Abstract
With the aim to control and reduce the pain of patients during a surgery with general anesthesia, one of the main challenges is the proposal of safe an optimal and efficient methods of drugs administering. First step to achieve this goal is the proposal and development of right indexes that correlate satisfactory with analgesia. One of this index gives the most hopeful results is the Analgesia Nociception Index (ANI). The present research work deals the ANI response of patients during surgeries with general anesthesia with intravenous drug infusion. The main aim is to predict the ANI signal behavior regarding of the analgesic infusion rate. To do that, a hybrid intelligent model is developed, using clustering and regression techniques based on artificial neural networks and support vector regression. The proposal was validated with a dataset of surgeries real cases of patients undergoing general anesthesia. The achieved results attest for the potential of the proposed technique.
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Marrero A, Méndez JA, Reboso JA, Martín I, Calvo JL (2017) Adaptive fuzzy modeling of the hypnotic process in anesthesia. J Clin Monit Comput 31(2):319–330. https://doi.org/10.1007/s10877-016-9868-y
Mendez JA, Marrero A, Reboso JA, Leon A (2016) Adaptive fuzzy predictive controller for anesthesia delivery. Control Eng Pract 46:1–9. https://doi.org/10.1016/j.conengprac.2015.09.009
Guignard B (2006) Monitoring analgesia. Best Pract Res Clin Anaesthesiol 20(1):161–180. https://doi.org/10.1016/j.bpa.2005.09.002
Casteleiro-Roca J, Calvo-Rolle J, Meizoso-Lopez M, non Pazos AP, Rodriguez-Gómez B (2014) New approach for the QCM sensors characterization. Sens Actuators A 207:1–9. https://doi.org/10.1016/j.sna.2013.12.002
Crespo-Ramos MJ, Machón-González I, López-García H, Calvo-Rolle JL (2013) Detection of locally relevant variables using SOM-NG algorithm. Eng Appl Artif Intell 26(8):1992–2000
Cowen R, Stasiowska MK, Laycock H, Bantel C (2015) Assessing pain objectively: the use of physiological markers. Anaesthesia 70(7):828–847. https://doi.org/10.1111/anae.13018
Gritsan A, Dovbish N, Kurnosov D, Gritsan E (2016) Control of the adequacy of analgesia during general anesthesia with the use of the monitor “Analgesia Nociception Index”. Anesth Analg 123(3 Supplement):769. https://doi.org/10.1213/01.ane.0000492984.63279.34
Boselli E, Daniela-Ionescu M, Begou G, Bouvet L, Dabouz R, Magnin C, Allaouchiche B (2013) Prospective observational study of the non-invasive assessment of immediate postoperative pain using the analgesia/nociception index (ANI). Br J Anaesth 111(3):453–459. https://doi.org/10.1093/bja/aet110
Gruenewald M, Schoenherr T, Herz J, Ilies C, Fudickar A, Bein B (2013) Analgesia nociception index (ANI) for detection of noxious stimulation during sevoflurane Remifentanil anaesthesia: 14AP78. Eur J Anaesthesiol 30:223
Absalom AR, Mani V, De Smet T, Struys MMRF (2009) Pharmacokinetic models for propofol-defining and illuminating the devil in the detail. Br J Anaesth 103(1):26–37. https://doi.org/10.1093/bja/aep143
Schnider TW, Minto CF, Gambus PL, Andresen C, Goodale DB, Shafer SL, Youngs EJ (1998) The influence of method of administration and covariates on the pharmacokinetics of propofol in adult volunteers. Anesthesiology 88(5):1170–1182. https://doi.org/10.1097/00000542-199805000-00006
Schnider TW, Minto CF, Shafer SL, Gambus PL, Andresen C, Goodale DB, Youngs EJ (1999) The influence of age on propofol pharmacodynamics. Anesthesiology 90(6):1502–1516. https://doi.org/10.1097/00000542-199906000-00003
Casteleiro-Roca JL, Pérez JAM, Piñón-Pazos AJ, Calvo-Rolle JL, Corchado E (2015) Modeling the electromyogram (EMG) of patients undergoing anesthesia during surgery. In: 10th international conference on soft computing models in industrial and environmental applications, pp 273–283
Calvo-Rolle JL, Fontenla-Romero O, 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
Calvo-Rolle JL, Quintian-Pardo H, Corchado E, del Carmen Meizoso-López M, García RF (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
Ghanghermeh A, Roshan G, Orosa JA, Calvo-Rolle JL, Costa AM (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
Casteleiro-Roca JL, Calvo-Rolle JL, Meizoso-López MC, Piñón-Pazos A, Rodríguez-Gómez BA (2015) Bio-inspired model of ground temperature behavior on the horizontal geothermal exchanger of an installation based on a heat pump. Neurocomputing 150:90–98
Machón-González I, López-García H, Calvo-Rolle JL (2010) A hybrid batch SOM-NG algorithm. In: The 2010 international joint conference on neural networks (IJCNN), pp 1–5
Alaiz Moretón H, Calvo Rolle J, García I, Alonso Alvarez A (2011) Formalization and practical implementation of a conceptual model for PID controller tuning. Asian J Control 13(6):773–784
Rolle J, Gonzalez I, Garcia H (2011) Neuro-robust controller for non-linear systems. Dyna 86(3):308–317. https://doi.org/10.6036/3949
Calvo-Rolle JL, Casteleiro-Roca JL, Quintián H, del Carmen 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
García RF, Rolle JLC, Castelo JP, Gomez MR (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
García RF, Rolle JLC, Gomez MR, Catoira AD (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
Quintián H, Calvo-Rolle JL, Corchado E (2014) A hybrid regression system based on local models for solar energy prediction. Informatica 25(2):265–282
Quintian Pardo H, Calvo Rolle JL, Fontenla Romero O (2012) Application of a low cost commercial robot in tasks of tracking of objects. Dyna 79(175):24–33
Kaski S, Sinkkonen J, Klami A (2005) Discriminative clustering. Neurocomputing 69(13):18–41. https://doi.org/10.1016/j.neucom.2005.02.012
Qin A, Suganthan P (2005) Enhanced neural gas network for prototype-based clustering. Pattern Recogn 38(8):1275–1288. https://doi.org/10.1016/j.patcog.2004.12.007
Wasserman P (1993) Advanced methods in neural computing, 1st edn. Wiley, New York
Zeng Z, Wang J (2010) Advances in neural network research and applications, 1st edn. Springer, Berlin
Vapnik V (1995) The nature of statistical learning theory. Springer, Berlin
Casteleiro-Roca JL, Calvo-Rolle JL, Méndez Pérez JA, Roqueñí Gutiérrez N, de Cos Juez FJ (2017) Hybrid intelligent system to perform fault detection on bis sensor during surgeries. Sensors 17(1):179
Fernández-Serantes LA, Vázquez RE, Casteleiro-Roca JL, Calvo-Rolle JL, Corchado E (2014) Hybrid intelligent model to predict the SOC of a LFP power cell type. In: International conference on hybrid artificial intelligence systems, pp 561–572
Casteleiro-Roca JL, Quintián H, Calvo-Rolle JL, Corchado E, del Carmen Meizoso-López M, Piñón-Pazos A (2016) An intelligent fault detection system for a heat pump installation based on a geothermal heat exchanger. J Appl Log 17:36–47
Li Y, Shao X, Cai W (2007) A consensus least squares support vector regression (LS-SVR) for analysis of near-infrared spectra of plant samples. Talanta 72(1):217–222. https://doi.org/10.1016/j.talanta.2006.10.022
Charles F, Minto MB, Ch B, Thomas W, Schnider MDS (1997) Pharmacokinetics and pharmacodynamics of remifentanil. Model application. Anesthesiology 86:24–33
Minto CF, Schnider TW, Gregg KM, Henthorn TK, Shafer SL (2003) Using the time of maximum effect site concentration to combine pharmacokinetics and pharmacodynamics. Anesthesiology 99(2):324–333. https://doi.org/10.1097/00000542-200308000-00014
Brogi E, Cyr S, Kazan R, Giunta F, Hemmerling TM (2017) Clinical performance and safety of closed-loop systems: a systematic review and meta-analysis of randomized controlled trials. Anesth Analg 124(2):446–455. https://doi.org/10.1213/ANE.0000000000001372
Albino Mendez J, Torres S, Antonio Reboso J, Reboso H (2009) Adaptive computer control of anesthesia in humans. Comput Methods Biomech Biomed Eng 12(6):727–734. https://doi.org/10.1080/10255840902911528
Acknowledgements
Jose M. Gonzalez-Cava’s research was supported by the Spanish Ministry of Education, Culture and Sport (www.mecd.gob.es), under the “Formación de Profesorado” Grant FPU15/03347.
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Casteleiro-Roca, JL., Jove, E., Gonzalez-Cava, J.M. et al. Hybrid model for the ANI index prediction using Remifentanil drug and EMG signal. Neural Comput & Applic 32, 1249–1258 (2020). https://doi.org/10.1007/s00521-018-3605-z
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DOI: https://doi.org/10.1007/s00521-018-3605-z