Automated epileptic seizure detection system based on a wearable prototype and cloud computing to assist people with epilepsy
Epilepsy is characterized by the recurrence of epileptic seizures that affect secondary physiological changes in the patient. This leads to a series of adverse events in the manifestation of convulsions in an uncontrolled environment and without medical help, resulting in risk to the patient, especi...
- Autores:
-
González Vargas, Andrés Mauricio
Escobar Cruz, Juan Nicolás
Solarte, Jhon
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2018
- Institución:
- Universidad Autónoma de Occidente
- Repositorio:
- RED: Repositorio Educativo Digital UAO
- Idioma:
- eng
- OAI Identifier:
- oai:red.uao.edu.co:10614/11414
- Acceso en línea:
- http://hdl.handle.net/10614/11414
- Palabra clave:
- Electrodiagnóstico
Arquitectura en la nube
Electrodiagnosis
Cloud computing architecture
Epileptic seizure detection
Wearable
Electromyography
Cloud computing
- Rights
- openAccess
- License
- Derechos Reservados - Universidad Autónoma de Occidente
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Automated epileptic seizure detection system based on a wearable prototype and cloud computing to assist people with epilepsy |
| title |
Automated epileptic seizure detection system based on a wearable prototype and cloud computing to assist people with epilepsy |
| spellingShingle |
Automated epileptic seizure detection system based on a wearable prototype and cloud computing to assist people with epilepsy Electrodiagnóstico Arquitectura en la nube Electrodiagnosis Cloud computing architecture Epileptic seizure detection Wearable Electromyography Cloud computing |
| title_short |
Automated epileptic seizure detection system based on a wearable prototype and cloud computing to assist people with epilepsy |
| title_full |
Automated epileptic seizure detection system based on a wearable prototype and cloud computing to assist people with epilepsy |
| title_fullStr |
Automated epileptic seizure detection system based on a wearable prototype and cloud computing to assist people with epilepsy |
| title_full_unstemmed |
Automated epileptic seizure detection system based on a wearable prototype and cloud computing to assist people with epilepsy |
| title_sort |
Automated epileptic seizure detection system based on a wearable prototype and cloud computing to assist people with epilepsy |
| dc.creator.fl_str_mv |
González Vargas, Andrés Mauricio Escobar Cruz, Juan Nicolás Solarte, Jhon |
| dc.contributor.author.none.fl_str_mv |
González Vargas, Andrés Mauricio Escobar Cruz, Juan Nicolás Solarte, Jhon |
| dc.subject.armarc.spa.fl_str_mv |
Electrodiagnóstico Arquitectura en la nube |
| topic |
Electrodiagnóstico Arquitectura en la nube Electrodiagnosis Cloud computing architecture Epileptic seizure detection Wearable Electromyography Cloud computing |
| dc.subject.armarc.eng.fl_str_mv |
Electrodiagnosis Cloud computing architecture |
| dc.subject.proposal.eng.fl_str_mv |
Epileptic seizure detection Wearable Electromyography Cloud computing |
| description |
Epilepsy is characterized by the recurrence of epileptic seizures that affect secondary physiological changes in the patient. This leads to a series of adverse events in the manifestation of convulsions in an uncontrolled environment and without medical help, resulting in risk to the patient, especially in people with refractory epilepsy where modern pharmacology is not able to control seizures. The traditional methods of detection based on wired hospital monitoring systems are not suitable for the detection of long-term monitoring in outdoors. For these reasons, this paper proposes a system that can detect generalized tonic-clonic seizures on patients to alert family members or medical personnel for prompt assistance, based on a wearable device (glove), a mobile application and a Support Vector Machine classifier deployed in a system based on cloud computing. In the proposed approach we use Accelerometry (ACC), Electromyography (ECG) as measurement signals for the development of the glove, a machine learning algorithm (SVM) is used to discriminate between simulated tonic-clonic seizures and non-seizure activities that may be confused with convulsions. In this paper, the high level architecture of the system and its implementation based on Cloud Computing are described. Considering the traditional methods of measurement, the detection system proposed in this paper could mean an alternative solution that allows a prompt response and assistance that could be lifesaving in many situations |
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2018 |
| dc.date.issued.none.fl_str_mv |
2018-09-13 |
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2019-11-06T14:38:36Z |
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2019-11-06T14:38:36Z |
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Capítulo - Parte de Libro |
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9783030003531 (en línea) |
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http://hdl.handle.net/10614/11414 |
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10.1007/978-3-030-00353-1_18 |
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9783030003531 (en línea) 10.1007/978-3-030-00353-1_18 |
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http://hdl.handle.net/10614/11414 |
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eng |
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Escobar Cruz, N., Solarte, J., Gonzalez-Vargas, A. (2018). Automated Epileptic Seizure Detection System Based on a Wearable Prototype and Cloud Computing to Assist People with Epilepsy. In: Figueroa-García, J., Villegas, J., Orozco-Arroyave, J., Maya Duque, P. (eds) Applied Computer Sciences in Engineering. WEA 2018. Communications in Computer and Information Science. 916. Springer, Cham. https://doi.org/10.1007/978-3-030-00353-1_18 |
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Communications in Computer and Information Science book series (CCIS,volume 916) |
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Quet, F., Odermatt, P., Preux, P.-M.: Challenges of epidemiological research on epilepsy in resource-poor countries. Neuroepidemiology 30(1), 3–5 (2008). https://doi.org/10.1159/000113299 Kobau, R., et al.: Epilepsy surveillance among adults-19 states, behavioral risk factor surveillance system 2008. MMWR Surveill Summ. 57, 1–20 (2005) Ministry of Health of Colombia. Press Release No 016 of 2017: Epilepsia: Mucho más que convulsiones, https://www.minsalud.gov.co/Paginas/Epilepsia-mucho-mas-que-convulsiones.aspx. Accessed 09 Jan 2017 Tzallas, T., et al.: Automated epileptic seizure detection methods: a review study. In: Chapter 4, pp. 75–98. InTech (2012) Rodriguez, J.J.P.C., Compte, S.S., de la Torre Diez, I.: E-Health Systems: Theory and Technical Applications, 1st edn. Elsevier, New York City (2016) Ulate-Campos, A., Coughlin, F., Gaínza-Lein, M., Fernández, I.S., Pearl, P.L., Loddenkemper, T.: Automated seizure detection systems and their effectiveness for each type of seizure. Seizure 40, 88–101 (2016). Sciencedirect Aghaei, H., Kiani, M.M., Aghajan, H.: Epileptic seizure detection based on video and EEG recordings. In: 2017 IEEE Biomedical Circuits and Systems Conference (BioCAS), Torino, Italy, pp. 1–4 (2017) Embrace. https://www.empatica.com/product-embrace. Accessed Jan 23 2018 Biolert LTDA. https://bio-lert.com. Accesed 23 Jan 2018 Emfit. https://www.emfit.com. Accessed 23 Jan 2018 Bioserenity. http://bioserenity.com/fr. Accessed 23 Jan 2018 Senthilkumar, M., Anbunami, N., Hayavadana, J.: Elastane fabrics – a tool for stretch applications in sports. Indian J. Fibre Text. Res. 36(3), 3 (2011) dos Santos, K.B., Bento, P.C.B., Rodacki, A.L.F.: Efeito do uso do traje de neoprene sobre variáveis técnicas, fisiológicas e perceptivas de nadadores/Effects of a neoprene suit over technical, physiological and perceptive variables of swimmers. Revista Brasileira de Educação Física e Esporte, São Paulo, no. 2, p. 189 (2011) Bitalino, http://bitalino.com/en/learn/documentation. Accessed 21 Apr 2018 Finneran, A., O’Sullivan, L.: Effects of grip type and wrist posture on forearm EMG activity, endurance time and movement accuracy. Int. J. Ind. Ergon. 43(1), 91–99 (2013) Ramgopal, S., et al.: Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy. Epilepsy Behav. 37, 291–307 (2014) System public repository. https://github.com/nicolaxs69/SVM-Epilepsy–Machine-Learning-Classifier. Accessed 28 Apr 2018 Lasboo, A., Fisher, R.S.: Methods for measuring seizure frequency and severity. Neurol. Clin. 34(2), 383–394 (2016). Mayo Types of Seizures and Their Symptoms. WebMD. https://www.webmd.com/epilepsy/types-of-seizures-their-symptoms#1. Accessed 6 Sept 2017 |
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Derechos Reservados - Universidad Autónoma de Occidente |
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González Vargas, Andrés Mauriciovirtual::2066-1Escobar Cruz, Juan Nicolás1d6c8ec14ed6a0859274ca776a7c2393Solarte, Jhonc45117e14651e3aaf59802d09aaaa68aUniversidad Autónoma de Occidente. Calle 25 115-85. Km 2 vía Cali-Jamundí2019-11-06T14:38:36Z2019-11-06T14:38:36Z2018-09-139783030003531 (en línea)http://hdl.handle.net/10614/1141410.1007/978-3-030-00353-1_18Epilepsy is characterized by the recurrence of epileptic seizures that affect secondary physiological changes in the patient. This leads to a series of adverse events in the manifestation of convulsions in an uncontrolled environment and without medical help, resulting in risk to the patient, especially in people with refractory epilepsy where modern pharmacology is not able to control seizures. The traditional methods of detection based on wired hospital monitoring systems are not suitable for the detection of long-term monitoring in outdoors. For these reasons, this paper proposes a system that can detect generalized tonic-clonic seizures on patients to alert family members or medical personnel for prompt assistance, based on a wearable device (glove), a mobile application and a Support Vector Machine classifier deployed in a system based on cloud computing. In the proposed approach we use Accelerometry (ACC), Electromyography (ECG) as measurement signals for the development of the glove, a machine learning algorithm (SVM) is used to discriminate between simulated tonic-clonic seizures and non-seizure activities that may be confused with convulsions. In this paper, the high level architecture of the system and its implementation based on Cloud Computing are described. Considering the traditional methods of measurement, the detection system proposed in this paper could mean an alternative solution that allows a prompt response and assistance that could be lifesaving in many situationsapplication/pdf10 páginasengSpringer, ChamDerechos Reservados - Universidad Autónoma de Occidentehttps://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_abf2Automated epileptic seizure detection system based on a wearable prototype and cloud computing to assist people with epilepsyCapítulo - Parte de Librohttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_3248Textinfo:eu-repo/semantics/bookParthttp://purl.org/redcol/resource_type/ARTREFinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85ElectrodiagnósticoArquitectura en la nubeElectrodiagnosisCloud computing architectureEpileptic seizure detectionWearableElectromyographyCloud computing218204916Escobar Cruz, N., Solarte, J., Gonzalez-Vargas, A. (2018). Automated Epileptic Seizure Detection System Based on a Wearable Prototype and Cloud Computing to Assist People with Epilepsy. In: Figueroa-García, J., Villegas, J., Orozco-Arroyave, J., Maya Duque, P. (eds) Applied Computer Sciences in Engineering. WEA 2018. Communications in Computer and Information Science. 916. Springer, Cham. https://doi.org/10.1007/978-3-030-00353-1_18Communications in Computer and Information Science book series (CCIS,volume 916)Quet, F., Odermatt, P., Preux, P.-M.: Challenges of epidemiological research on epilepsy in resource-poor countries. Neuroepidemiology 30(1), 3–5 (2008). https://doi.org/10.1159/000113299Kobau, R., et al.: Epilepsy surveillance among adults-19 states, behavioral risk factor surveillance system 2008. MMWR Surveill Summ. 57, 1–20 (2005)Ministry of Health of Colombia. Press Release No 016 of 2017: Epilepsia: Mucho más que convulsiones, https://www.minsalud.gov.co/Paginas/Epilepsia-mucho-mas-que-convulsiones.aspx. Accessed 09 Jan 2017Tzallas, T., et al.: Automated epileptic seizure detection methods: a review study. In: Chapter 4, pp. 75–98. InTech (2012)Rodriguez, J.J.P.C., Compte, S.S., de la Torre Diez, I.: E-Health Systems: Theory and Technical Applications, 1st edn. Elsevier, New York City (2016)Ulate-Campos, A., Coughlin, F., Gaínza-Lein, M., Fernández, I.S., Pearl, P.L., Loddenkemper, T.: Automated seizure detection systems and their effectiveness for each type of seizure. Seizure 40, 88–101 (2016). SciencedirectAghaei, H., Kiani, M.M., Aghajan, H.: Epileptic seizure detection based on video and EEG recordings. In: 2017 IEEE Biomedical Circuits and Systems Conference (BioCAS), Torino, Italy, pp. 1–4 (2017)Embrace. https://www.empatica.com/product-embrace. Accessed Jan 23 2018Biolert LTDA. https://bio-lert.com. Accesed 23 Jan 2018Emfit. https://www.emfit.com. Accessed 23 Jan 2018Bioserenity. http://bioserenity.com/fr. Accessed 23 Jan 2018Senthilkumar, M., Anbunami, N., Hayavadana, J.: Elastane fabrics – a tool for stretch applications in sports. Indian J. Fibre Text. Res. 36(3), 3 (2011)dos Santos, K.B., Bento, P.C.B., Rodacki, A.L.F.: Efeito do uso do traje de neoprene sobre variáveis técnicas, fisiológicas e perceptivas de nadadores/Effects of a neoprene suit over technical, physiological and perceptive variables of swimmers. Revista Brasileira de Educação Física e Esporte, São Paulo, no. 2, p. 189 (2011)Bitalino, http://bitalino.com/en/learn/documentation. Accessed 21 Apr 2018Finneran, A., O’Sullivan, L.: Effects of grip type and wrist posture on forearm EMG activity, endurance time and movement accuracy. Int. J. Ind. Ergon. 43(1), 91–99 (2013)Ramgopal, S., et al.: Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy. Epilepsy Behav. 37, 291–307 (2014)System public repository. https://github.com/nicolaxs69/SVM-Epilepsy–Machine-Learning-Classifier. Accessed 28 Apr 2018Lasboo, A., Fisher, R.S.: Methods for measuring seizure frequency and severity. Neurol. Clin. 34(2), 383–394 (2016). MayoTypes of Seizures and Their Symptoms. WebMD. https://www.webmd.com/epilepsy/types-of-seizures-their-symptoms#1. 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