Hybrid Adaptive Parametric Frequency Analysis

Konstantinidis, Kriton and Brown, Emery (2021) Hybrid Adaptive Parametric Frequency Analysis. EAI Endorsed Transactions on Bioengineering and Bioinformatics, 1 (1). e5. ISSN 2709-4111

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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.

Item Type: Article
Uncontrolled Keywords: Time-Frequency Analysis, Electroencephalogram, State-Space, General Anesthesia, Propofol
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
Depositing User: EAI Editor II.
Date Deposited: 03 Mar 2021 08:57
Last Modified: 03 Mar 2021 08:57
URI: https://eprints.eudl.eu/id/eprint/1154

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