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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.

Monitoring depth of anesthesia (DOA) via vital signs is a major ongoing challenge for anesthetists. A number of electroencephalogram (EEG)-based monitors such as the Bispectral (BIS) index have been proposed. However, anesthesia is related to central and autonomic nervous system functions whereas the EEG signal originates only from the central nervous system. This paper proposes an automated DOA detection system which consists of three steps. Initially, we introduce multiscale modified permutation entropy index which is robust in the characterization of the burst suppression pattern and combine multiscale information. This index quantifies the amount of complexity in EEG data and is computationally efficient, conceptually simple and artifact resistant. Then, autonomic nervous system activity is quantified with heart rate and mean arterial pressure which are easily acquired using routine monitoring machine. Finally, the extracted features are used as input to a linear discriminate analyzer (LDA). The method is validated with data obtained from 25 patients during the cardiac surgery requiring cardiopulmonary bypass. The experimental results indicate that an overall accuracy of 89.4 % can be obtained using combination of EEG measure and hemodynamic variables, together with LDA to classify the vital sign into awake, light, surgical and deep anesthetised states. The results demonstrate that the proposed method can estimate DOA more effectively than the commercial BIS index with a stronger artifact-resistance.

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Distinguishing between awake and anesthetized states is one of the important problems in surgery. Vital signals contain valuable information that can be used in prediction of different levels of anesthesia. Some monitors based on electroencephalogram (EEG) such as the Bispectral (BIS) index have been proposed in recent years. This study proposes a new method for characterizing between awake and anesthetized states. We validated our method by obtaining data from 25 patients during the cardiac surgery that requires cardiopulmonary bypass. At first, some linear and non-linear features are extracted from EEG signals. Then a method called "LLE"(Locally Linear Embedding) is used to map high-dimensional features in a three-dimensional output space. Finally, low dimensional data are used as an input to a quadratic discriminant analyzer (QDA). The experimental results indicate that an overall accuracy of 88.4 % can be obtained using this method for classifying the EEG signal into conscious and unconscious states for all patients. Considering the reliability of this method, we can develop a new EEG monitoring system that could assist the anesthesiologists to estimate the depth of anesthesia accurately.