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Open AccessArticle

Hybrid Intelligent System to Perform Fault Detection on BIS Sensor During Surgeries

Department of Industrial Engineering, Universidade da Coruña, 15405 Coruña, Spain
Departamento de Ingeniería Informática y de Sistemas, Universidad de La Laguna, Apdo. 456; 38200 La Laguna, Spain
Project Engineering Area, Department of Exploitation and Exploration of Mines, University of Oviedo, 33004 Oviedo, Spain
Prospecting and Exploitation of Mines Department, University of Oviedo, 33004 Oviedo, Spain
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Current address: Department of Industrial Engineering, University of Coruña, 15405 Coruña, Spain.
Academic Editor: Gonzalo Pajares Martinsanz
Sensors 2017, 17(1), 179;
Received: 31 October 2016 / Revised: 9 January 2017 / Accepted: 12 January 2017 / Published: 18 January 2017
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Spain 2016)
This paper presents a new fault detection system in hypnotic sensors used for general anesthesia during surgery. Drug infusion during surgery is based on information received from patient monitoring devices; accordingly, faults in sensor devices can put patient safety at risk. Our research offers a solution to cope with these undesirable scenarios. We focus on the anesthesia process using intravenous propofol as the hypnotic drug and employing a Bispectral Index (BISTM) monitor to estimate the patient’s unconsciousness level. The method developed identifies BIS episodes affected by disturbances during surgery with null clinical value. Thus, the clinician—or the automatic controller—will not take those measures into account to calculate the drug dose. Our method compares the measured BIS signal with expected behavior predicted by the propofol dose provider and the electromyogram (EMG) signal. For the prediction of the BIS signal, a model based on a hybrid intelligent system architecture has been created. The model uses clustering combined with regression techniques. To validate its accuracy, a dataset taken during surgeries with general anesthesia was used. The proposed fault detection method for BIS sensor measures has also been verified using data from real cases. The obtained results prove the method’s effectiveness. View Full-Text
Keywords: EMG; BIS; clustering; MLP; SVM; anesthesia; dosification EMG; BIS; clustering; MLP; SVM; anesthesia; dosification
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Casteleiro-Roca, J.-L.; Calvo-Rolle, J.L.; Méndez Pérez, J.A.; Roqueñí Gutiérrez, N.; De Cos Juez, F.J. Hybrid Intelligent System to Perform Fault Detection on BIS Sensor During Surgeries. Sensors 2017, 17, 179.

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