Sudden Bispectral Index Reduction and Suppression Ratio Increase Associated with Bradycardia in a Patient Undergoing Breast Conserving Surgery

The use of processed electroencephalography (pEEG) in obstetric anaesthesia: a narrative review

Accidental awareness under general anaesthesia (AAGA) remains a major complication of anaesthesia. The incidence of AAGA during obstetric anaesthesia is high relative to other specialities. The use of processed electroencephalography (pEEG) in the form of "depth of anaesthesia" monitoring has been shown to reduce the incidence of AAGA in the non-obstetric population. The evidence for using pEEG to prevent AAGA in the obstetric population is poor and requires further exploration. Furthermore, pregnancy and disease states affecting the central nervous system, such as pre-eclampsia, may alter the interpretation of pEEG waveforms although this has not been fully characterised. National guidelines exist for pEEG monitoring with total intravenous anaesthesia and for "high-risk" cases regardless of technique, including the obstetric population. However, none of the currently available guidelines relates specifically to obstetric anaesthesia. Using pEEG monitoring for obstetric anaesthesia may also provide additional benefits beyond a reduction in risk of AAGA. These potential benefits include reduced postoperative nausea and vomiting, reduced anaesthetic agent use, a shorter post-anaesthetic recovery stay. In addition, pEEG acts as a surrogate marker of cerebral perfusion, and thus as an additional monitor for impending cardiovascular collapse, as seen in amniotic fluid embolism. The subtle physiological and pathological changes in EEG activity that may occur during pregnancy are an unexplored research area in the context of anaesthetic pEEG monitors. We believe that the direction of clinical practice is moving towards greater use of pEEG monitoring and individualisation of anaesthesia.

Assessing rheoencephalography dynamics through analysis of the interactions among brain and cardiac networks during general anesthesia

Cerebral blood flow (CBF) reflects the rate of delivery of arterial blood to the brain. Since no nutrients, oxygen or water can be stored in the cranial cavity due to space and pressure restrictions, a continuous perfusion of the brain is critical for survival. Anesthetic procedures are known to affect cerebral hemodynamics, but CBF is only monitored in critical patients due, among others, to the lack of a continuous and affordable bedside monitor for this purpose. A potential solution through bioelectrical impedance technology, also known as rheoencephalography (REG), is proposed, that could fill the existing gap for a low-cost and effective CBF monitoring tool. The underlying hypothesis is that REG signals carry information on CBF that might be recovered by means of the application of advanced signal processing techniques, allowing to track CBF alterations during anesthetic procedures. The analysis of REG signals was based on geometric features extracted from the time domain in the first place, since this is the standard processing strategy for this type of physiological data. Geometric features were tested to distinguish between different anesthetic depths, and they proved to be capable of tracking cerebral hemodynamic changes during anesthesia. Furthermore, an approach based on Poincaré plot features was proposed, where the reconstructed attractors form REG signals showed significant differences between different anesthetic states. This was a key finding, providing an alternative to standard processing of REG signals and supporting the hypothesis that REG signals do carry CBF information. Furthermore, the analysis of cerebral hemodynamics during anesthetic procedures was performed by means of studying causal relationships between global hemodynamics, cerebral hemodynamics and electroencephalogram (EEG) based-parameters. Interactions were detected during anesthetic drug infusion and patient positioning (Trendelenburg positioning and passive leg raise), providing evidence of the causal coupling between hemodynamics and brain activity. The provided alternative of REG signal processing confirmed the hypothesis that REG signals carry information on CBF. The simplicity of the technology, together with its low cost and easily interpretable outcomes, should provide a new opportunity for REG to reach standard clinical practice. Moreover, causal relationships among the hemodynamic physiological signals and brain activity were assessed, suggesting that the inclusion of REG information in depth of anesthesia monitors could be of valuable use to prevent unwanted CBF alterations during anesthetic procedures.