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

José-Luis Casteleiro-Roca,Francisco De Cos Juez,Nieves Roqueñí Gutiérrez,José Calvo-Rolle,Juan Méndez Pérez

doi:10.3390/s17010179

José-Luis Casteleiro-Roca, Francisco De Cos Juez + Show 3 more

Open Access

https://doi.org/10.3390/s17010179

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Abstract

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.

Highlights

Anesthesia control has aroused the attention of many scientists in recent years in order to move towards personalized drug infusion [1,2], where the drug dose infused to the patient is calculated according to precise individualized measures in the operating room
This paper focuses on hypnosis monitoring using the Bispectral Index (BIS) signal
This study provides a methodology for the detection of failure episodes in BIS sensors affected by disturbances

Summary

Introduction

Anesthesia control has aroused the attention of many scientists in recent years in order to move towards personalized drug infusion [1,2], where the drug dose infused to the patient is calculated according to precise individualized measures in the operating room. The three main variables that the clinician has to pay attention to during surgery are hypnosis, analgesia, and neuromuscular blockade. Hypnosis measures the patient’s unconsciousness level, analgesia is related to pain mitigation, and neuromuscular blockade refers to immobility. Control of analgesia is still a challenge, significant advances have been made in the automatic control of hypnosis and neuromuscular blockade [3,4]. Different techniques using both signal-based and mode-based methodologies have been used successfully [5,6,7].

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