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Showing 4 results for Electroencephalography
Drug withdrawal in patients with epilepsy: Prognostic value of the EEG
Najafi Mr, Tamizi Far B,
Volume 59, Issue 5 (9-2001)
Abstract
The use of Antiepileptic drugs (AEDs) in children may be associated with adverse effects especially behavioral and cognitive and teratogenic potential effects. The main propose of this study was to find an answer to the question of which factors in EEG of patients before AED withdrawal could have prognostic role in our decision. We studied 106 children whom their medication had been withdrawn 2 years after their last seizure. Before starting of this, an EEG was recorded and interpreted by an expert neurologist. Many variables such as background activity, focal spike, generalized sharp and spik waves, focal slowing, in comparison with the EEG of patient at the time of diagnosis, and also final result of the trace interpret also examined. Follow-up visits were scheduled every 3 months at least for one year. If seizure relapsed, AEDs was resumed and follow up terminated. The overall probability of remaining seizure free was analyzed as a function of time by Kaplan-Meier survical analysis. Prognostic factors affecting seizure relapse were evaluated by using the log-rank test. The overall probability of seizure recurrences was 24.8 percent (95 percent C.I, 22.5 to 28.5) at 12 months. EEG comparisons with previous times were a significant factor for prediction of relapses. Relative risk of this factor was about 1.98 (95 percent C.I, 1.01 to 3.91) (P<0.05). We found that EEG interpretation at the time of diagnosis was not a significant factor but if it divided by sex, there is a significant difference in gender (P=0.06). According to our study the rate of AED withdrawal in children is small. The benefits of continuing AED therapy must be weighted against the risk of potential adverse effects. EEG comparison with previous traces could be evaluated as a prognostic factor before AED withdrawal in children.
Depth of anesthesia estimation based on EEG signal using brain effective connectivity between frontal and temporal regions
Sanaz Jafari, Ahmad Shalbaf, Jamie Sleigh,
Volume 78, Issue 6 (9-2020)
Abstract
Background: Ensuring adequate depth of anesthesia during surgery is essential for anesthesiologists to prevent the occurrence of unwanted alertness during surgery or failure to return to consciousness. Since the purpose of using anesthetics is to affect the central nervous system, brain signal processing such as electroencephalography (EEG) can be used to predict different levels of anesthesia. Anesthesia disrupts the interaction between different regions of the brain, so brain connectivity between different areas can be a key factor in the anesthesia process. This study aims to determine the depth of anesthesia based on the EEG signal using the effective brain connectivity between frontal and temporal regions.
Methods: This study, which is done from April to December 2018 in Tehran, used EEG signals recorded from eight patients undergoing Propofol anesthesia at Waikato Hospital of New Zealand. In this study, effective brain connectivity in the frontal and temporal regions have been extracted by using various Granger causality methods, including directional transfer function, normalized directional transfer function, partial coherence, partial oriented coherence, and imaginary coherence. The extraction of effective connectivity indices in three modes (awake, anesthesia and recovery) was calculated using MATLAB software. The perceptron neural network is then used to automatically classify the anesthetic phases (Awake, Anesthesia, and recovery).
Results: The results show that the directional transfer function method has a high correlation coefficient with BIS in all cases. Also, the directional transfer function index due to faster response on the drug, low variability, and better ability to track the effect of Propofol works better than the BIS index as a commercial anesthetic depth monitor in clinical application. Also, when using an artificial neural network, our index has a better ability to automatically detect three anesthesia than the BIS index.
Conclusion: The directional transfer function between the pair of EEG signals in the frontal and temporal regions can effectively track the effect of Propofol and estimate the patient's anesthesia well compared to other effective connectivity indexes. It also works better than the BIS index in clinical centers.

Deep learning approach for motor imagery EEG signal classification
Faezeh Moghadas, Zahra Amini, Rahele Kafieh,
Volume 80, Issue 10 (1-2023)
Abstract
Background: Brain-computer interface systems provide the possibility of communicating with the outside world without using physiological mediators for people with physical disabilities through brain signals. A popular type of BCIs is the motor imagery-based systems and one of the most important parts in the design of these systems is the classification of brain signals into different motor imagery classes in order to transform them into control commands. In this paper, a new method of brain signal classifying based on deep learning methods is presented.
Methods: This cross-sectional study was conducted at Isfahan University of Medical Sciences, School of Advanced Technologies in Medicine, from February 2020 to June 2022. In the pre-processing block, segmentation of brain signals, selection of suitable channels and filtering by Butterworth filter have been done; then data has transformed to the time-frequency domain by three different kinds of mother wavelets including Cmor, Mexicanhat, and Cgaus. In the classification step, two types of convolutional neural networks (one-dimensional and two-dimensional) were applied whereas each one of them was utilized in two different architectures. Finally, the performance of the networks has been investigated by each one of these three types of input data.
Results: Three channels were selected as the best ones for nine subjects. To separate 8-30 Hz, a 5th degree Butterworth filter was used. After finding the optimal parameters in the proposed networks, wavelet transform with Cgauss mother wavelet has the highest percentage in the both proposed architectures. Two-dimensional convolutional neural network has higher convergence speed, higher accuracy and more complexity of calculations. In terms of accuracy, precision, sensitivity and F1-score, two-dimensional convolutional neural network has performed better than one-dimensional convolutional neural network. The accuracy of 92.53%, which is obtained from the second architecture, as the best result, is reported.
Conclusion: The results obtained from the proposed network indicate that suitable, and well-designed deep learning networks can be utilized as an accurate tool for data classification in application of motion perception.

Mohsen Sadat Shahabi , Ahmad Shalbaf ,
Volume 82, Issue 2 (5-2024)
Abstract
Background: Major Depressive Disorder (MDD) is one of the most prevalent and disabling mental disorders in the world. Due to the life quality decline caused by this disease and its growing nature, timely detection and treatment is of paramount importance. In the present study Electroencephalogram (EEG) signal utilized for the precise detection of MDD using Artificial Intelligence (AI) Methods.
Methods: In this analytic study, which is done in Shahid Beheshti University of medical Sciences in 2023, fifty eight subjects were investigated using an experienced psychiatrist that 30 subjects diagnosed as MDD and 28 determined to be healthy. Nineteen channels EEG signals in resting state with eyes closed situation acquired for five minutes from all of the participants including 36 men and 22 women with the average age of 39.3 years. The EEG signals were preprocessed to remove contaminating signals from brain-originated signals. The EEGLAB package in MATLAB utilized to re-reference channels to the average reference, apply a band-pass filter between 1 and 40 Hz and to remove non-brain components of the signal using Independent Component Analysis (ICA). The cleaned data segmented to the three seconds windows with 50 percent overlapping. These segments were used as the input to the AI models. Deep Learning (DL) models utilized in the present study were EEGNet, ShallowConvNet and DeepConvNet which were developed based on the deep convolutional models for the classification of healthy and MDD brain signals. The main difference between these models laid in the number of specific convolutional layers and the model complexity.
Results: MDD and Healthy signals classification has been done using EEGNet, ShallowConvNet and DeepConvNet models and accuracy of 92.3%, 83.2% and 92.2% were achieved, respectively. Also EEGNet acquired the highest sensitivity of 98.9% and specificity of 79.1%.
Conclusion: The detection of MDD patients using EEG signals with high accuracy and generalizability is possible and proposed AI models can be utilized in the clinical settings as assistant tools.

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