Hybrid Intelligent Model to Predict the Remifentanil Infusion Rate in Patients Under General Anesthesia

Author:

Jove Esteban1,Gonzalez-Cava Jose M2,Casteleiro-Roca José-Luis1,Quintián Héctor1,Méndez Pérez Juan Albino2,Vega Vega Rafael1,Zayas-Gato Francisco1,de Cos Juez Francisco Javier3,León Ana4,MartÍn María4,Reboso José A4,Woźniak Michał5,Luis Calvo-Rolle José1

Affiliation:

1. Department of Industrial Engineering, University of A Coruña, CTC, CITIC Avda., 15405, Ferrol, A Coruña, Spain

2. University of La Laguna, Department of Computer Science and System Engineering, Avda. Astrof. Francisco Sánchez s/n, S/C de Tenerife, Spain

3. Department of Mining Exploitation, University of Oviedo, Calle San Francisco, 1, Oviedo, Spain

4. Hospital Universitario de Canarias, S/C de Tenerife, Spain

5. Department of Systems and Computer Networks, Wrocław University of Science and Technology, Wrocław, Poland

Abstract

Abstract Automatic control of physiological variables is one of the most active areas in biomedical engineering. This paper is centered in the prediction of the analgesic variables evolution in patients undergoing surgery. The proposal is based on the use of hybrid intelligent modelling methods. The study considers the Analgesia Nociception Index (ANI) to assess the pain in the patient and remifentanil as intravenous analgesic. The model proposed is able to make a one-step-ahead prediction of the remifentanil dose corresponding to the current state of the patient. The input information is the previous remifentanil dose, the ANI variable and the electromyogram signal. Modelling techniques used are Artificial Neural Networks and Support Vector machines for Regression combined with clustering methods. Both training and validation were done with a real dataset from different patients. Results obtained show the potential of this methodology to calculate the drug dose corresponding to a given analgesic state of the patient.

Funder

Fundación Canaria de Investigación Sanitaria

Ministry of Education, Culture and Sports

Publisher

Oxford University Press (OUP)

Subject

Logic

Cited by 16 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3