A New Index of Coordinated Posterior and Anterior Evoked EEG to Detect Recall Under Sedation - A Pilot Study

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.

Intraoperative EEG-based monitors: are we looking under the lamppost?

PURPOSE OF REVIEW

While electroencephalogram (EEG)-based depth of anesthesia monitors have been in use clinically for decades, there is still a major debate concerning their efficacy for detecting awareness under anesthesia (AUA). Further utilization of these monitors has also been discussed vividly, for example, reduction of postoperative delirium (POD).It seems that with regard to reducing AUA and POD, these monitors might be applicable, under specific anesthetic protocols. But in other settings, such monitoring might be less contributive and may have a 'built-it glass ceiling'.Recent advances in other venues of electrophysiological monitoring might have a strong theoretical rationale, and early supporting results, to offer a breakthrough out of this metaphorical glass ceiling. The purpose of this review is to present this possibility.

RECENT FINDINGS

Following previous findings, it might be concluded that for some anesthesia protocols, the prevailing depth of anesthesia monitors may prevent incidences of AUA and POD. However, in other settings, which may involve other anesthesia protocols, or specifically for POD - other perioperative causes, they may not. Attention-related processes measured by easy-to-use real-time electrophysiological markers are becoming feasible, also under anesthesia, and might be applicable for more comprehensive prevention of AUA, POD and possibly other perioperative complications.

SUMMARY

Attention-related monitoring might have a strong theoretical basis for the prevention of AUA, POD, and potentially other distressing postoperative outcomes, such as stroke and postoperative neurocognitive disorder. There seems to be already some initial supporting evidence in this regard.

Identifying Recall Under Sedation by a Novel EEG Based Index of Attention—A Pilot Study

Overview

Recall is an accepted consequence of sedation. But due to the very low prevalence of the more devastating awareness under anesthesia (AUA), it might be of value to assess first the efficacy of new markers for AUA by their efficacy in discovering the more prevalent recall under sedation (RUS). In this pilot study we assessed whether a novel index for attentional effort, the cognitive effort index (CEI), derived in real-time from one forehead EEG channel, could differentiate between patients with or without RUS.

Methods

EEG was sampled from 2 groups: (1) Patients who underwent deep sedation (n = 25) (using drugs according to the anesthesiologist preference, but generally combining either Midazolam or Propofol together with either Fentanyl or Remifentanil). (2) Patients who underwent general anesthesia (GA, n = 13, a negative control for recall). In recovery, recall was assessed using the BRICE questionnaire.

Results

Of the 25 sedated patients, 11 experienced recall. The CEI marker was high during significantly longer periods in patients with recall, compared to sedated patients, or patients under GA, without recall. Moreover, the increase in CEI was evident mainly toward the end of the procedure.

Conclusion

RUS seems to associate with higher level of attention, which is identified in real-time by the easy-to-extract CEI marker.