Comparisons of Electroencephalographically Derived Measures of Hypnosis and Antinociception in Response to Standardized Stimuli During Target-Controlled Propofol-Remifentanil Anesthesia

Abstract

Combining electroencephalographically derived hypnotic and analgesic quantifiers may enable better prediction of patients who are likely to respond to tetanic stimulation.

Tracking Electroencephalographic Changes Using Distributions of Linear Models: Application to Propofol-Based Depth of Anesthesia Monitoring

“…A comparison of ARMA model parameter estimation methods using the Broersen technique [21] and Kalman filtering [28] is made to evaluate the effect of different estimation methods on the performance of our distributionbased approach. The Broersen technique is the primary estimation technique underlying the brain anaesthesia response monitor (Cortical Dynamics, Australia) [8], [19], [20], [25] and Kalman filtering represents a time-domain alternative for estimating the parameters. In addition, given that the application domain is depth of anesthesia monitoring, the performance of the distribution-based approach using ARMA model parameters is compared to the benchmark performance of the distributionbased approach using a depth of anesthesia monitoring measure, Higuchi fractal dimension (HFD) [7], that can be regarded as one of the best performing hypnotic measures evaluated to date and has been evaluated over the same dataset as considered here [7].…”

“…Note that this is true for all linearizations about any physiologically plausible stable fixed point of the six-dimensional state and eightdimensional state Liley models. Athough variations of the Liley model have not been directly applied to the modeling of propofol anesthesia, the use of an ARMA (8, 5) model approach to derive a cortical state and cortical input features, and inspired by the Liley model, has been shown to be able to reliably track the propofol-induced anesthetic state [8], [19], [20]. In order to relate our methods to physiology, selection of ARMA model orders that correspond to the model orders of transfer functions of linearized versions of neural models is preferred over the use of model order selection approaches based on information theoretic criteria [30].…”

“…This was performed to avoid spurious fitting to 50-Hz spectral peaks or any low-pass filter band edges. For the original EEG time series, the electromyogram (EMG-detected as the total power between 70 and 110 Hz excluding a notch at 98-102 Hz due to 50 Hz electric power harmonic at 100 Hz) was calculated [8], [19], [20]. The root mean square (rms) amplitude was also calculated from the resampled electroencephalogram time series.…”

“…Our hypothesis is that the cortical region underlying the frontal EEG recording electrode can be modeled by a nonlinear dynamical system. This nonlinear model can be approximated by the concatenation over time of linearized approximations of the model which are estimated using ARMA modeling and an appropriately defined sliding window [8], [19], [20], [25]. In this paper, the focus is only on the linear ARMA model estimates for tracking depth of anesthesia.…”

Prediction of hemodynamic reactivity by electroencephalographically derived Pain Rating Index in children during general anesthesia: a prospective observational study

“…Furthermore, many confounding factors such as hypovolemia, β-blockers, anticholinergic drugs can also interfere with these clinical signs [12]. In recent years, several noninvasive monitoring tools based on skin conductance (SC), pupil diameter, heart rate variability (HRV), amplitude from photoplethymography (Surgical Pleth Index) or analysis of electroencephalogram (EEG) signals to estimate the antinociceptive state during general anesthesia has been used in adults, but less common in children, which might be due to age-dependent characteristics of EEG, ECG or lack of pediatric electrodes [11,[13][14][15][16][17].…”