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Machine learning for a combined electroencephalographic anesthesia index to detect awareness under anesthesia. | LitMetric

AI Article Synopsis

  • - The study explores using spontaneous EEG and auditory evoked potentials (AEP) to monitor consciousness levels during anesthesia, suggesting that combining data from both can provide more comprehensive insights into brain activity.
  • - Machine learning algorithms are utilized to select and combine relevant parameters from EEG and AEP signals to create a predictive index for measuring responsiveness in patients under anesthesia.
  • - The analysis shows that a combined approach using both EEG and AEP data yields a prediction accuracy of 0.935, outperforming individual predictions from EEG (0.916) and AEP (0.880), demonstrating the effectiveness of machine learning in enhancing consciousness assessment.

Article Abstract

Spontaneous electroencephalogram (EEG) and auditory evoked potentials (AEP) have been suggested to monitor the level of consciousness during anesthesia. As both signals reflect different neuronal pathways, a combination of parameters from both signals may provide broader information about the brain status during anesthesia. Appropriate parameter selection and combination to a single index is crucial to take advantage of this potential. The field of machine learning offers algorithms for both parameter selection and combination. In this study, several established machine learning approaches including a method for the selection of suitable signal parameters and classification algorithms are applied to construct an index which predicts responsiveness in anesthetized patients. The present analysis considers several classification algorithms, among those support vector machines, artificial neural networks and Bayesian learning algorithms. On the basis of data from the transition between consciousness and unconsciousness, a combination of EEG and AEP signal parameters developed with automated methods provides a maximum prediction probability of 0.935, which is higher than 0.916 (for EEG parameters) and 0.880 (for AEP parameters) using a cross-validation approach. This suggests that machine learning techniques can successfully be applied to develop an improved combined EEG and AEP parameter to separate consciousness from unconsciousness.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7449480PMC
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0238249PLOS

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