Shalbaf, Ahmad
Assistant of Professor, Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
[ 1 ] - مانیتور عمق بیهوشی توسط شاخص‌های همبستگی غیرخطی
زمینه و هدف: مانتیور عمق بیهوشی نقش مهمی برای متخصصین بیهوشی جهت جلوگیری از اثرات نامطلوب از جمله بیداری حین عمل، ریکاوری طولانی مدت و افزایش خطر عوارض پس از عمل ایفا میکند. سیستم عصبی مرکزی هدف اصلی داروهای بیهوشی میباشد. ازاینرو پردازش سیگنال EEG در طول بیهوشی به منظور مانیتور عمق بیهوشی مناسب میباشد. ارزیابی همبستگی بین سیگنالهای نوروفیزیولوژیکی برای مطالعه و تفسیر ویژگیهای این سیگنالها ابزار …
[ 2 ] - Assessment of Anesthesia Depth Using Effective Brain Connectivity Based on Transfer Entropy on EEG Signal
Introduction: Ensuring an adequate Depth of Anesthesia (DOA) during surgery is essential for anesthesiologists. Since the effect of anesthetic drugs is on the central nervous system, brain signals such as Electroencephalogram (EEG) can be used for DOA estimation. Anesthesia can interfere among brain regions, so the relationship among different areas can be a key factor in the anesthetic process. …
[ 3 ] - Mental Arithmetic Task Recognition Using Effective Connectivity and Hierarchical Feature Selection From EEG Signals
Introduction: Mental arithmetic analysis based on Electroencephalogram (EEG) signals can help understand disorders, such as attention-deficit hyperactivity, dyscalculia, or autism spectrum disorder where the difficulty in learning or understanding the arithmetic exists. Most mental arithmetic recognition systems rely on features of a single channel of EEG; however, the relationships between EEG c…
[ 4 ] - Detection of schizophrenia patients using convolutional neural networks from brain effective connectivity maps of electroencephalogram signals
Background: Schizophrenia is a mental disorder that severely affects the perception and relations of individuals. Nowadays, this disease is diagnosed by psychiatrists based on psychiatric tests, which is highly dependent on their experience and knowledge. This study aimed to design a fully automated framework for the diagnosis of schizophrenia from electroencephalogram signals using advanced deep…
[ 5 ] - Identification of mild cognitive impairment disease using brain functional connectivity and graph analysis in fMRI data
Background: Early diagnosis of patients in the early stages of Alzheimer's, known as mild cognitive impairment, is of great importance in the treatment of this disease. If a patient can be diagnosed at this stage, it is possible to treat or delay Alzheimer's disease. Resting-state functional magnetic resonance imaging (fMRI) is very common in the process of diagnosing Alzheimer's disease. In this…
[ 6 ] - Automatic classification of Non-alcoholic fatty liver using texture features from ultrasound images
Background: Accurate and early detection of non-alcoholic fatty liver, which is a major cause of chronic diseases is very important and is vital to prevent the complications associated with this disease. Ultrasound of the liver is the most common and widely performed method of diagnosing fatty liver. However, due to the low quality of ultrasound images, the need for an automatic and intelligent c…
[ 7 ] - Compensation of brain shift during surgery using non-rigid registration of MR and ultrasound images
Background: Surgery and accurate removal of the brain tumor in the operating room and after opening the scalp is one of the major challenges for neurosurgeons due to the removal of skull pressure and displacement and deformation of the brain tissue. This displacement of the brain changes the location of the tumor relative to the MR image taken preoperatively. Methods: This study, which is done fr…
[ 8 ] - Depth of anesthesia estimation based on EEG signal using brain effective connectivity between frontal and temporal regions
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. A…
[ 9 ] - A Hybrid EEG-based Emotion Recognition Approach Using Wavelet Convolutional Neural Networks and Support Vector Machine
Introduction: Nowadays, deep learning and convolutional neural networks (CNNs) have become widespread tools in many biomedical engineering studies. CNN is an end-to-end tool, which makes the processing procedure integrated, but in some situations, this processing tool requires to be fused with machine learning methods to be more accurate. Methods: In this paper, a hybrid approach based on deep fe…
[ 10 ] - Classification of Right/Left Hand Motor Imagery by Effective Connectivity Based on Transfer Entropy in Electroencephalogram Signal
Introduction: The right and left-hand motor imagery (MI) analysis based on the electroencephalogram (EEG) signal can directly link the central nervous system to a computer or a device. This study aims to identify a set of robust and nonlinear effective brain connectivity features quantified by transfer entropy (TE) to characterize the relationship between brain regions from EEG signals and create…
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