Quantifying Cortical Activity During General Anesthesia Using Wavelet Analysis

Abstract

This paper reports on a novel method for quantifying the cortical activity of a patient during general anesthesia as a surrogate measure of the patient's level of consciousness. The proposed technique is based on the analysis of a single-channel (frontal) electroencephalogram (EEG) signal using stationary wavelet transform (SWT). The wavelet coefficients calculated from the EEG are pooled into a statistical representation, which is then compared to two well-defined states: the awake state with normal EEG activity, and the isoelectric state with maximal cortical depression. The resulting index, referred to as the wavelet-based anesthetic value for central nervous system monitoring (WAV(CNS)), quantifies the depth of consciousness between these two extremes. To validate the proposed technique, we present a clinical study which explores the advantages of the WAV(CNS) in comparison with the BIS monitor (Aspect Medical Systems, MA), currently a reference in consciousness monitoring. Results show that the WAV(CNS) and BIS are well correlated (r = 0.969) during periods of steady-state despite fundamental algorithmic differences. However, in terms of dynamic behavior, the WAV(CNS) offers faster tracking of transitory changes at induction and emergence, with an average lead of 15-30 s. Furthermore, and conversely to the BIS, the WAV(CNS) regains its preinduction baseline value when patients are responding to verbal command after emergence from anesthesia. We conclude that the proposed analysis technique is an attractive alternative to BIS monitoring. In addition, we show that the WAV(CNS) dynamics can be modeled as a linear time invariant transfer function. This index is, therefore, well suited for use as a feedback sensor in advisory systems, closed-loop control schemes, and for the identification of the pharmacodynamic models of anesthetic drugs.

Ultra-low Noise EEG at LSBB: New results

“…Each epoch was then normalized in order to reduce the influence of electrode impedance and alleviate the disparities across subjects due to factors such as skin conductivity, which yielded epochs with comparable amplitude values. Channel-wise normalization was performed in the time domain by removing the mean and dividing by the root-mean-square value of the corresponding channel [4], according to the equation below: We measure the high frequency activity during each of the epochs by computing the total gamma band energy of the epoch in the time-frequency space, defined as:…”

“…Advancements in EEG signal acquisition and analysis have enabled improved diagnostic capabilities for seizure detection [1], sleep disorders [2], and Alzheimer's disease [3], just to name a few. EEG has also been extensively used to monitor anesthesia and sedation in the operating room and intensive care unit [4].…”

“…To reduce the disruptive effects of electrical interference and movement (i.e. noise), depending on the neuronal activity of interest, EEG is often band-pass filtered at different frequencies from 0.5 to 100 Hz and is then studied in terms of activity across different frequency bands; including (0-4 Hz), (4)(5)(6)(7), (8)(9)(10)(11)(12) Hz), (12-30 Hz), and (30-100 Hz). Clinically, EEG frequencies are most often studied from 0.5 to 30 Hz with the gamma ( ) band being ignored.…”

Towards a Multivariable Model for Controlling the Depth of Anaesthesia using Propofol and Remifentanil

“…Bispectral Index BIS, Auditory Evoked Potential, WAV), there exists no "pain sensor" which measures analgesia directly (Absalom et al, 2011;Zikov et al, 2006). However, there are several systems for analgesia control reported in the literature.…”

Benchmark Problem: A PK/PD Model and Safety Constraints for Anesthesia Delivery

“…As one example, a second order linear model of the W AV CN S Monitor is derived in [14]. In a surgical setting, it may not be feasible to estimate the state or adjust the input in continuous time, so it is natural to consider a discrete time model.…”