Research Article
Hybrid Adaptive Parametric Frequency Analysis
@ARTICLE{10.4108/eai.8-7-2020.165514, author={Kriton Konstantinidis and Emery N. Brown}, title={Hybrid Adaptive Parametric Frequency Analysis}, journal={EAI Endorsed Transactions on Bioengineering and Bioinformatics}, volume={1}, number={1}, publisher={EAI}, journal_a={BEBI}, year={2020}, month={7}, keywords={Time-Frequency Analysis, Electroencephalogram, State-Space, General Anesthesia, Propofol}, doi={10.4108/eai.8-7-2020.165514} }- Kriton Konstantinidis
Emery N. Brown
Year: 2020
Hybrid Adaptive Parametric Frequency Analysis
BEBI
EAI
DOI: 10.4108/eai.8-7-2020.165514
Abstract
INTRODUCTION: High quality spectra are very useful in anesthesia related procedures where Electroencephalogram (EEG) frequency content has been shown to drastically help track different brain states. A recent work (Konstantinidis & Brown, 2019 [1]) introduced the Gaussian Hybrid Autoregressive Model as a parametric method to generate smooth, very high resolution spectrograms of non-stationary EEG data of humans under propofol.
OBJECTIVE: In this paper, we extend the model proposed in [1] to incorporate non-Gaussian state noise.
METHODS: A Monte Carlo Markov Chain (MCMC) filtering procedure on a self-organizing state-space model is presented.
RESULTS: We test the extended model on EEGs from human patients under propofol, ketamine and sevoflurane and illustrate the advantages over its Gaussian counterpart.
CONCLUSION: The suitability of the proposed method for online use, in combination with its ability to smoothly track frequency changes in human EEG signals under the most common anesthetics, suggests that it can be used for real-time brain state tracking. Such online use can facilitate the design of more precise closed loop systems for automatic control of brain states under general anesthesia.
Copyright © 2020 Kriton Konstantinidis et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
