Hybrid Intelligent System to Perform Fault Detection on BIS Sensor During Surgeries
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-01-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/17/1/179 |
| _version_ | 1811279062050537472 |
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| author | José-Luis Casteleiro-Roca José Luis Calvo-Rolle Juan Albino Méndez Pérez Nieves Roqueñí Gutiérrez Francisco Javier de Cos Juez |
| author_facet | José-Luis Casteleiro-Roca José Luis Calvo-Rolle Juan Albino Méndez Pérez Nieves Roqueñí Gutiérrez Francisco Javier de Cos Juez |
| author_sort | José-Luis Casteleiro-Roca |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T00:48:03Z |
| format | Article |
| id | doaj.art-aa915766979e49b1b4d10aa2a5782272 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T00:48:03Z |
| publishDate | 2017-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-aa915766979e49b1b4d10aa2a57822722022-12-22T03:09:57ZengMDPI AGSensors1424-82202017-01-0117117910.3390/s17010179s17010179Hybrid Intelligent System to Perform Fault Detection on BIS Sensor During SurgeriesJosé-Luis Casteleiro-Roca0José Luis Calvo-Rolle1Juan Albino Méndez Pérez2Nieves Roqueñí Gutiérrez3Francisco Javier de Cos Juez4Department of Industrial Engineering, Universidade da Coruña, 15405 Coruña, SpainDepartment of Industrial Engineering, Universidade da Coruña, 15405 Coruña, SpainDepartamento de Ingeniería Informática y de Sistemas, Universidad de La Laguna, Apdo. 456; 38200 La Laguna, SpainProject Engineering Area, Department of Exploitation and Exploration of Mines, University of Oviedo, 33004 Oviedo, SpainProspecting and Exploitation of Mines Department, University of Oviedo, 33004 Oviedo, SpainThis 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.http://www.mdpi.com/1424-8220/17/1/179EMGBISclusteringMLPSVManesthesiadosification |
| spellingShingle | José-Luis Casteleiro-Roca José Luis Calvo-Rolle Juan Albino Méndez Pérez Nieves Roqueñí Gutiérrez Francisco Javier de Cos Juez Hybrid Intelligent System to Perform Fault Detection on BIS Sensor During Surgeries Sensors EMG BIS clustering MLP SVM anesthesia dosification |
| title | Hybrid Intelligent System to Perform Fault Detection on BIS Sensor During Surgeries |
| title_full | Hybrid Intelligent System to Perform Fault Detection on BIS Sensor During Surgeries |
| title_fullStr | Hybrid Intelligent System to Perform Fault Detection on BIS Sensor During Surgeries |
| title_full_unstemmed | Hybrid Intelligent System to Perform Fault Detection on BIS Sensor During Surgeries |
| title_short | Hybrid Intelligent System to Perform Fault Detection on BIS Sensor During Surgeries |
| title_sort | hybrid intelligent system to perform fault detection on bis sensor during surgeries |
| topic | EMG BIS clustering MLP SVM anesthesia dosification |
| url | http://www.mdpi.com/1424-8220/17/1/179 |
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