A computationally bio-inspired framework of brain activities based on cognitive processes for estimating the depth of anesthesia.

Abstract

This paper develops a computationally bio-inspired framework of brain activities based on concepts, such as sensory register (SR), encoding, emotion, short-term memory (STM), selective attention, working memory (WM), forgetting, long-term memory (LTM), sustained memory (SM), and response selection for estimating the depth of anesthesia (DOA) using electroencephalogram (EEG) signals. Different brain regions, such as the thalamus, cortex, neocortex, amygdala, striatum, basal ganglia, cerebellum, and hippocampus, are considered for developing a cognitive architecture and a computationally bio-inspired framework. A clinical study was managed on twenty-two patients corresponding to three anesthetic states, including awake state, moderate anesthesia, and general anesthesia. The proposed approach utilizes a multiple of dynamically reconfigurable neural networks with radial basis function (RBF) and its associated data processing mechanisms. The emotion effect in the model, dynamic RBFs in WM and LTMs, and adjusting the adaptive weights in the last layer are the main innovations of the proposed approach. In the proposed approach, various incoming information is entered into the model. The correct labeling process of EEG signals is performed by qualitative and quantitative analyses of peripheral parameters. Then, an SR is used to accumulate the pre-processed EEG segment for a period of 2.3s. Feature extraction is performed in the encoding stage as a primary perception. The output of this stage can be transferred to STM and WM with a bottom-up involuntary attentional capture. LTM and SM are a fairly permanent reservoir for information which is passed from WM using a top-down voluntary attention mechanism. Finally, weighting factors in SM and LTMs outputs are determined and then response selection is used by winner-take-all (WTA) strategy. The results indicate that the proposed approach can classify in different anesthetic states with an average accuracy of 89.2%. Results also indicate that the combined use of the above elements can effectively decipher the cognitive process task. A final comparison between the obtained results and the previous method on the same database indicate the effectiveness of the proposed approach for estimating DOA.