Monitoring the Depth of Anesthesia Using a New Adaptive Neurofuzzy System

Abstract

Accurate and noninvasive monitoring of the depth of anesthesia (DoA) is highly desirable. Since the anesthetic drugs act mainly on the central nervous system, the analysis of brain activity using electroencephalogram (EEG) is very useful. This paper proposes a novel automated method for assessing the DoA using EEG. First, 11 features including spectral, fractal, and entropy are extracted from EEG signal and then, by applying an algorithm according to exhaustive search of all subsets of features, a combination of the best features (Beta-index, sample entropy, shannon permutation entropy, and detrended fluctuation analysis) is selected. Accordingly, we feed these extracted features to a new neurofuzzy classification algorithm, adaptive neurofuzzy inference system with linguistic hedges (ANFIS-LH). This structure can successfully model systems with nonlinear relationships between input and output, and also classify overlapped classes accurately. ANFIS-LH, which is based on modified classical fuzzy rules, reduces the effects of the insignificant features in input space, which causes overlapping and modifies the output layer structure. The presented method classifies EEG data into awake, light, general, and deep states during anesthesia with sevoflurane in 17 patients. Its accuracy is 92% compared to a commercial monitoring system (response entropy index) successfully. Moreover, this method reaches the classification accuracy of 93% to categorize EEG signal to awake and general anesthesia states by another database of propofol and volatile anesthesia in 50 patients. To sum up, this method is potentially applicable to a new real-time monitoring system to help the anesthesiologist with continuous assessment of DoA quickly and accurately.

Identification of ghost artifact using texture analysis in pediatric spinal cord diffusion tensor images

“…In the recent decade, researchers have paid attention to the ANFIS [26] which is an improved version of the NFS and is a kind of ANN based on the Takagi– Sugeno fuzzy inference system. The ANFIS structure can successfully model systems with nonlinear relationships between input and output, and are widely used in biomedical applications [27]. ANFIS is the combination of the multilayer perception (MLP) neural network and a fuzzy logic inference system and uses benefits of these two powerful methods.…”

“…Neural learning uses select and tune fuzzy parameters. The speed and accuracy of parameter learning, the small number of adjustable parameters, and the performance of the model in classification of different diseases are the advantages of ANFIS structure [27]. ANFIS requires normalizing the input data during the training process to improve its efficiency.…”

Techniques and Methods for Biosignal Analysis for Monitoring the Depth of Anesthesia

“…Разом із моніторингом стану ЦНС та сигналів периферичної серцево-судинної системи сьогодні проводять дослідження методів визначення глибини седації нервової системи на основі комплексного аналізу біологічних сигналів [11], [12], [18]- [24] Детальніше про ці дослідження буде розказано у розділі ІІІ.…”

Use of Multiple EEG Features and Artificial Neural Network to Monitor the Depth of Anesthesia

“…Liu et al used random forest with nonstationary signal features to estimate DoA through human EEG signal at different levels of unconsciousness [31]. Shalbaf et al assessed DoA using Adaptive Neurofuzzy System with spectral, fractal, and entropy [32]. Then they assessed the level of anesthesia with sevoflurane in 17 patients using support vector machine (SVM) with Shannon entropy and frequency features [33].…”