Wearable EEG-Based Depth of Anesthesia Monitoring: A Nonparametric Feature Set

Abstract

Objectives: Commercial systems for monitoring the depth of anesthesia (DoA) are often financially inaccessible to developing countries. As an alternative, a wearable single frontal electroencephalogram (EEG) device can be utilized. Nonetheless, most studies addressing DoA monitoring utilizing just one frontal EEG channel rely on nonlinear features that require parameter tuning before computation, overlooking the potential interchangeability of such features across different databases. Methods: Here, we present a parameter-free feature set for DoA monitoring using a single frontal EEG channel and evaluate its performance on two databases with different characteristics. First, the EEG signal is denoised and split into its subbands. Second, several parameter-free features based on entropy, power and frequency, fractal, and variation are extracted from all subbands. Finally, the distinguished features are chosen and input into a random forest regressor to estimate the DoA index values. Results: The reliability of the proposed feature set for the DoA monitoring is indicated by achieving a comparable correlation coefficient (CC) of 0.80 and 0.79 and mean absolute error (MAE) of 7.1 and 9.0 between the reference and estimated DoA index values for Databases I and II, respectively. Significance: The obtained results from this study confirm the possibility of affordable DoA monitoring using a portable EEG system. Given its simplicity and comparable results for both databases, the proposed feature set holds promise for practical application in real-world scenarios.