Seyyed Abed Hosseini
Research Center of Biomedical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran.
[ 1 ] - A Hybrid Approach Based on Higher Order Spectra for Clinical Recognition of Seizure and Epilepsy Using Brain Activity
Introduction: This paper proposes a reliable and efficient technique to recognize different epilepsy states, including healthy, interictal, and ictal states, using Electroencephalogram (EEG) signals. Methods: The proposed approach consists of pre-processing, feature extraction by higher order spectra, feature normalization, feature selection by genetic algorithm and ranking method, and classif...
[ 2 ] - استخراج دینامیک سیگنال مغزی در حالت های مختلف بیهوشی به کمک تحلیل طرح پوآنکاره
Aims and background: Poincare plot and its length and width are known as a criterion for short-term variations of electroencephalogram signals )EEGs(. This study evaluates the effect of time delay on changes in the width of the Poincare plot in brain signal during different anesthesia states. Materials and Methods: Poincare plots are drawn with one to six delay in three sets, including awake s...
[ 3 ] - ارائه یک روش برچسب گذاری سیگنالهای مغزی بهمنظور طبقهبندی حالتهای مختلف بیهوشی
Aims and background: This study develops a computational framework for the classification of different anesthesia states, including awake, moderate anesthesia, and general anesthesia, using electroencephalography (EEG) signals and peripheral parameters. Materials and Methods: The proposed method proposes ...
[ 4 ] - Optimizing Teleportation Cost in Multi-Partition Distributed Quantum Circuits
There are many obstacles in quantum circuits implementation with large scales, so distributed quantum systems are appropriate solution for these quantum circuits. Therefore, reducing the number of quantum teleportation leads to improve the cost of implementing a quantum circuit. The minimum number of teleportations can be considered as a measure of the efficiency of distributed quantum systems....
[ 5 ] - Outlier Detection Using Extreme Learning Machines Based on Quantum Fuzzy C-Means
One of the most important concerns of a data miner is always to have accurate and error-free data. Data that does not contain human errors and whose records are full and contain correct data. In this paper, a new learning model based on an extreme learning machine neural network is proposed for outlier detection. The function of neural networks depends on various parameters such as the structur...
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