill, 2010. 777 p. ISBN: 978-607-15-0314-5. PICARD, Rosalind. Detecting Seizures and their Autonomic impact with a Wristband [En linea]. Affective computing. [Consultado: el 25 de Enero 2017]. Disponible en internet: http://affect.media.mit.edu/projectpages/epilepsy/ POH, Ming, et al. Convulsive seizure detection using a wrist-worn electrodermal activity and accelerometry biosensor. En: Epilepsia. Official journal of the international league against epilepsy. Mayo, 2012, vol. 53, no. 5, p. 93-97. QUET, Fabrice, ODERMATT, Peter, PREUX Pierre-Marie. Challenges of epidemiological research on epilepsy in resource-poor countries. En: Neuroepidemiology. Junio, 2008, vol. 30, no. 1, p. 3-5. doi:10.1159/000113299. RASCHKA, Sebastian. How to Select Support Vector Machine Kernels [En linea]. KDnuggets. Michigan State University. [Consultado 11 de julio 2017]. Disponible en internet: https://www.kdnuggets.com/2016/06/select-support-vector-machine-kernels.html RCN RADIO. En Colombia 450 mil personas padecen de epilepsia [En linea]. En: RCN Radio. 26, Marzo, 2017. [Consultado: 11 de enero 2017]. Disponible en internet: http://www.rcnradio.com/salud/colombia-450-mil-personas-padecen-epilepsia/ RHODES, Bradley. The Wearable Remembrance Agent: A system for augmented memory [En linea]. MIT. Cambridge. [Consultado: 26 febrero 2017], Disponible en internet: http://alumni.media.mit.edu/~rhodes/Papers/wear-ra-personaltech/ SABINO, Carlos. El Proceso de Investigación [En linea]. Bogota: Ed. Paramericana, 1992. 171 p. [Consultado: 12 de Febrero de 2017 ]. Disponible en internet: http://paginas.ufm.edu/sabino/word/proceso_investigacion.pdf SANCHEZ, Alvarez, et al. Epilepsia resistente a farmacos. En: Sociedad Española de Neurología, Noviembre, 2012, vol. 27, no.9. p. 575-584. SHAFER, Patricia. Vagus nerve stimulation (VNS) [En linea]. Epilspy foundation. [Consultado 15 enero 2017]. Disponible en internet: https://bit.ly/2uBWH8F SHARIK, Babak, JAFARI, Amir. Prediction of epileptic seizures from EEG using analysis of ictal rules on Poincaré plane. En: Elsevier. Julio, 2017, vol. 145, p. 11-22. Smartmonitor Homepage [En línea]. SmartMonitor. [Consultado 12 enero 2017]. Disponible en internet: https://goo.gl/FUCfBm SOUZA, Cesar. Kernel Functions for Machine Learning Applications. Cesar Souza. (17 de Marzo de 2010). [Consultado 11 de julio 2017]. Disponible en internet: http://crsouza.com/2010/03/17/kernel-functions-for-machine-learning-applications/ STAMFORD, Conn. Hype Cycle for Emerging Technologies [En línea]. GARTNER. (18 de Agosto de 2015). [Consultado el 11 de enero de 2016]. Disponible en internet: http://goo.gl/NixFT STARNER, T. Wearable Computers: No Longer Science Fiction. En: IEE pervasive computing. Marzo, 2002, vol. 1, no. 1, p. 86-88. Support Vector Machines [En linea]. Scikit-learn. [Consultado 11 de julio 2017]. Disponible en internet: http://scikit-learn.org/stable/modules/svm.html SWANBOROUGH, Nicola. Having a seizure while cycling [En linea]. Epilepsy Society. (22 de Octubre de 2015). [Consultado: 9 de enero 2017]. Disponible en internet: https://goo.gl/KK5D47 SWANBOROUGH, Nicola. The hardest time of my life [En linea]. Epilepsy Society. (4 de Noviembre de 2015). [Consultado: 9 de enero 2017]. Disponible en internet: https://goo.gl/jKWJq1 SWANBOROUGH, Nicola. Report shows GPs need more support to treat people with epilepsy [En linea]. Epilepsy Society. (25 de Agosto de 2016). [Consultado: 9 de enero 2017]. Disponible en internet: https://goo.gl/wc9rjw TZALLAS, Alexandros, et al. Automated Epileptic Seizure Detection Methods. En: DEJAN, Stevanovic. Epilepsy - Histological, Electroencephalographic and Psychological Aspects. Croatia: InTech, 2012. p. 75–98. NIJSEN, Tamara, et al. The potential value of three-dimensional accelerometry for detection of motor seizures in severe epilepsy. En: Epilepsy and behavior. Agosto, 2005, vol. 7, no. 1, p. 74-84. TIGARAN S, et al. ECG changes in epilepsy patients. En : Acta neurologica scandinavica. Agosto, 1997, vol. 96, no.2, p. 72-75. TÓTH, et al. Effect of epileptic seizures on the heart rate. En: Ideggyogyaszati szemle. Mayo, 2008, vol. 61, no. 5-6, p. 155-161. TRUJILLO ACOSTA, Oscar. Microsoft Azure Machine Learning para detección de distintos tipos de cáncer. Universidad de la Laguna. (5 de julio de 2016). [Consultado 10 de julio 2017]. Disponible en web : https://goo.gl/KpwnYL TSCHOFENING, Hannes, et al. Architectural Considerations in Smart Object Networking [En linea]. IETF. (Marzo de 2015), parr. 1. [Consultado: 12 junio de 2017]. Disponible en internet: https://tools.ietf.org/html/rfc7452 ULRICH, Karl, EPPINGER, Steven. Diseño y desarrollo de productos. 5 ed. Mexico: McGraw-Hill Education, 2016. 409 p. ISBN: 978-607-15-0944-4 VAN DE VEL, Anouk, et al. Non-EEG seizure detection systems and potential SUDEP prevention: State of the art. En: European Journal of Epilepsy. Enero, 2016, vol. 41, p. 141-153. VELEZ, Mariel, et al. Tracking generalized tonic-clonic seizures with a wrist accelerometer linked to an online database. En: Elsevier. Julio, 2016, vol. 39, p. 13-18. Types of Seizures and Their Symptoms. WebMD. [Consultado 6 de septiembre 2017]. Disponible en internet: https://www.webmd.com/epilepsy/types-of-seizures-their-symptoms#1 WRIGHT, Cynthia, FRIEDMAN Daniel. The role of seizure alerts [En linea]. Epilepsy foundation. (Octubre de 2013). [Consultado 12 enero 2017]. Disponible en internet: https://goo.gl/BtkJ31 XIE, Michael, PAN, David. Acelerometer Gesture Recognition [En linea]. Stanford University. (12 de Diciembre de 2014). [Consultado 23 abril 2017]. Disponible en internet: https://stanford.io/2yq2W3q ZIJLMANS, M, FLANAGAN, D, GOTMAN J. Heart rate changes and ECG abnormalities during epileptic seizures: prevalence and definition of an objective clinical sign. En: Epilepsia. Agosto, 2002, vol. 43, no. 8, p. 847-854.
dc.subjectIngeniería Multimedia
dc.subjectIngeniería Medicable
dc.subjectSistemas de computador embebidos
dc.subjectComputación en la nube
dc.subjectInternet de las cosas
dc.subjectAplicaciones móviles
dc.subjectEpilepsia
dc.subjectElectromiografía
dc.subjectAcelerómetro
dc.titleDesarrollo de un prototipo de dispositivo basado en prendas tecnológicas para la asistencia inmediata en personas con epilepsia
dc.typeTrabajo de grado - Pregrado


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