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An Intelligent Model to Predict ANI in Patients Undergoing General Anesthesia

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International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding (SOCO 2017, ICEUTE 2017, CISIS 2017)

Abstract

One of the main challenges in anesthesia is the proposal of safe and efficient methods to administer drugs to regulate the pain that the patient is sufffering during the surgical process. First steps towards this objective is the proposal of adequate indexes that correlate well with analgesia. One of the most promising index is ANI (Antinociception Index). This research focuses on the modelling of the ANI response in patients undergoing general anesthesia with intravenous drug infusion. The aim is to predict the ANI response in terms of the analgesic infusion rate. For this a model based on intelligent regression techniques is proposed. To create the model, it has been checked Artificial Neural Networks (ANN) and Support Vector Regression (SVR). Results were validated using data from patients in the operating room. The measured performance attest for the potential of the proposed technique.

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Acknowledgments

Authors greatly appreciate the support both from Spanish Ministry of Economy and Competitiveness through grant AYA2014-57648-P, and from regional Ministry of Economy and Employment through grant FC-15-GRUPIN14-017. 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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Correspondence to Esteban Jove .

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Jove, E., Gonzalez-Cava, J.M., Casteleiro-Roca, J.L., Pérez, J.A.M., Calvo-Rolle, J.L., de Cos Juez, F.J. (2018). An Intelligent Model to Predict ANI in Patients Undergoing General Anesthesia. In: Pérez García, H., Alfonso-Cendón, J., Sánchez González, L., Quintián, H., Corchado, E. (eds) International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding. SOCO ICEUTE CISIS 2017 2017 2017. Advances in Intelligent Systems and Computing, vol 649. Springer, Cham. https://doi.org/10.1007/978-3-319-67180-2_48

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  • DOI: https://doi.org/10.1007/978-3-319-67180-2_48

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