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.

scholarly journals EEG Signal Discrimination using Non-linear Dynamics in the EMD Domain S. M. Shafiul Alam,S. M. Shafiul Alam,Aurangozeb, and Syed TarekShahriar Abstract—An EMD-chaos based approach is proposed todiscriminate EEG signals corresponding to healthy persons,and epileptic patients during seizure-free intervals and seizureattacks. An electroencephalogram (EEG) is first empiricallydecomposed to intrinsic mode functions (IMFs). The nonlineardynamics of these IMFs are quantified in terms of the largestLyapunov exponent (LLE) and correlation dimension (CD).This chaotic analysis in EMD domain is applied to a large groupof EEG signals corresponding to healthy persons as well asepileptic patients (both with and without seizure attacks). It isshown that the values of the obtained LLE and CD exhibitfeatures by which EEG for seizure attacks can be clearlydistinguished from other EEG signals in the EMD domain.Thus, the proposed approach may aid researchers in developingeffective techniques to predict seizure activities. Index Terms—Electroencephalogram (EEG), empiricalmode decomposition (EMD), largest Lyapunov exponent (LLE),correlation dimension (CD), epileptic seizures. The Authors are with the Electrical and Electronic EngineeringDepartment, Bangladesh University of Engineering and Technology,Dhaka-1000, Bangladesh (e-mail: [email protected]) [PDF] Cite: S. M. Shafiul Alam,S. M. Shafiul Alam,Aurangozeb, and Syed Tarek Shahriar, "EEG Signal Discrimination using Non-linear Dynamics in the EMD Domain," International Journal of Computer and Electrical Engineering vol. 4, no. 3, pp. 326-330, 2012. PREVIOUS PAPER Perception of Emotions Using Constructive Learningthrough Speech NEXT PAPER Physical Layer Impairments Aware OVPN Connection Selection Mechanisms Copyright © 2008-2013. International Association of Computer Science and Information Technology Press (IACSIT Press)

2012 ◽  
pp. 326-330 ◽  
Author(s):  
S. M. Shafiul Alam ◽  
S. M. Shafiul Alam ◽  
Aurangozeb ◽  
Syed TarekShahriar
Keyword(s):  
Correlation Dimension ◽  
Largest Lyapunov Exponent ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Linear Dynamics ◽  
Signal Discrimination ◽  
Non Linear ◽  
Electroencephalogram Eeg ◽  
Seizure Attacks ◽  
Healthy Persons

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.

scholarly journals Development of an EEG signal analysis application through a convolution of a complex Morlet wavelet: preliminary results

2019 ◽  
Vol 8 (2) ◽  
pp. B1-1-B1-19
Author(s):  
José Humberto Trueba Perdomo ◽  
◽  
Ignacio Herrera Aguilar ◽  
Francesca Gasparini ◽  
◽  
...  
Keyword(s):  
Morlet Wavelet ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Eyes Closed ◽  
Eyes Open ◽  
Analysis Application ◽  
The Subject ◽  
Complex Morlet Wavelet ◽  
Electroencephalogram Eeg ◽  
Eeg Signal Analysis

This paper presents a new application for analyzing electroencephalogram (EEG) signals. The signals are pre-filtered through MATLAB's EEGLAB tool. The created application performs a convolution between the original EEG signal and a complex Morlet wavelet. As a final result, the application shows the signal power value and a spectrogram of the convoluted signal. Moreover, the created application compares different EEG channels at the same time, in a fast and straightforward way, through a time and frequency analysis. Finally, the effectiveness of the created application was demonstrated by performing an analysis of the alpha signals of healthy subjects, one signal created by the subject with eyes closed and the other, with which it was compared, was created by the same subject with eyes open. This also served to demonstrate that the power of the alpha band of the closed-eyed signal is higher than the power of the open-eyed signal.

scholarly journals Denoising of EEG signal based on word imagination using ICA for artifact and noise removal on unspoken speech

2021 ◽  
Vol 22 (1) ◽  
pp. 83
Author(s):  
Efy Yosrita ◽  
Rosida Nur Aziza ◽  
Rahma Farah Ningrum ◽  
Givary Muhammad
Keyword(s):  
Noise Removal ◽  
Physical Activities ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Signal Ratio ◽  
Eye Blink ◽  
Eeg Data ◽  
Word Classification ◽  
Electroencephalogram Eeg ◽  
Electrical Activities

<span>The purpose of this research is to observe the effectiveness of independent component analysis (ICA) method for denoising raw EEG signals based on word imagination, which will be used for word classification on unspoken speech. The electroencephalogram (EEG) signals are signals that represent the electrical activities of the human brain when someone is doing activities, such as sleeping, thinking or other physical activities. EEG data based on the word imagination used for the research is accompanied by artifacts, that come from muscle movements, heartbeat, eye blink, voltage and so on. In previous studies, the ICA method has been widely used and effective for relieving physiological artifacts. Artifact to signal ratio (ASR) is used to measure the effectiveness of ICA in this study. If the ratio is getting larger, the ICA method is considered effective for clearing noise and artifacts from the EEG data. Based on the experiment, the obtained ASR values from 11 subjects on 14 electrodes amounted are within the range of 0,910 to 1,080. Thus, it can be concluded that ICA is effective for removing artifacts from EEG signals based on word imagination.</span>

scholarly journals Maximum Marginal Approach on EEG Signal Preprocessing for Emotion Detection

2020 ◽  
Vol 10 (21) ◽  
pp. 7677
Author(s):  
Gen Li ◽  
Jason J. Jung
Keyword(s):  
Feature Selection ◽  
Eeg Signal ◽  
Emotion Detection ◽  
Eeg Signals ◽  
Training Time ◽  
Eeg Data ◽  
Signal Preprocessing ◽  
The Difference ◽  
Electroencephalogram Eeg ◽  
Signal Similarity

Emotion detection is an important research issue in electroencephalogram (EEG). Signal preprocessing and feature selection are parts of feature engineering, which determines the performance of emotion detection and reduces the training time of the deep learning models. To select the efficient features for emotion detection, we propose a maximum marginal approach on EEG signal preprocessing. The approach selects the least similar segments between two EEG signals as features that can represent the difference between EEG signals caused by emotions. The method defines a signal similarity described as the distance between two EEG signals to find the features. The frequency domain of EEG is calculated by using a wavelet transform that exploits a wavelet to calculate EEG components in a different frequency. We have conducted experiments by using the selected feature from real EEG data recorded from 10 college students. The experimental results show that the proposed approach performs better than other feature selection methods by 17.9% on average in terms of accuracy. The maximum marginal approach-based models achieve better performance than the models without feature selection by 21% on average in terms of accuracy.

scholarly journals Removal of Artifacts from EEG Signals: A Review

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 987 ◽  
Author(s):  
Xiao Jiang ◽  
Gui-Bin Bian ◽  
Zean Tian
Keyword(s):  
Comparative Analysis ◽  
Open Problem ◽  
State Of The Art ◽  
Brain Activity ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Artifact Removal ◽  
Eeg Data ◽  
And Behavior ◽  
Electroencephalogram Eeg

Electroencephalogram (EEG) plays an important role in identifying brain activity and behavior. However, the recorded electrical activity always be contaminated with artifacts and then affect the analysis of EEG signal. Hence, it is essential to develop methods to effectively detect and extract the clean EEG data during encephalogram recordings. Several methods have been proposed to remove artifacts, but the research on artifact removal continues to be an open problem. This paper tends to review the current artifact removal of various contaminations. We first discuss the characteristics of EEG data and the types of different artifacts. Then, a general overview of the state-of-the-art methods and their detail analysis are presented. Lastly, a comparative analysis is provided for choosing a suitable methods according to particular application.

Analysis of the Multi-Channel Epileptiform EEG Using the Markov Chains Formalism

1989 ◽  
Vol 28 (03) ◽  
pp. 160-167 ◽  
Author(s):  
P. Penczek ◽  
W. Grochulski
Keyword(s):  
Dynamic Behaviour ◽  
Joint Probability ◽  
Drug Effects ◽  
Anticonvulsant Drug ◽  
Channel System ◽  
Markov Network ◽  
Eeg Data ◽  
Multi Level ◽  
Joint Probability Functions ◽  
Dependent State

Abstract:A multi-level scheme of syntactic reduction of the epileptiform EEG data is briefly discussed and the possibilities it opens up in describing the dynamic behaviour of a multi-channel system are indicated. A new algorithm for the inference of a Markov network from finite sets of sample symbol strings is introduced. Formulae for the time-dependent state occupation probabilities, as well as joint probability functions for pairs of channels, are given. An exemplary case of analysis in these terms, taken from an investigation of anticonvulsant drug effects on EEG seizure patterns, is presented.

scholarly journals Eye-blink artifact removal from single channel EEG with k-means and SSA

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ajay Kumar Maddirala ◽  
Kalyana C Veluvolu
Keyword(s):  
Machine Learning ◽  
Single Channel ◽  
Learning Algorithm ◽  
Singular Spectrum Analysis ◽  
Machine Learning Algorithm ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Unsupervised Machine Learning ◽  
Eye Blink ◽  
Blink Artifact

AbstractIn recent years, the usage of portable electroencephalogram (EEG) devices are becoming popular for both clinical and non-clinical applications. In order to provide more comfort to the subject and measure the EEG signals for several hours, these devices usually consists of fewer EEG channels or even with a single EEG channel. However, electrooculogram (EOG) signal, also known as eye-blink artifact, produced by involuntary movement of eyelids, always contaminate the EEG signals. Very few techniques are available to remove these artifacts from single channel EEG and most of these techniques modify the uncontaminated regions of the EEG signal. In this paper, we developed a new framework that combines unsupervised machine learning algorithm (k-means) and singular spectrum analysis (SSA) technique to remove eye blink artifact without modifying actual EEG signal. The novelty of the work lies in the extraction of the eye-blink artifact based on the time-domain features of the EEG signal and the unsupervised machine learning algorithm. The extracted eye-blink artifact is further processed by the SSA method and finally subtracted from the contaminated single channel EEG signal to obtain the corrected EEG signal. Results with synthetic and real EEG signals demonstrate the superiority of the proposed method over the existing methods. Moreover, the frequency based measures [the power spectrum ratio ($$\Gamma $$ Γ ) and the mean absolute error (MAE)] also show that the proposed method does not modify the uncontaminated regions of the EEG signal while removing the eye-blink artifact.

AGE-BASED VARIATIONS OF FRACTAL STRUCTURE OF EEG SIGNAL IN PATIENTS WITH EPILEPSY

Fractals ◽  
2018 ◽  
Vol 26 (04) ◽  
pp. 1850051 ◽  
Author(s):  
HAMIDREZA NAMAZI ◽  
SAJAD JAFARI
Keyword(s):  
Fractal Dimension ◽  
Brain Research ◽  
Age Groups ◽  
Eeg Signal ◽  
Brain Disorders ◽  
Eeg Signals ◽  
Important Challenge ◽  
Patients With Epilepsy ◽  
Process Complexity ◽  
Electroencephalogram Eeg

It is known that aging affects neuroplasticity. On the other hand, neuroplasticity can be studied by analyzing the electroencephalogram (EEG) signal. An important challenge in brain research is to study the variations of neuroplasticity during aging for patients suffering from epilepsy. This study investigates the variations of the complexity of EEG signal during aging for patients with epilepsy. For this purpose, we employed fractal dimension as an indicator of process complexity. We classified the subjects in different age groups and computed the fractal dimension of their EEG signals. Our investigations showed that as patients get older, their EEG signal will be more complex. The method of investigation that has been used in this study can be further employed to study the variations of EEG signal in case of other brain disorders during aging.

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