scholarly journals Consciousness and Depth of Anesthesia Assessment Based on Bayesian Analysis of EEG Signals

2013 ◽  
Vol 60 (6) ◽  
pp. 1488-1498 ◽  
Author(s):  
Tai Nguyen-Ky ◽  
Peng Wen ◽  
Yan Li
Keyword(s):  
Bayesian Analysis ◽  
Depth Of Anesthesia ◽  
Eeg Signals

scholarly journals EEG Signals Analysis Using Multiscale Entropy for Depth of Anesthesia Monitoring during Surgery through Artificial Neural Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Quan Liu ◽  
Yi-Feng Chen ◽  
Shou-Zen Fan ◽  
Maysam F. Abbod ◽  
Jiann-Shing Shieh
Keyword(s):  
Time Scales ◽  
Multiscale Entropy ◽  
Expert Assessment ◽  
Data Series ◽  
Depth Of Anesthesia ◽  
Eeg Signals ◽  
New Approach ◽  
Before And After ◽  
Artificial Neural ◽  
Different Time Scales

In order to build a reliable index to monitor the depth of anesthesia (DOA), many algorithms have been proposed in recent years, one of which is sample entropy (SampEn), a commonly used and important tool to measure the regularity of data series. However, SampEn only estimates the complexity of signals on one time scale. In this study, a new approach is introduced using multiscale entropy (MSE) considering the structure information over different time scales. The entropy values over different time scales calculated through MSE are applied as the input data to train an artificial neural network (ANN) model using bispectral index (BIS) or expert assessment of conscious level (EACL) as the target. To test the performance of the new index’s sensitivity to artifacts, we compared the results before and after filtration by multivariate empirical mode decomposition (MEMD). The new approach via ANN is utilized in real EEG signals collected from 26 patients before and after filtering by MEMD, respectively; the results show that is a higher correlation between index from the proposed approach and the gold standard compared with SampEn. Moreover, the proposed approach is more structurally robust to noise and artifacts which indicates that it can be used for monitoring the DOA more accurately.

scholarly journals Adaptive Computation of Multiscale Entropy and Its Application in EEG Signals for Monitoring Depth of Anesthesia During Surgery

Entropy ◽  
2012 ◽  
Vol 14 (6) ◽  
pp. 978-992 ◽  
Author(s):  
Quan Liu ◽  
Qin Wei ◽  
Shou-Zen Fan ◽  
Cheng-Wei Lu ◽  
Tzu-Yu Lin ◽  
...  
Keyword(s):  
Multiscale Entropy ◽  
Depth Of Anesthesia ◽  
Eeg Signals ◽  
Adaptive Computation

Application of Back Propagation Neural Network and Information Entropy in Deep Detection of Anesthesia

2020 ◽  
Vol 10 (8) ◽  
pp. 1875-1879
Author(s):  
Yujuan Zhou ◽  
Lei Wang ◽  
Jintai Jia ◽  
Gema Monasterio
Keyword(s):  
Neural Network ◽  
General Anesthesia ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Sample Entropy ◽  
Depth Of Anesthesia ◽  
Eeg Signals ◽  
Entropy Index ◽  
Wavelet Entropy ◽  
Anesthesia Depth

In order to study the monitoring of anesthesia depth during general anesthesia, the EEG (electroencephalogram) signals of 30 patients with laparoscopic general anesthesia were taken as the research objects. The approximate entropy, sample entropy, ranking entropy, and wavelet entropy of EEG signals under different anesthesia conditions were compared by BP (Back Propagation) neural network. The results showed that with the deepening of anesthesia, the four kinds of information entropies of EEG signal showed a downward trend. Among them, the sample entropy algorithm, ranking entropy algorithm, and wavelet entropy algorithm had a higher accuracy in the classification of anesthesia depth. Whereas, the network model established by combining sample entropy index and wavelet entropy index had the highest accuracy in judging anesthesia depth, which was 99.98%. To sum up, the method presented to monitor the depth of anesthesia by combining the characteristics of various EEG signals provides a new reference for the monitoring of the depth of anesthesia.

scholarly journals ESTIMATION OF THE DEPTH OF ANESTHESIA BY ELECTROENCEPHALOGRAM USING NEURAL NETWORKS

2019 ◽  
Vol 22 (3) ◽  
pp. 106-112
Author(s):  
Mokhammed A. Al-Ghaili ◽  
Alexander N. Kalinichenko
Keyword(s):  
Neural Networks ◽  
The State ◽  
Depth Of Anesthesia ◽  
Eeg Signals ◽  
Spectral Edge Frequency ◽  
Complex Processes ◽  
Spectral Components ◽  
Waking State ◽  
The Brain ◽  
Anesthesia Depth

Introduction. Monitoring of the depth of anesthesia during surgery is a complex task. Since electroencephalogram (EEG) signals contain valuable information about processes in the brain, EEG analysis is considered to be one of the most useful methods for study and assessment of the depth of anesthesia in clinical applications. Anesthetics affect the frequency composition of the EEG. EEG of awake persons, as a rule, contains mixed alpha and beta rhythms. Changes in the EEG, caused by the transition from the waking state to the state of deep anesthesia, manifest as a shift of the spectral components of the signal to the lower part of the frequency range. Anesthetics cause a whole range of neurophysiological changes, which cannot be correctly assessed with just one indicator. Objective. In order to describe complex processes during the transition from the waking state to the state of deep anesthesia adequately, it is required to propose a method for assessing the depth of anesthesia, using a comprehensive set of parameters reflecting changes in the EEG signal. The article presents the results of study the possibility of building a regression model based on artificial neural networks (ANN) to determine levels of anesthesia using a set of parameters calculated by EEG. Materials and methods. The authors of the article propose the method for assessing the level of anesthesia, based on the use of neural networks, which input parameters are time and frequency EEG parameters, namely: spectral entropy (SE); burst-suppression ratio (BSR); spectral edge frequency (SEF95) and log power ratio of the spectrum (RBR) for three pairs of frequency ranges. Results. The optimal parameters of ANN were determined, at which the highest level of regression is achieved between the calculated and the verified values of the anesthesia depth indices. Conclusion. In order to create a practical version of the algorithm, it is necessary to investigate further the noise stability of the proposed method and develop a set of algorithmic solutions, which ensure a reliable operation of the algorithm in the presence of noise.

scholarly journals Time-Frequency Analysis of EEG Signals and GLCM Features for Depth of Anesthesia Monitoring

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Seyed Mortaza Mousavi ◽  
Akbar Asgharzadeh-Bonab ◽  
Ramin Ranjbarzadeh
Keyword(s):  
Frequency Analysis ◽  
Data Augmentation ◽  
Distance Measure ◽  
Correlation Energy ◽  
Confusion Matrix ◽  
Depth Of Anesthesia ◽  
Eeg Signals ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Knn Classifier

One of the important tasks in the operating room is monitoring the depth of anesthesia (DoA) during surgery, and noninvasive techniques are very popular. Hence, we propose a new scheme for DoA monitoring considering the time-frequency analysis of electroencephalography (EEG) signals and GLCM features extracted from them. To this end, at first, the time-frequency map (TFM) of each channel of each EEG is computed by smoothed pseudo-Wigner–Ville distribution (SPWVD), where the EEG signal used in this paper is recorded in 15 channels. After that, we consider the gray-level co-occurrence matrix (GLCM) to obtain the content of TFM, and after that, four features such as homogeneity, correlation, energy, and contrast are obtained for each GLCM. Finally, after the selection of efficient features using the minimum redundancy maximum relevance (MRMR) method, the K-nearest neighbor (KNN) classifier is utilized to determine the DoA. Here, we consider the three states, namely, deep hypnotic, surgical anesthesia, and sedation and awake states according to bispectral index (BIS), and each EEG epoch is classified to these states. We also employ data augmentation to enhance the training phase and increase accuracy. We obtain the accuracy and confusion matrix of the proposed method. We also analyze the effects of a number of gray levels of GLCM, distance measure in KNN classifier, and parameters of data augmentation on the performance of the proposed method. Results indicate the efficiency of the proposed method to determine the DoA during surgery.

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