Skip to main content
SpringerLink
Log in

Fuzzy Intelligent System for Supporting Preeclampsia Diagnosis from the Patient Biosignals

  • Conference paper
  • First Online:
Ubiquitous Computing and Ambient Intelligence (UCAmI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10069))

Abstract

This contribution presents a proposal for generating linguistic reports based on the study of biomedical signals of human patients. Although this topic is dealt in many previous works, there are challenges still open for the scientific community, such as the development of systems to produce reports and alerts using a human-friendly language. We present a brief review of some relevant previous works, as well as our proposal of a system based on fuzzy linguistic approach applied to the diagnosis of the preeclampsia disease that may affect pregnant women. Our system transforms numerical values of biomedical signals into linguistic values that are understandable information for the patients and the medical staff. The dataset used for testing the system contains real data from a study carried out by the Davinci UNAD Group (Colombia) on patients that suffer from preeclampsia.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ramos-Soto, A., Bugarin, A.J., Barro, S., Taboada, J.: Linguistic descriptions for automatic generation of textual short-term weather forecasts on real prediction data. IEEE Trans. Fuzzy Syst. 23(1), 44–57 (2015)

    Article  Google Scholar 

  2. Uribe, T.M.: El autocuidado y su papel en la promoción de la salud. Investigación y Educación en Enfermería, vol. 17, no. 2 (2013)

    Google Scholar 

  3. Karahoca, A., Karahoca, D., Şanver, M.: Survey of Data Mining and Applications (Review from 1996 to Now) (2012)

    Google Scholar 

  4. Bhunia, S.S., Dhar, S.K., Mukherjee, N.: iHealth: a fuzzy approach for provisioning intelligent health-care system in smart city. In: 2014 IEEE 10th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), pp. 187–193 (2014)

    Google Scholar 

  5. Baig, M.M., Gholamhosseini, H.: Smart health monitoring systems: an overview of design and modeling. J. Med. Syst. 37(2), 1–14 (2013)

    Article  Google Scholar 

  6. Kar, S., Das, S., Ghosh, P.K.: Applications of neuro fuzzy systems: a brief review and future outline. Appl. Soft Comput. 15, 243–259 (2015)

    Article  Google Scholar 

  7. Touati, F., Tabish, R.: U-healthcare system: state-of-the-art review and challenges. J. Med. Syst. 37(3), 1–20 (2013)

    Article  Google Scholar 

  8. Banaee, H., Ahmed, M.U., Loutfi, A.: Data mining for wearable sensors in health monitoring systems: a review of recent trends and challenges. J. Sens. 13(12), 17472–17500 (2013)

    Article  Google Scholar 

  9. Yuan, B., Herbert, J.: Fuzzy cara-a fuzzy-based context reasoning system for pervasive healthcare. Procedia Comput. Sci. 10, 357–365 (2012)

    Article  Google Scholar 

  10. Latifi, F., Hosseini, R., Mazinai, M.: A fuzzy expert system for diagnosis of acute lymphocytic leukemia in children. Int. J. Inf. Secur. Syst. Manage. 4(2), 424–429 (2015)

    Google Scholar 

  11. Sen, A.K., Patel, S., Shukla, D.P.: A data mining technique for prediction of coronary heart disease using neuro-fuzzy integrated approach two level. Int. J. Eng. Comput. Sci. ISSN 2319-7242 (2013)

    Google Scholar 

  12. Ekong, V.E., Inyang, U.G., Onibere, E.A.: Intelligent decision support system for depression diagnosis based on neuro-fuzzy-CBR hybrid. Modern Appl. Sci. 6(7), 79 (2012)

    Article  Google Scholar 

  13. Nnamoko, N.A., Arshad, F.N., England, D., Vora, J.: Fuzzy expert system for Type 2 Diabetes Mellitus (T2DM) management using dual inference mechanism. In: AAAI Spring Symposium: Data Driven Wellness (2013)

    Google Scholar 

  14. Dennis, B., Muthukrishnan, S.: AGFS: Adaptive Genetic Fuzzy System for medical data classification. Appl. Soft Comput. 25, 242–252 (2014)

    Article  Google Scholar 

  15. Jain, A., Keller, J.M.: Textual summarization of events leading to health alerts. In: 2015 37th Annual International Conference of the IEEE on Engineering in Medicine and Biology Society (EMBC), pp. 7634–7637 (2015)

    Google Scholar 

  16. Wilbik, A., Keller, J.M., Alexander, G.L.: Linguistic summarization of sensor data for eldercare. In: 2011 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 2595–2599 (2011)

    Google Scholar 

  17. Matamoros, A., Torrealba, A., Rivas, F., González, S., Sánchez, R., Molina, L., Rivas-Echeverría, C.: Expert system for the preeclampsia prevention program. In: Proceedings of the 4th WSEAS International Conference on Computational Intelligence, Man-machine Systems and Cybernetics, (MSC 2005), Miami, USA, pp. 146–149 (2005)

    Google Scholar 

  18. Acampora, G., Cook, D.J., Rashidi, P., Vasilakos, A.V.: A survey on ambient intelligence in healthcare. Proc. IEEE 101(12), 2470–2494 (2013)

    Article  Google Scholar 

  19. Zadeh, L.A.: The concept of a linguistic variable and its applications to approximate reasoning, pp. 43–80 (1975)

    Google Scholar 

  20. Ortiz, F., Liu, W.Y.: Modelado y control PD-difuso en tiempo real para el sistema barra-esfera, México DF (2004)

    Google Scholar 

  21. Rodríguez, F.O., Liu, W.Y.: Modelado y control PD-difuso em tiempo real para el sistema barraesfera. Master’s thesis, Universidad de Mexico (2004)

    Google Scholar 

  22. Díaz, B., Morillas, A., et al.: Minería de datos y lógica difusa. Una aplicación al estudio de la rentabilidad económica de las empresas agroalimentarias en Andalucía. Estadística Española 46(157), 409–430 (2004)

    Google Scholar 

  23. PARA, EPIDEMIOLOGÍA: Los protocolos clínicos. Med Clin (Barc), vol. 95, pp. 309–316 (1990)

    Google Scholar 

  24. Llano, L., Zapata, G., Ovalle, D.: Sistema de Inferencia Difuso para Identificar Eventos de Falla en Tiempo Real del STE usando Registros SOE [Fuzzy Inference System for Identifying Fault Events in STE Real Time Using SOE Registries]. Revista Avances en Sistemas e Informática [Advances in Systems and Informatics Journal] Escuela de Sistemas [School of Systems] de la Universidad Nacional de Colombia Sede Medellín 4(2) (2007). Special Edition. ISSN: 1657-7663

    Google Scholar 

  25. Soli, S.B.: Presentación y evolución de las pacientes ingresadas en la Unidad de Cuidados Intensivos con diagnóstico de Preeclampsia-Eclampsia (2013)

    Google Scholar 

  26. Gutiérrez, A., Herráiz, M.A., Bellón, M., Martell, N., Escudero, M.: Protocolo de actuación ante la preeclampsia. Nuestra experiencia (2002)

    Google Scholar 

  27. Pérez, J.H., Unanua, A.P.: Hipertensión arterial (2002)

    Google Scholar 

  28. Padilla-Raygoza, N., Ruiz-Paloalto, M.L., Díaz-Guerrero, R., Olvera-Villanueva, G., Maldonado, A., del Pilar Raygoza-Mendoza, M.: Correlación de mediciones de temperatura corporal con 3 termómetros: ótico, cutáneo y digital, en niños mexicanos. Enfermería Clínica 4(3), 175–182 (2014)

    Google Scholar 

  29. Ng, K.-G., Wong, S.-T., Lim, S.-M., Goh, Z.: Evaluation of the cadi thermosensor wireless skin-contact thermometer against ear and axillary temperatures in children. J. Pediatr. Nurs. 25(3), 176–186 (2010)

    Article  Google Scholar 

  30. Ávila, D., Cervantes, H., Hipólito, J.I., Nieto, L., de Dios, J., Sánchez, R., Martınez, M.E., Calvo, A.H.: Arquitectura de e-Salud basada en Redes Inalámbricas de Sensores. DIFU100ci@, vol. 6, no. 2, pp. 54–61 (2012)

    Google Scholar 

  31. Beurer, G.: Body temperature from Beurer. Soeflinger Strasse, Alemania. Recuperado de: https://www.beurer.com/web/es/products/body_temperature/body_temperature.php (2016)

  32. Sanchez, R.A., Ayala, M., Baglivo, H.: otros: Guias Latinoamericanas de Hipertension Arterial. Revista chilena de cardiologia 29 (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science Department, University of Jaén, Jaén, Spain

    Macarena Espinilla

  2. Universidad Nacional Abierta y a Distancia - UNAD, ECBTI, Pasto, Colombia

    Sixto Campaña

  3. Universidad Pontificia Bolivariana - UPB, Medellín, Colombia

    Jorge Londoño

  4. Computer Science Department, University of Jaén, Jaén, Spain

    Ángel-Luis García-Fernández

Authors
  1. Macarena Espinilla
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sixto Campaña
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jorge Londoño
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ángel-Luis García-Fernández
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Macarena Espinilla .

Editor information

Editors and Affiliations

  1. University of Las Palmas de Gran Canaria, Las Palmas, Spain

    Carmelo R. García

  2. Departamento de Estadistica, Universidad La Laguna, La Laguna, Spain

    Pino Caballero-Gil

  3. Florida State University, Tallahassee, Florida, USA

    Mike Burmester

  4. University of Las Palmas de Gran Canaria, Las Palmas, Spain

    Alexis Quesada-Arencibia

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Espinilla, M., Campaña, S., Londoño, J., García-Fernández, ÁL. (2016). Fuzzy Intelligent System for Supporting Preeclampsia Diagnosis from the Patient Biosignals. In: García, C., Caballero-Gil, P., Burmester, M., Quesada-Arencibia, A. (eds) Ubiquitous Computing and Ambient Intelligence. UCAmI 2016. Lecture Notes in Computer Science(), vol 10069. Springer, Cham. https://doi.org/10.1007/978-3-319-48746-5_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-48746-5_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48745-8

  • Online ISBN: 978-3-319-48746-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation