Non-sinusoidal waves in the EEG and their simulated effect on anaesthetic quantitative EEG monitors.

J Clin Monit Comput

Department of Anaesthesiology, Waikato Clinical School, University of Auckland, Hamilton, New Zealand.

Published: December 2019

The effect of anaesthetic drugs on the cortex are commonly estimated from the electroencephalogram (EEG) by quantitative EEG monitors such as the Bispectral Index (BIS). These monitors use ratios of high to low frequency power which assumes that each neurological process contributes a unique frequency pattern. However, recent research of the effect of deep brain stimulation on EEG beta oscillations suggests that wave shape, a non-sinusoidal feature that is only measurable in the time-domain, can change the frequency 'signature' of a neurological rhythmical process by the inclusion or removal of harmonic frequencies. If wave shape variations are present in the EEG of anaesthetised patients, then quantitative EEG monitors likely overestimate the anaesthetic drug effect. The purpose of this paper is to investigate alpha-wave shape in the EEG of anaesthetised patients and demonstrate the effect of wave shape on the frequency ratios that are commonly utilised in the BIS quantitative EEG monitor. EEG data, demographic information, and surgery details were collected prospectively from 305 patients undergoing a general anaesthetic for elective surgery. Alpha-wave shape was categorised by triangularity of the EEG extrema, a measure of how peaked (towards a sawtooth wave) or flat (towards a square wave) the extremum was. The alpha-wave was then artificially modified to either a sawtooth wave or square wave, and BetaRatio and PowerFastSlow metrics calculated. Age was found to be the only significant predictor of alpha wave triangularity. The artificially modified square-alpha waves increased the power in the frequency spectrum at 26 Hz by 1-5 dB, and increased the BetaRatio by 0.7. The alpha-wave of anaesthetised patients contains non-sinusoidal components which likely impact depth of anaesthesia calculations.

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10877-019-00254-7DOI Listing

Publication Analysis

Top Keywords

quantitative eeg
16
eeg monitors
12
wave shape
12
anaesthetised patients
12
eeg
11
wave
8
eeg anaesthetised
8
alpha-wave shape
8
sawtooth wave
8
square wave
8

Similar Publications

Purpose: Population level tracking of post-stroke functional outcomes is critical to guide interventions that reduce the burden of stroke-related disability. However, functional outcomes are often missing or documented in unstructured notes. We developed a natural language processing (NLP) model that reads electronic health records (EHR) notes to automatically determine the modified Rankin Scale (mRS).

View Article and Find Full Text PDF

Background: F-8-coil repetitive transcranial magnetic stimulation (rTMS) and H-1-coil deep repetitive transcranial magnetic stimulation (dTMS) have been indicated for the treatment of major depressive disorder (MDD) in adult patients by applying different treatment protocols. Nevertheless, the evidence for long-term electrophysiological alterations in the cortex following prolonged TMS interventions, as assessed by quantitative electroencephalography (qEEG), remains insufficiently explored. This study aims to demonstrate the qEEG-based distinctions between rTMS and dTMS in the management of depression and to evaluate the potential correlation between the electrophysiological changes induced by these two distinct TMS interventions and the clinical improvement in depressive and anxiety symptoms.

View Article and Find Full Text PDF

Post-traumatic epilepsy (PTE) is a debilitating chronic outcome of traumatic brain injury (TBI). Although FTO has been reported as a possible intervention target of TBI, its precise roles in the PTE remain incompletely understood. Here we used mild or serious mice TBI model to probe the role and molecular mechanism of FTO in PTE.

View Article and Find Full Text PDF

Task-free brain activity affords unique insight into the functional structure of brain network dynamics and has been used to identify neural markers of individual differences. In this work, we present an algorithmic optimization framework that directly inverts and parameterizes brain-wide dynamical-systems models involving hundreds of interacting neural populations, from single-subject M/EEG time-series recordings. This technique provides a powerful neurocomputational tool for interrogating mechanisms underlying individual brain dynamics ("precision brain models") and making quantitative predictions.

View Article and Find Full Text PDF

In recent years, the utilization of motor imagery (MI) signals derived from electroencephalography (EEG) has shown promising applications in controlling various devices such as wheelchairs, assistive technologies, and driverless vehicles. However, decoding EEG signals poses significant challenges due to their complexity, dynamic nature, and low signal-to-noise ratio (SNR). Traditional EEG pattern recognition algorithms typically involve two key steps: feature extraction and feature classification, both crucial for accurate operation.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!