scholarly journals INVESTIGATION OF EEG SIGNAL LENGTH INFLUENCE ON ACCURACY OF ANESTHESIA LEVELS CLASSIFICATION

2019 ◽  
pp. 111-117
Author(s):  
Mokhammed A. Al-Ghaili ◽  
Alexander N. Kalinichenko ◽  
Mokhammed R. Qaid
Keyword(s):  
Discriminant Analysis ◽  
Surgical Procedure ◽  
Eeg Signal ◽  
Depth Of Anesthesia ◽  
Linear Discriminant ◽  
Analysis Algorithm ◽  
Acceptable Accuracy ◽  
Anesthetic Drugs ◽  
Challenging Tasks ◽  
Anesthesia Level

This paper considers one of the challenging tasks during surgical procedure, i.e. depth of anasthesia estimate. The purpose of this paper is to investigate the effect of the analyzed EEG signal fragment duration on the accuracy of anesthesia level estimate using the linear discriminant analysis algorithm and determining the EEG signal length, which yields acceptable accuracy of anesthesia level separation using these parameters.A new method for classifying EEG anesthesia levels is proposed. The possibility of classifying levels of anesthesia is demonstrated by means of sharing the EEG parameters under consideration (SE, BSR, SEF95, RBR). The method can be used in anesthesia monitors that are used to monitor the depth of anesthesia in order to select the appropriate dose of anesthetic drugs during operations, thus avoiding both cases of intraoperative arousal and excessively deep anesthesia.

Classification of Sleep Stages Using Multi-wavelet Time Frequency Entropy and LDA

2010 ◽  
Vol 49 (03) ◽  
pp. 230-237 ◽  
Author(s):  
K. Lweesy ◽  
N. Khasawneh ◽  
M. Fraiwan ◽  
H. Wenz ◽  
H. Dickhaus ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Discriminant Analysis ◽  
Sleep Stage ◽  
Continuous Wavelet ◽  
Eeg Signal ◽  
Linear Discriminant ◽  
Time Frequency ◽  
Mother Wavelets ◽  
Sleep Stage Scoring

Summary Background: The process of automatic sleep stage scoring consists of two major parts: feature extraction and classification. Features are normally extracted from the polysomno-graphic recordings, mainly electroencephalograph (EEG) signals. The EEG is considered a non-stationary signal which increases the complexity of the detection of different waves in it. Objectives: This work presents a new technique for automatic sleep stage scoring based on employing continuous wavelet transform (CWT) and linear discriminant analysis (LDA) using different mother wavelets to detect different waves embedded in the EEG signal. Methods: The use of different mother wave-lets increases the ability to detect waves in the EEG signal. The extracted features were formed based on CWT time frequency entropy using three mother wavelets, and the classification was performed using the linear discriminant analysis. Thirty-two data sets from the MIT-BIH database were used to evaluate the performance of the proposed method. Results: Features of a single EEG signal were extracted successfully based on the time frequency entropy using the continuous wavelet transform with three mother wavelets. The proposed method has shown to outperform the classification based on a CWT using a single mother wavelet. The accuracy was found to be 0.84, while the kappa coefficient was 0.78. Conclusions: This work has shown that wavelet time frequency entropy provides a powerful tool for feature extraction for the non-stationary EEG signal; the accuracy of the classification procedure improved when using multiple wavelets compared to the use of single wavelet time frequency entropy.

scholarly journals QUANTIFICATION OF DEPTH OF ANESTHESIA BY MEANS OF ADAPTIVE CALCULATION OF CORRELATION DIMENSION PARAMETERS

Fractals ◽  
2009 ◽  
Vol 17 (04) ◽  
pp. 473-483
Author(s):  
BEHZAD AHMADI ◽  
BAHAREH ZAGHARI ◽  
RASSOUL AMIRFATTAHI ◽  
MOJTABA MANSOURI
Keyword(s):  
Correlation Dimension ◽  
Single Channel ◽  
Drug Effects ◽  
Eeg Signal ◽  
Depth Of Anesthesia ◽  
Anesthetic Drugs ◽  
Eeg Data ◽  
Prediction Probability ◽  
Analysis Application ◽  
Electroencephalogram Eeg

This paper proposes an approach for quantifying Depth of Anesthesia (DOA) based on correlation dimension (D2) of electroencephalogram (EEG). The single-channel EEG data was captured in both ICU and operating room while different anesthetic drugs, including propofol and isoflurane, were used. Correlation dimension was computed using various optimized parameters in order to achieve the maximum sensitivity to anesthetic drug effects and to enable real time computation. For better analysis, application of adaptive segmentation on EEG signal for estimating DOA was evaluated and compared to fixed segmentation, too. Prediction probability (PK) was used as a measure of correlation between the predictors and BIS index to evaluate the proposed methods. Appropriate correlation between DOA and correlation dimension is achieved while choosing (D2) parameters adaptively in comparison to fixed parameters due to the nonstationary nature of EEG signal.

PSO-Based Uncorrelated Hybrid Discriminant Analysis Algorithm

2011 ◽  
Vol 109 ◽  
pp. 671-675 ◽  
Author(s):  
Xiao Ping Liu ◽  
Gui Yun Xu
Keyword(s):  
Discriminant Analysis ◽  
Optimization Problem ◽  
Optimal Solution ◽  
Principal Component ◽  
Small Sample ◽  
Model Parameters ◽  
Orthogonal Condition ◽  
Linear Discriminant ◽  
Analysis Algorithm ◽  
Discriminant Information

Hybrid discriminant analysis (HDA) can overcome small sample problems and outperform PCA and LDA by unifying principal component analysis (PCA) and linear discriminant analysis (LDA) in a single framework. However, the existing HDA algorithm can’t extract more discriminant information from dataset, and model parameters are difficult to select. To deal with the above problems, a particle swarm optimal (PSO)-based uncorrelated hybrid discriminant analysis algorithm is presented. The conjugate orthogonal condition is added to optimization problem of HDA, PSO is explored to select optimal HDA parameters and the optimal solution can be achieved by solving eigenvalue problem. Simulation demonstrates merits of the proposed algorithm.

Sign in / Sign up

Export Citation Format

Share Document