Tracking electroencephalographic changes using distributions of linear models: Application to propofol-based depth of anesthesia monitoring
Tutkimustuotos › › vertaisarvioitu
|Julkaisu||IEEE Transactions on Biomedical Engineering|
|Varhainen verkossa julkaisun päivämäärä||2016|
|DOI - pysyväislinkit|
|Tila||Julkaistu - 2017|
Objective: Tracking brain states with electrophysiological measurements often relies on short-term averages of extracted features and this may not adequately capture the variability of brain dynamics. The objective is to assess the hypotheses that this can be overcome by tracking distributions of linear models using anesthesia data, and that anesthetic brain state tracking performance of linear models is comparable to that of a high performing depth of anesthesia monitoring feature. Methods: Individuals’ brain states are classified by comparing the distribution of linear (Auto-Regressive Moving Average - ARMA) model parameters estimated from electroencephalographic (EEG) data obtained with a sliding window to distributions of linear model parameters for each brain state. The method is applied to frontal EEG data from 15 subjects undergoing propofol anesthesia and classified by the observers assessment of alertness/sedation (OAA/S) scale. Classification of the OAA/S score was performed using distributions of either ARMA parameters or the benchmark feature, Higuchi Fractal Dimension. Results: The highest average testing sensitivity of 59% (chance sensitivity 17%) was found for ARMA (2; 1) models and Higuchi Fractal Dimension achieved 52%, however, no statistical difference was observed. For the same ARMA case, there was no statistical difference if medians are used instead of distributions (sensitivity: 56%). Conclusion: The model-based distribution approach is not necessarily more effective than a median/short-term average approach, however, it performs well compared to a distribution approach based on a high performing anesthesia monitoring measure. Significance: These techniques hold potential for anesthesia monitoring and may be generally applicable to tracking brain states.