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Anomaly Detection on Patients Undergoing General Anesthesia

  • Esteban JoveEmail author
  • Jose M. Gonzalez-Cava
  • José-Luis Casteleiro-Roca
  • Héctor Quintián
  • Juan Albino Méndez-Pérez
  • José Luis Calvo-Rolle
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 951)

Abstract

The importance of the infusion drug optimization in patients undergoing general anesthesia has led to the implementation of automatic control loops and models to predict the state of the patient. The appearance of any anomaly during the anesthetic process may lead, for instance, to incorrect drug administration. This could produce undesirable side effects that can affect the patient postoperative and also reduce the safety of the patient in the operating room. This study evaluates different one-class intelligent techniques to detect anomalies in patients undergoing general anesthesia. Due to the difficulty of obtaining data from anomaly situations, artificial outliers are generated to check the performance of each classifier. The final results give good performance in general terms.

Keywords

Anomaly detection Outlier generation Anesthesia 

Notes

Acknowledgments

This research is partially supported through the “Fundación Canaria de Investigación Sanitaria” (FUNCANIS) [ref: PIFUN23/18].

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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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Esteban Jove
    • 1
    • 2
    Email author
  • Jose M. Gonzalez-Cava
    • 2
  • José-Luis Casteleiro-Roca
    • 1
  • Héctor Quintián
    • 1
  • Juan Albino Méndez-Pérez
    • 2
  • José Luis Calvo-Rolle
    • 1
  1. 1.Department of Industrial EngineeringUniversity of A CoruñaFerrol, A CoruñaSpain
  2. 2.Department of Computer Science and System EngineeringUniversidad de La LagunaS/C de TenerifeSpain

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