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Title: EEG connectivity measures and their application to assess the depth of anaesthesia and sleep
Author: Lioi, Giulia
ISNI:       0000 0004 7225 6048
Awarding Body: University of Southampton
Current Institution: University of Southampton
Date of Award: 2018
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General anaesthesia has been used for more than two centuries to guarantee unconsciousness, analgesia and immobility during surgery, yet our ability to evaluate the level of anaesthesia of the patient remains insufficient. This contributes on one hand to occasional episodes of intraoperative awareness and recall and on the other to ‘controlled’ drug over-dosage that increases hospital costs and patients recovery times. At present parameters used in clinical practice to monitor anaesthesia are indirect measures of the state of the brain, which is the target organ of anaesthetics. The lack of a reliable monitor of anaesthetic depth has led to considerable effort to develop new monitoring methods based on electrophysiological measurements. This progress has produced a series of depth of anaesthesia monitors based on various features of the electroencephalogram (EEG) signal. Even though these indexes are practically useful, their theoretical and physiological validity is poorly evidenced and they suffer from some practical limitations. As a result, their clinical uptake has been quite low. In recent years increasing attention has been given to brain connectivity as a powerful tool to investigate the complex behaviour of the brain. Theoretical and experimental findings have identified the disruption of brain connectivity as a crucial mechanism of anaesthetic-induced loss of consciousness. In this work a novel index of anaesthetic depth based on brain connectivity estimated from non-invasive scalp recordings (EEG) is proposed. Firstly, robust estimators of directed connectivity were identified in the framework of multivariate autoregressive (MVAR) models. With a series of simulation studies the performances of these methods in estimating causal connections were assessed in particular with respect to the deleterious effects of instantaneous connectivity due to volume conduction. Recently published solutions were also tested (and rejected). From a comparison of connectivity measurements in simulations, MVAR based estimators were most robust to the effects of volume conduction than conventional coherence measurements. Next the performances of directed connectivity estimators were tested in two experimental studies on NREM sleep and on anaesthesia. Features that exhibited the most robust changes with the individual level of consciousness were identified and their performances in discriminating wakefulness from anaesthesia tested on ten patients undergoing a slow induction of propofol anaesthesia. The performance of the proposed method were also compared with established depth of anaesthesia indexes such as Bispectral Index (BIS) or Auditory Evoked Potentials (AEP). Results suggest that EEG connectivity features are sensitive to the anaesthetic induced changes and that they have the potential to be integrated in future monitors of intra-operative awareness and anaesthetic adequacy.
Supervisor: Bell, Steven Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available