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Comparison of the baseline, waking and sleep EEGs after sleep deprivation in patients with sleep seizure
Motamedi M, Yordkhani F, Shirali A, Gheini Mr,
Volume 69, Issue 8 (11-2011)
Abstract

Normal 0 false false false EN-US X-NONE AR-SA MicrosoftInternetExplorer4 Background: Sleep and sleep deprivation plays a major role in EEG abnormalities and also idiopathic and symptomatic seizures. The aims of this study were to compare baseline EEG findings with waking and sleep EEGs after sleep deprivation in patients with sleep seizure.
Methods : In this cross-sectional study, 33 patients with sleep seizure attending the Neurology Clinic of Sina Hospital in Tehran, Iran, during year 2009 were enrolled. After a baseline EEG, patients were asked to remain awake for 24 hours before taking a waking and a sleep EEG. Finally, the baseline EEGs were compared with findings from waking and sleep EEGs after sleep deprivation.
Results : From 33 patients with sleep seizure, sixteen (48.5%) patients were female and seventeen (51.5%) were male. Patients aged from 7 to 49 years and the mean age of the participants was 26.83 (SD=10.69) years. Twenty patients had no family histories of seizure contrary to 13 patients with a positive history for the disease. There was statistically significant differences between the baseline and waking EEGs after sleep deprivation (P=0.042) as there was between baseline and sleep EEGs (P=0.041). Moreover, there was significant differences between waking and sleep EEGs after sleep deprivation (P=0.048).
Conclusion: This study demonstrated the effects of sleep deprivation on EEG findings in patients with sleep seizure. In patients with sleep seizure, waking and sleep EEGs could be better demonstrated after sleep deprivation than routine waking EEGs. According to the results of this study, waking EEGs taken after a period of sleep deprivation is superior to sleep EEGs after the deprivation.


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.

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