Comparison of the effect of electromyogram activity during emergence on anesthetic depth monitoring between phase lag entropy and bispectral index: a prospective observational study

Abstract

Background: The bispectral index (BIS) is the most widely used algorithm for measuring anesthetic depth.The BIS has been demonstrated as inaccurate when neuromuscular blocking drugs (NMBDs) are used.Compared with BIS, phase lag entropy (PLE), which measures the anesthetic depth based on a 4-channel EEG signal, is less affected by EMG. The purpose of this study was to compare the effect of EMG activity during emergence on anesthetic depth monitoring between PLE and BIS.Methods: Twenty five consecutive patients with physical status I-II of American Society of Anesthesiologists undergoing general anesthesia (age range, 20-60 years). The anesthesiologist attached the sensors of BIS and PLEM 100 on the patient's forehead. NMB reversal was performed by intravenously injecting sugammadex after confirmation of shallow NMB (TOF count 1-4) under neuromuscular monitoring. The BIS and PLE scores were recorded with neuromuscular monitoring at 1-min intervals for 5 min after administration of sugammadex. Results:The BIS and BIS-EMG measured at 1 min after sugammadex injection were significantly higher

EEG Connectivity Diversity Differences between Children with Autism and Typically Developing Children: A Comparative Study

“…The phase lag entropy method combines the temporal dynamics of instantaneous phase with phase synchronization analysis. The calculation process of PLE algorithm is as follows [ 16 ]: Extracting the phase difference of two EEG signals and through Hilbert transform; Converting the phase difference into binary form , which represents the symbolic result of the phase relationship between signals X(t) and Y(t) at time t. The symbolization rule is as follows: if leads , i.e., , then set the symbol parameter ; If lags behind , i.e., , then set the symbol parameter . Defining the temporal dynamic pattern of the phase relationship between signals and : where and represent the embedding dimension and time lag parameters, respectively.…”

“…In this study, we choose l = 3 (dimensionless) and τ = 6 (unit: timepoints). Based on the dynamic phase relationship pattern and the theory of Shannon entropy, the definition of PLE is as follows [ 16 ]: …”

“…Based on the dynamic phase relationship pattern and the theory of Shannon entropy, the definition of PLE is as follows [ 16 ]:…”

“…The phase lag entropy method combines the temporal dynamics of instantaneous phase with phase synchronization analysis. The calculation process of PLE algorithm is as follows [16]…”

“…Synchronization emerges and dissipates on a sub-second timescale, and fluctuating functional connectivity patterns have been observed in resting-state EEG [ 14 ] and fMRI data [ 15 ]. In this study, we introduced a novel measurement method called phase lag entropy (PLE) to quantify the diversity of temporal patterns in phase relationships [ 16 ]. PLE is a new phase synchronization estimation algorithm that can extract synchronization information between neural signals at a sub-second time scale while effectively avoiding mutual cancellation of synchronization information.…”