Do Complexity Measures of Frontal EEG Distinguish Loss of Consciousness in Geriatric Patients Under Anesthesia?

Abstract

While geriatric patients have a high likelihood of requiring anesthesia, they carry an increased risk for adverse cognitive outcomes from its use. Previous work suggests this could be mitigated by better intraoperative monitoring using indexes defined by several processed electroencephalogram (EEG) measures. Unfortunately, inconsistencies between patients and anesthetic agents in current analysis techniques have limited the adoption of EEG as standard of care. In attempts to identify new analyses that discriminate clinically-relevant anesthesia timepoints, we tested 1/f frequency scaling as well as measures of complexity from nonlinear dynamics. Specifically, we tested whether analyses that characterize time-delayed embeddings, correlation dimension (CD), phase-space geometric analysis, and multiscale entropy (MSE) capture loss-of-consciousness changes in EEG activity. We performed these analyses on EEG activity collected from a traditionally hard-to-monitor patient population: geriatric patients on beta-adrenergic blockade who were anesthetized using a combination of fentanyl and propofol. We compared these analyses to traditional frequency-derived measures to test how well they discriminated EEG states before and after loss of response to verbal stimuli. We found spectral changes similar to those reported previously during loss of response. We also found significant changes in 1/f frequency scaling. Additionally, we found that our phase-space geometric characterization of time-delayed embeddings showed significant differences before and after loss of response, as did measures of MSE. Our results suggest that our new spectral and complexity measures are capable of capturing subtle differences in EEG activity with anesthesia administration—differences which future work may reveal to improve geriatric patient monitoring.

Complexity measures of the electroencephalograph capture loss and recovery of consciousness in patients anesthetized with propofol

“…Specifically, we observed that initially spherical attractors became flattened and more ellipsoidal when moving from waking to loss of righting reflex (MacIver & Bland, 2014). We observed a similar change in attractor shape when characterizing human subjects anesthetized with a combination of remifentanil and nitrous oxide , and another patient population anesthetized with propofol and fentanyl (Eagleman, Vaughn, et al, 2018). We quantified the shape changes we observed using a novel geometric phase-space analysis, termed the ellipse radius ratio (ERR).…”

“…We fit the 3D attractor with an ellipsoidal solid of revolution (Khachiyan, 1980) and then calculated the ratio between the major and minor axes. This ellipse radius ratio changes significantly and consistently before and after loss and recovery of response during several anesthetic regimes (Eagleman, Vaughn, et al, 2018;. Additionally, the ERR also shows a significant difference in adult patients across a wide range of ages (Eagleman, Vaughn, et al, 2018;.…”

“…This ellipse radius ratio changes significantly and consistently before and after loss and recovery of response during several anesthetic regimes (Eagleman, Vaughn, et al, 2018;. Additionally, the ERR also shows a significant difference in adult patients across a wide range of ages (Eagleman, Vaughn, et al, 2018;. To make sense of this shape change, we compared this analysis to other complexity measures such as the correlation dimension and multiscale entropy, and found a significant correlation with multiscale entropy (Eagleman, Vaughn, et al, 2018).…”

Evaluating Complexity of Fetal MEG Signals: A Comparison of Different Metrics and Their Applicability

“…Studies with Propofol anesthesia showed a change in scale free behavior before and after loss of consciousness (Eagleman et al, 2018) and a difference between wakefulness and loss of consciousness as well as recovery from anesthesia (Tagliazucchi et al, 2016). Also with the help of entropy measures, Eagleman et al (2018) could show a change in complexity of scalp EEG data before and after loss of responsiveness in anesthesia patients. Similarly, Schartner et al (2015) could distinguish between loss of consciousness during Propofol induced anesthesia and wakeful rest by means of entropy measures as well as compressibility measures.…”

“…In general, the measurement of this scale free behavior appears promising in the investigation of state transitions (Weiss et al, 2009). Studies with Propofol anesthesia showed a change in scale free behavior before and after loss of consciousness (Eagleman et al, 2018) and a difference between wakefulness and loss of consciousness as well as recovery from anesthesia (Tagliazucchi et al, 2016). Also with the help of entropy measures, Eagleman et al (2018) could show a change in complexity of scalp EEG data before and after loss of responsiveness in anesthesia patients.…”