Electroencephalogram Monitoring in Anesthesia Practice

Intraoperative use of processed electroencephalogram in a quaternary center: a quality improvement audit

Although intraoperative electroencephalography (EEG) is not consensual among anesthesiologists, growing evidence supports its use to titrate anesthetic drugs, assess the level of arousal/consciousness, and detect ischemic cerebrovascular events; in addition, intraoperative EEG monitoring may decrease the incidence of postoperative neurocognitive disorders. Based on the known and potential benefits of intraoperative EEG monitoring, an educational program dedicated to staff anesthesiologists, residents of Anesthesiology and anesthesia technicians was started at Cleveland Clinic Abu Dhabi in May 2022 and completed in June 2022, aiming to have all patients undergoing general anesthesia with adequate brain monitoring and following international initiatives promoting perioperative brain health. All the surgical cases performed under General Anesthesia at 24 daily locations were prospectively inspected during 15 consecutive working days in March 2023. The use or absence of a processed EEG monitor was registered. Of 379 surgical cases distributed by 24 locations under General Anesthesia, 233 cases (61%) had processed EEG monitoring. The specialty with the highest use of EEG monitoring was Cardiothoracic Surgery, with 100% of cases, followed by interventional Cardiology (90%) and Vascular Surgery (75%). Otorhinolaryngology (29%), Gastrointestinal Endoscopy (25%), and Interventional Pulmonology (20%) were the areas with the lowest use of EEG monitoring. Of note, in the Neuroradiology suite, no processed EEG monitor was used in cases under General Anesthesia. We identified a reasonable use of EEG monitoring during general anesthesia, unfortunately not reaching our target of 100%. The educational and support program previously implemented within the Anesthesiology Institute needs to be continued and improved, including workshops, online discussions, and journal club sessions, to increase the use of EEG monitoring in underused areas.

Harnessing machine learning for EEG signal analysis: Innovations in depth of anaesthesia assessment

Anaesthesia, crucial to surgical practice, is undergoing renewed scrutiny due to the integration of artificial intelligence in its medical use. The precise control over the temporary loss of consciousness is vital to ensure safe, pain-free procedures. Traditional methods of depth of anaesthesia (DoA) assessment, reliant on physical characteristics, have proven inconsistent due to individual variations. In response, electroencephalography (EEG) techniques have emerged, with indices such as the Bispectral Index offering quantifiable assessments. This literature review explores the current scope and frontier of DoA research, emphasising methods utilising EEG signals for effective clinical monitoring. This review offers a critical synthesis of recent advances, specifically focusing on electroencephalography (EEG) techniques and their role in enhancing clinical monitoring. By examining 117 high-impact papers, the review delves into the nuances of feature extraction, model building, and algorithm design in EEG-based DoA analysis. Comparative assessments of these studies highlight their methodological approaches and performance, including clinical correlations with established indices like the Bispectral Index. The review identifies knowledge gaps, particularly the need for improved collaboration for data access, which is essential for developing superior machine learning models and real-time predictive algorithms for patient management. It also calls for refined model evaluation processes to ensure robustness across diverse patient demographics and anaesthetic agents. The review underscores the potential of technological advancements to enhance precision, safety, and patient outcomes in anaesthesia, paving the way for a new standard in anaesthetic care. The findings of this review contribute to the ongoing discourse on the application of EEG in anaesthesia, providing insights into the potential for technological advancement in this critical area of medical practice.

Processed Electroencephalogram-Based Monitoring to Guide Sedation in Critically Ill Adult Patients: Recommendations from an International Expert Panel-Based Consensus

BACKGROUND

The use of processed electroencephalography (pEEG) for depth of sedation (DOS) monitoring is increasing in anesthesia; however, how to use of this type of monitoring for critical care adult patients within the intensive care unit (ICU) remains unclear.

METHODS

A multidisciplinary panel of international experts consisting of 21 clinicians involved in monitoring DOS in ICU patients was carefully selected on the basis of their expertise in neurocritical care and neuroanesthesiology. Panelists were assigned four domains (techniques for electroencephalography [EEG] monitoring, patient selection, use of the EEG monitors, competency, and training the principles of pEEG monitoring) from which a list of questions and statements was created to be addressed. A Delphi method based on iterative approach was used to produce the final statements. Statements were classified as highly appropriate or highly inappropriate (median rating ≥ 8), appropriate (median rating ≥ 7 but < 8), or uncertain (median rating < 7) and with a strong disagreement index (DI) (DI < 0.5) or weak DI (DI ≥ 0.5 but < 1) consensus.

RESULTS

According to the statements evaluated by the panel, frontal pEEG (which includes a continuous colored density spectrogram) has been considered adequate to monitor the level of sedation (strong consensus), and it is recommended by the panel that all sedated patients (paralyzed or nonparalyzed) unfit for clinical evaluation would benefit from DOS monitoring (strong consensus) after a specific training program has been performed by the ICU staff. To cover the gap between knowledge/rational and routine application, some barriers must be broken, including lack of knowledge, validation for prolonged sedation, standardization between monitors based on different EEG analysis algorithms, and economic issues.

CONCLUSIONS

Evidence on using DOS monitors in ICU is still scarce, and further research is required to better define the benefits of using pEEG. This consensus highlights that some critically ill patients may benefit from this type of neuromonitoring.

Signatures of Thalamocortical Alpha Oscillations and Synchronization With Increased Anesthetic Depths Under Isoflurane

Background: Electroencephalography (EEG) recordings under propofol exhibit an increase in slow and alpha oscillation power and dose-dependent phase–amplitude coupling (PAC), which underlie GABAA potentiation and the central role of thalamocortical entrainment. However, the exact EEG signatures elicited by volatile anesthetics and the possible neurophysiological mechanisms remain unclear.

Methods: Cortical EEG signals and thalamic local field potential (LFP) were recorded in a mouse model to detect EEG signatures induced by 0.9%, 1.5%, and 2.0% isoflurane. Then, the power of the EEG spectrum, thalamocortical coherence, and slow–alpha phase–amplitude coupling were analyzed. A computational model based on the thalamic network was used to determine the primary neurophysiological mechanisms of alpha spiking of thalamocortical neurons under isoflurane anesthesia.

Results: Isoflurane at 0.9% (light anesthesia) increased the power of slow and delta oscillations both in cortical EEG and in thalamic LFP. Isoflurane at 1.5% (surgery anesthesia) increased the power of alpha oscillations both in cortical EEG and in thalamic LFP. Isoflurane at 2% (deep anesthesia) further increased the power of cortical alpha oscillations, while thalamic alpha oscillations were unchanged. Thalamocortical coherence of alpha oscillation only exhibited a significant increase under 1.5% isoflurane. Isoflurane-induced PAC modulation remained unchanged throughout under various concentrations of isoflurane. By adjusting the parameters in the computational model, isoflurane-induced alpha spiking in thalamocortical neurons was simulated, which revealed the potential molecular targets and the thalamic network involved in isoflurane-induced alpha spiking in thalamocortical neurons.

Conclusion: The EEG changes in the cortical alpha oscillation, thalamocortical coherence, and slow–alpha PAC may provide neurophysiological signatures for monitoring isoflurane anesthesia at various depths.