Abstract
Electroencephalography (EEG) signals may provide abundant information reflecting the developmental changes in brain status. It usually takes a long time to finally judge whether a brain is dead, so an effective pre-test of brain states method is needed. In this paper, we present a hybrid processing pipeline to differentiate brain death and coma patients based on canonical correlation analysis (CCA) of power spectral density, complexity features, and feature fusion for group analysis. In addition, time-varying power spectrum and complexity were observed based on the analysis of individual patients, which can be used to monitor the change of brain status over time. Results showed three major differences between brain death and coma groups of EEG signal: slowing, increased complexity, and the improvement on classification accuracy with feature fusion. To the best of our knowledge, this is the first scheme for joint general analysis and time-varying state monitoring. Delta-band relative power spectrum density and permutation entropy could effectively be regarded as potential features of discrimination analysis on brain death and coma patients.
Highlights
The generally accepted definition of brain death is the irreversible loss functions of the whole brain [1,2]
We investigated the relative Power spectrum density (PSD) features in group analysis and the time-varying spectral feature in special individual analysis over different frequency bands
RPSD values in δ band were at the interval [0.75 0.83], and the values in γ band greatly decreased into the the interval [0.0005 0.009]
Summary
The generally accepted definition of brain death is the irreversible loss functions of the whole brain (including hemisphere and brain stem) [1,2]. The procedure of brain death determination normally takes a long time and has certain dangerous operations (e.g., in apnea test, the respirator needs to be removed) [3] (see Figure 1). Electroencephalography (EEG) is frequently used to analyze and auxiliarily diagnose brain death clinically with the features of high time resolution and relative potable [4,5]. Since the characteristics of irreversible coma were first defined by the ad hoc Committee of Harvard. We aimed to use less electrodes to explore more distinctive results from EEG for brain death determination, differentiating brain death from coma
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