Advertisement

Electromyogram prediction during anesthesia by using a hybrid intelligent model

  • José-Luis Casteleiro-Roca
  • Marco Gomes
  • Juan Albino Méndez-Pérez
  • Héctor Alaiz-MoretónEmail author
  • María del Carmen Meizoso-López
  • Benigno Antonio Rodríguez-Gómez
  • José Luis Calvo-Rolle
Original Research
  • 26 Downloads

Abstract

In the search for new and more efficient ways to administer drugs, clinicians are turning to engineering tools. The availability of these models to predict physiological variables are a significant factor. A model is set out in this research to predict the EMG (electromyogram) signal during surgery, in patients under general anaesthesia. This prediction hinges on the Bispectral Index™ (BIS) and the infusion rate of the drug propofol. The results of the research are very satisfactory, with error values of less than 0.67 (for a Normalized Mean Squared Error). A hybrid intelligent model is used which combines both clustering and regression algorithms. The resulting model is validated and trained using real data.

Keywords

EMG BIS™ Clustering MLP SVM 

Notes

Acknowledgements

This study was conducted under the auspices of Research Project DPI2010-18278, supported by the Spanish Ministry of Innovation and Science.

References

  1. Aláiz-Moretón H, Castejón-Limas M, Casteleiro-Roca J-L, Jove E, Fernández Robles L, Calvo-Rolle JL (2019) A fault detection system for a geothermal heat exchanger sensor based on intelligent techniques. Sensors 19(12):2740.  https://doi.org/10.3390/s19122740 Google Scholar
  2. Bishop C (2006) Pattern recognition and machine learning (information science and statistics). Springer, New YorkzbMATHGoogle Scholar
  3. Bursa M, Lhotska L, Chudacek V, Spilka J, Janku P, Hruban L (2015) Information retrieval from hospital information system: increasing effectivity using swarm intelligence. J Appl Log 13(2, Part A):126–137.  https://doi.org/10.1016/j.jal.2014.11.006 Google Scholar
  4. Calvo-Rolle J, Machón-González I, López-García H (2011) Neuro-robust controller for non-linear systems. Dyna 86(3):308–317.  https://doi.org/10.6036/3949 Google Scholar
  5. Calvo-Rolle J, Casteleiro-Roca J, Quintián H, Meizoso-Lopez M (2013) A hybrid intelligent system for PID controller using in a steel rolling process. Expert Syst Appl 40(13):5188–5196.  https://doi.org/10.1016/j.eswa.2013.03.013 Google Scholar
  6. Calvo-Rolle J, Fontenla-Romero Ó, Pérez-Sánchez B, Guijarro-Berdinas B (2014) Adaptive inverse control using an online learning algorithm for neural networks. Informatica 25(3):401–414.  https://doi.org/10.15388/Informatica.2014.20 Google Scholar
  7. Calvo-Rolle J, Quintian-Pardo H, Corchado E, Meizoso-López M, Ferreiro García R (2015) Simplified method based on an intelligent model to obtain the extinction angle of the current for a single-phase half wave controlled rectifier with resistive and inductive load. J Appl Log 13(1):37–47.  https://doi.org/10.1016/j.jal.2014.11.010 Google Scholar
  8. Casteleiro-Roca J, Calvo-Rolle J, Meizoso-López M, Piñón-Pazos A, Rodríguez-Gómez B (2014) New approach for the QCM sensors characterization. Sens Actuators A Phys 207:1–9.  https://doi.org/10.1016/j.sna.2013.12.002 Google Scholar
  9. Casteleiro-Roca JL, Calvo-Rolle J, Meizoso-López AMC, Piñón-Pazos Rodríguez-Gómez B (2015a) Bio-inspired model of ground temperature behavior on the horizontal geothermal exchanger of an installation based on a heat pump. Neurocomputing 150:90–98.  https://doi.org/10.1016/j.neucom.2014.02.075 Google Scholar
  10. Casteleiro-Roca JL, Pérez JAM, Piñón-Pazos AJ, Calvo-Rolle JL, Corchado E (2015b) Modeling the electromyogram (EMG) of patients undergoing anesthesia during surgery. In: 10th International conference on soft computing models in industrial and environmental applications, Springer International Publishing, Cham, pp 273–283.  https://doi.org/10.1007/978-3-319-19719-7_24
  11. Casteleiro-Roca J-L, Jove E, Gonzalez-Cava JM, Méndez Pérez JA, Calvo-Rolle JL, Blanco Alvarez F (2018) Hybrid model for the ANI index prediction using remifentanil drug and EMG signal. Neural Comput Appl.  https://doi.org/10.1007/s00521-018-3605-z Google Scholar
  12. Casteleiro-Roca J-L, Barragán AJ, Segura F, Calvo-Rolle JL, Andújar JM (2019a) Fuel cell output current prediction with a hybrid intelligent system. Complexity.  https://doi.org/10.1155/2019/6317270 Google Scholar
  13. Casteleiro-Roca J-L, Gómez-González JF, Calvo-Rolle JL, Jove E, Quintián H, Gonzalez Diaz B, Mendez Perez JA (2019b) Short-term energy demand forecast in hotels using hybrid intelligent modeling. Sensors 19(11):2485.  https://doi.org/10.3390/s19112485 Google Scholar
  14. de Cos J, Sanchez F, Ortega F, Montequin V (2008) Rapid cost estimation of metallic components for the aerospace industry. Int J Prod Econ 11(1):470–482.  https://doi.org/10.1016/j.ijpe.2007.05.016 Google Scholar
  15. de Cos FJ, García Nieto P, Martínez Torres J, Taboada Castro J (2010) Analysis of lead times of metallic components in the aerospace industry through a supported vector machine model. Math Comput Model 52(7–8):1177–1184.  https://doi.org/10.1016/j.mcm.2010.03.017 Google Scholar
  16. Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, New YorkzbMATHGoogle Scholar
  17. Cui S, Duan L, Qiao Y, Xiao Y (2018) Learning EEG synchronization patterns for epileptic seizure prediction using bag-of-wave features. J Ambient Intell Humaniz Comput.  https://doi.org/10.1007/s12652-018-1000-3 Google Scholar
  18. Ferreiro García R, Calvo Rolle J, Romero Gomez M, DeMiguel Catoira A (2013) Expert condition monitoring on hydrostatic self-levitating bearings. Expert Syst Appl 40(8):2975–2984.  https://doi.org/10.1016/j.eswa.2012.12.013 Google Scholar
  19. Ferreiro García R, Calvo-Rolle J, Pérez Castelo J, Romero Gomez M (2014) On the monitoring task of solar thermal fluid transfer systems using NN based models and rule based techniques. Eng Appl Artif Intell 27:129–136.  https://doi.org/10.1016/j.engappai.2013.06.011 Google Scholar
  20. García Nieto P, Martínez Torres J, de Cos Juez F, Sánchez Lasheras F (2012) Using multivariate adaptive regression splines and multilayer perceptron networks to evaluate paper manufactured using eucalyptus globulus. Appl Math Comput 219(2):755–763.  https://doi.org/10.1016/j.amc.2012.07.001 Google Scholar
  21. Ghanghermeh A, Roshan G, Orosa J, Calvo-Rolle J, Costa Á (2013) New climatic indicators for improving urban sprawl: a case study of Tehran city. Entropy 15(3):999–1013.  https://doi.org/10.3390/e15030999 Google Scholar
  22. González Gutiérrez C, Sánchez Rodríguez ML, Fernández Díaz RÁ, Calvo Rolle JL, Roqueñí Gutiérrez N, Javier de Cos Juez F (2018) Rapid tomographic reconstruction through GPU-based adaptive optics. Log J IGPL 27(2):214–226MathSciNetGoogle Scholar
  23. Guo Y, Li X, Bai G, Ma J (2012) Time series prediction method based on LS-SVR with modified Gaussian RBF. In: Neural information processing, pp 9–17.  https://doi.org/10.1007/978-3-642-34481-7_2
  24. Heiberger R, Neuwirth E (2009) Polynomial regression. In: R through Excel, use R. Springer, New York, pp 269–284.  https://doi.org/10.1007/978-1-4419-0052-4_11
  25. Hemmerling T, Arbeid E, Wehbe M, Cyr S, Taddei R, Zaouter C, Reilly C (2013) Evaluation of a novel closed-loop total intravenous anaesthesia drug delivery system: a randomized controlled trial. Br J Anaesth 110(6):1031–1039Google Scholar
  26. Jiang Y, Zhang T, Gou Y, He L, Bai H, Hu C (2018) High-resolution temperature and salinity model analysis using support vector regression. J Ambient Intell Humaniz Comput.  https://doi.org/10.1007/s12652-018-0896-y Google Scholar
  27. Jove E, Blanco-Rodríguez P, Casteleiro-Roca JL, Moreno-Arboleda J, López-Vázquez JA, de Cos Juez FJ, Calvo-Rolle JL (2018a) Attempts prediction by missing data imputation in engineering degree. In: International joint conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, proceeding, Springer International Publishing, Cham, pp 167–176Google Scholar
  28. Jove E, Gonzalez-Cava JM, Casteleiro-Roca J-L, Méndez-Pérez J-A, Antonio Reboso-Morales J, Javier Pérez-Castelo F, de Cos Javier, Juez F, Luis Calvo-Rolle J (2018b) Modelling the hypnotic patient response in general anaesthesia using intelligent models. Log J IGPL 27(2):189–201MathSciNetGoogle Scholar
  29. Jove E, Gonzalez-Cava JM, Casteleiro-Roca JL, Pérez JAM, Calvo-Rolle JL, de Cos Juez FJ (2018c) An intelligent model to predict ANI in patients undergoing general anesthesia. In: International joint conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, proceeding, Springer International Publishing, Cham, pp 492–501Google Scholar
  30. Jove E, López JAV, Fernández-Ibáñez I, Casteleiro-Roca JL, Calvo-Rolle JL (2018d) Hybrid intelligent system topredict the individual academic performance of engineering students. Int J Eng Educ 34(3):895–904Google Scholar
  31. Kaski S, Sinkkonen J, Klami A (2005) Discriminative clustering. Neurocomputing 69(1–3):18–41.  https://doi.org/10.1016/j.neucom.2005.02.012 Google Scholar
  32. Lemaître G, Martí R, Freixenet J, Vilanova JC, Walker PM, Meriaudeau F (2015) Computer-aided detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: a review. Comput Biol Med 60:8–31.  https://doi.org/10.1016/j.compbiomed.2015.02.009 Google Scholar
  33. Machon-Gonzalez I, Lopez-Garcia H, Calvo-Rolle J (2010) A hybrid batch SOM-NG algorithm. In: Neural networks (IJCNN), the 2010 international joint conference on, pp 1–5.  https://doi.org/10.1109/IJCNN.2010.5596812
  34. Manuel Vilar-Martinez X, Aurelio Montero-Sousa J, Luis Calvo-Rolle J, Luis Casteleiro-Roca J (2014) Expert system development to assist on the verification of “TACAN” system performance. Dyna 89(1):112–121Google Scholar
  35. Méndez J, Marrero A, Reboso J, León A (2016) Adaptive fuzzy predictive controller for anesthesia delivery. Control Eng Pract 46:1–9Google Scholar
  36. Pérez JAM, Torres S, Reboso JA, Reboso H (2011) Estrategias de control en la práctica de anestesia. Revista Iberoamericana Autom Inf Ind RIAI 8(3):241–249.  https://doi.org/10.1016/j.riai.2011.06.011 Google Scholar
  37. Qin A, Suganthan P (2005) Enhanced neural gas network for prototype-based clustering. Pattern Recognit 38(8):1275–1288.  https://doi.org/10.1016/j.patcog.2004.12.007 Google Scholar
  38. Quintián H, Calvo-Rolle J, Corchado E (2014) A hybrid regression system based on local models for solar energy prediction. Informatica 25(2):265–282Google Scholar
  39. Quintián H, Casteleiro-Roca J-L, Perez-Castelo FJ, Calvo-Rolle JL, Corchado E (2016) Hybrid intelligent model for fault detection of a lithium iron phosphate power cell used in electric vehicles. In: International conference on hybrid artificial intelligence systems, pp 751–762Google Scholar
  40. Quintián-Pardo H, Calvo-Rolle JL, Fontenla-Romero O (2012) Application of a low cost commercial robot in task of tracking of objects. Dyna 175:24–33Google Scholar
  41. Reboso J, Mendez J, Reboso H, León A (2012) Design and implementation of a closed-loop control system for infusion of propofol guided by bispectral index (BIS). Acta Anaesthesiol Scand 56(8):1032–1041Google Scholar
  42. Rynkiewicz J (2012) General bound of overfitting for MLP regression models. Neurocomputing 90:106–110.  https://doi.org/10.1016/j.neucom.2011.11.028 Google Scholar
  43. Sánchez SS, Vivas AM, Obregón JS, Ortega MR, Jambrina CC, Marco ILT, Jorge EC (2009) Monitorización de la sedación profunda. El monitor BIS. Enfermería Intensiva 20(4):159–166.  https://doi.org/10.1016/S1130-2399(09)73224-9 Google Scholar
  44. Sigl J, Chamoun N (1994) An introduction to bispectral analysis for the electroencephalogram. J Clin Monit 10(6):392–404Google Scholar
  45. Steinwart I, Christmann A (2008) Support vector machines. Springer Publishing Company, Incorporated, New YorkzbMATHGoogle Scholar
  46. Suykens J, Vandewalle J (1999) Least squares support vector machine slassifiers. Neural Process Lett 9(3):293–300.  https://doi.org/10.1023/A:1018628609742 Google Scholar
  47. Vapnik V (1995) The nature of statistical learning theory. Springer, New YorkzbMATHGoogle Scholar
  48. Vega Vega R, Quintián H, Calvo-Rolle JL, Herrero Á, Corchado E (2018) Gaining deep knowledge of android malware families through dimensionality reduction techniques. Log J IGPL 27(2):160–176MathSciNetGoogle Scholar
  49. Vrbaški M, Doroslovački R, Kupusinac A, Stokić E, Ivetić D (2019) Lipid profile prediction based on artificial neural networks. J Ambient Intell Humaniz Comput.  https://doi.org/10.1007/s12652-019-01374-3 Google Scholar
  50. Wang L, Wu J (2012) Neural network ensemble model using PPR and LS-SVR for stock market forecasting. Adv Intell Comput.  https://doi.org/10.1007/978-3-642-24728-6_1 Google Scholar
  51. Wang R, Wang A, Song Q (2012) Research on the alkalinity of sintering process based on LS-SVM algorithms. Advances in computer science and information engineering. Springer, New York, pp 449–454.  https://doi.org/10.1007/978-3-642-30126-1_71 Google Scholar
  52. Wasserman P (1993) Advanced methods in neural computing. Wiley, New YorkzbMATHGoogle Scholar
  53. Wu X (2007) Optimal designs for segmented polynomial regression models and web-based implementation of optimal design software. State University of New York, Stony BrookGoogle Scholar
  54. Ye J, Xiong T (2007) SVM versus least squares SVM. J Mach Learn Res 2:644–651 (Proceedings Track) Google Scholar
  55. Zeng Z, Wang J (2010) Advances in neural network research and applications. Springer Publishing Company, Incorporated, New YorkGoogle Scholar
  56. Zhang Z, Chan S (2011) On kernel selection of multivariate local polynomial modelling and its application to image smoothing and reconstruction. J Signal Process Syst 64(3):361–374.  https://doi.org/10.1007/s11265-010-0495-4 Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • José-Luis Casteleiro-Roca
    • 1
  • Marco Gomes
    • 2
  • Juan Albino Méndez-Pérez
    • 3
  • Héctor Alaiz-Moretón
    • 4
    Email author
  • María del Carmen Meizoso-López
    • 1
  • Benigno Antonio Rodríguez-Gómez
    • 1
  • José Luis Calvo-Rolle
    • 1
  1. 1.Dpto. de Ingeniería IndustrialUniversity of A CoruñaA CoruñaSpain
  2. 2.ALGORITMI CentreUniversity of MinhoBragaPortugal
  3. 3.Dpto. de Ingeniería de Sistemas y Automática y Arquitectura y Tecnología de ComputadoresUniversity of La LagunaSan Cristóbal de La Laguna Spain
  4. 4.Dpto. de Ingeniería Eléctrica y de Sistemas y AutomáticaUniversity of LeónLeónSpain

Personalised recommendations