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Related Concept Videos

General Anesthesia: Overview01:24

General Anesthesia: Overview

Anesthesia is a medical procedure that uses drugs for CNS suppression to enable painless surgeries and procedures. The selection of anesthetics is influenced by their pharmacokinetic properties, side effects, and patient characteristics. Various types of anesthesia include general, local, regional, spinal, and inhalational.
General anesthesia induces unconsciousness in the whole body, while the others target specific areas or sensations. It is administered to minimize adverse effects, maintain...
Stages of General Anesthesia01:22

Stages of General Anesthesia

Various sedation levels offer significant advantages in facilitating procedural interventions for patients undergoing medical or invasive surgical procedures. These levels span from anxiolysis to general anesthesia, providing a spectrum of sedative effects to cater to specific patient needs. Anxiolysis reduces anxiety and is achieved through minimal sedation, enabling patients to remain awake and responsive while feeling more at ease during the procedure. This level can benefit minor...
Local Anesthetics: Clinical Application as Spinal Anesthesia01:11

Local Anesthetics: Clinical Application as Spinal Anesthesia

Spinal anesthetics are given during lower abdomen and limb surgeries to block sensory and motor neurons. They are administered in the mid to low lumbar regions, primarily acting on the cauda equina's nerve roots. The blockade level depends on the local anesthetic (LA) concentration. Usually, low LA concentrations are sufficient to block sensory fibers, while only high LA concentrations block motor fibers. Other factors like injection volume and speed, the patient's posture, and the drug...
Local Anesthetics: Differential Sensitivity of Nerve Fibers01:24

Local Anesthetics: Differential Sensitivity of Nerve Fibers

Local anesthetics (LAs) block the sodium channels of nerve trunks, sensory nerve endings, and neuromuscular junctions. Although LAs can block all kinds of nerves, the sensitivity of nerve fibers differs according to nerve types and structures. LAs are known to block myelinated fibers faster than unmyelinated ones. Also, they block pain or sensory neurons at low concentrations without affecting the motor neurons involved in muscle contractions. This helps relieve labor pain without affecting the...

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Related Experiment Video

Updated: May 10, 2026

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
08:43

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

Neural network-based classification of anesthesia/awareness using Granger causality features.

Nicoletta Nicolaou1, Julius Georgiou

  • 1KIOS Research Centre, Department of Electrical and Computer Engineering, University of Cyprus, Nicosia, Cyprus.

Clinical EEG and Neuroscience
|July 4, 2013
PubMed
Summary
This summary is machine-generated.

This study used Granger causality (GC) and neural networks to analyze brain activity (EEG) for monitoring surgical anesthesia. The system achieved 96% accuracy in distinguishing between anesthesia and awareness.

Keywords:
anesthesiaawarenesselectroencephalogramgranger causalityneural network classifier

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Recording Brain Electromagnetic Activity During the Administration of the Gaseous Anesthetic Agents Xenon and Nitrous Oxide in Healthy Volunteers
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08:16

Optogenetic Activation of Afferent Pathways in Brain Slices and Modulation of Responses by Volatile Anesthetics

Published on: July 23, 2020

Area of Science:

  • Neuroscience
  • Signal Processing
  • Anesthesiology

Background:

  • Monitoring patient awareness during surgery is crucial for anesthetic safety.
  • Current methods for assessing awareness have limitations.
  • Electrical brain activity (EEG) offers a potential biomarker for consciousness states.

Purpose of the Study:

  • To develop and evaluate a signal processing system for real-time monitoring of surgical awareness.
  • To investigate the efficacy of Granger causality (GC) features derived from EEG for discriminating between anesthesia and awareness.
  • To assess the performance of a neural network classifier using GC features.

Main Methods:

  • Utilized electroencephalography (EEG) data from 31 subjects undergoing anesthesia (propofol, sevoflurane, desflurane).
  • Extracted Granger causality (GC) features to quantify anesthetic-induced changes in inter-regional brain signal relationships.
  • Employed a single-hidden-layer neural network for classification of EEG data into 'anesthesia' and 'awareness' states.

Main Results:

  • Granger causality features effectively captured changes in brain connectivity related to anesthetic states.
  • The neural network classifier achieved an average accuracy of 96% in distinguishing between anesthesia and awareness.
  • The proposed system demonstrated high performance in classifying EEG data based on consciousness levels.

Conclusions:

  • Granger causality analysis of EEG is a promising method for monitoring surgical awareness.
  • Neural network classification based on GC features provides a reliable approach to differentiate anesthetic states.
  • This signal processing system holds potential for enhancing patient safety during surgical procedures.