Monitoring of Anesthesia by Bispectral Analysis of EEG Signals

Author:

Bouafif Lamia1ORCID

Affiliation:

1. National Institute of Biomedical Studies of Tunis-Image and Signal Processing Laboratory ENIT, University of Tunis Manar, Tunisia

Abstract

Background. In intensive care, monitoring the depth of anesthesia during surgical procedures is a key element in the success of the medical operation and postoperative recovery. However, despite the development of anesthesia thanks to technological and pharmacological advances, its side effects such as underdose or overdose of hypnotics remain a major problem. Observation and monitoring must combine clinical observations (loss of consciousness and reactivity) with tools for real-time measurement of changes in the depth of anesthesia. Methodology. In this work, we will develop a noninvasive method for calculating, monitoring, and controlling the depth of general anesthesia during surgery. The objective is to reduce the effects of pharmacological usage of hypnotics and to ensure better quality recovery. Thanks to the overall activity of sets of neurons in the brain, we have developed a BIS technique based on bispectral analysis of the electroencephalographic signal EEG. Discussion. By collecting the electrical voltages from the brain, we distinguish light sleep from deep sleep according to the values of the BIS indicator (ranging from 0 : sleep to 100 : wake) and also control it by acting on the dosage of propofol and sevoflurane. We showed that the BIS value must be maintained during the operation and the anesthesia at a value greater than 60. Conclusion. This study showed that the BIS technology led to an optimization of the anesthetic management, the adequacy of the hypnotic dosage, and a better postoperative recovery.

Publisher

Hindawi Limited

Subject

Applied Mathematics,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,Modeling and Simulation,General Medicine

Reference21 articles.

1. Epistemological problems;E. Robert,2013

2. Surveillance de la profondeur de l'anesthésie générale

3. Montaine BernardMethodology for Analyzing Neuronal Synchronizations in EEG Signals Using Time-Frequency Information Graphs, [PHD Thesis]2006FranceDoctoral School: Engineering Sciences and Aeronautics, Research Sector: Signal and Image Processing, University of Poitiers

4. Automatic analysis of temporal lobe epileptic seizures from surface EEGs;M. Caparos;Nancy Center for Automatic Research CNRS-UMR,2010

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