A Survey on Comparison Analysis Between EEG Signal and MRI for Brain Stroke Detection

2018 ◽  
pp. 377-382 ◽  
Author(s):  
Snehashish Bhattacharjee ◽  
Sujata Ghatak ◽  
Soumi Dutta ◽  
Biswajoy Chatterjee ◽  
Mousumi Gupta
Keyword(s):  
Eeg Signal ◽  
Comparison Analysis ◽  
Brain Stroke ◽  
Brain Stroke Detection

scholarly journals EARLY BRAIN STROKE DETECTION USING FLEXIBLE MONOPOLE ANTENNA

2020 ◽  
Vol 99 ◽  
pp. 99-110 ◽  
Author(s):  
Md. Ashikur Rahman ◽  
Md. Foisal Hossain ◽  
Manjurul Ahsan Riheen ◽  
Praveen Kumar Sekhar
Keyword(s):  
Monopole Antenna ◽  
Brain Stroke ◽  
Brain Stroke Detection

scholarly journals Finite-element analysis of microwave scattering from a three-dimensional human head model for brain stroke detection

2018 ◽  
Vol 5 (7) ◽  
pp. 180319
Author(s):  
Awais Munawar Qureshi ◽  
Zartasha Mustansar ◽  
Samah Mustafa
Keyword(s):  
Finite Element ◽  
Dielectric Properties ◽  
Electric Field ◽  
Three Dimensional ◽  
Human Head ◽  
Head Model ◽  
Different Types ◽  
Brain Stroke ◽  
Human Head Model ◽  
Brain Stroke Detection

In this paper, a detailed analysis of microwave (MW) scattering from a three-dimensional (3D) anthropomorphic human head model is presented. It is the first time that the finite-element method (FEM) has been deployed to study the MW scattering phenomenon of a 3D realistic head model for brain stroke detection. A major contribution of this paper is to add anatomically more realistic details to the human head model compared with the literature available to date. Using the MRI database, a 3D numerical head model was developed and segmented into 21 different types through a novel tissue-mapping scheme and a mixed-model approach. The heterogeneous and frequency-dispersive dielectric properties were assigned to brain tissues using the same mapping technique. To mimic the simulation set-up, an eight-elements antenna array around the head model was designed using dipole antennae. Two types of brain stroke (haemorrhagic and ischaemic) at various locations inside the head model were then analysed for possible detection and classification. The transmitted and backscattered signals were calculated by finding out the solution of the Helmholtz wave equation in the frequency domain using the FEM. FE mesh convergence analysis for electric field values and comparison between different types of iterative solver were also performed to obtain error-free results in minimal computational time. At the end, specific absorption rate analysis was conducted to examine the ionization effects of MW signals to a 3D human head model. Through computer simulations, it is foreseen that MW imaging may efficiently be exploited to locate and differentiate two types of brain stroke by detecting abnormal tissues’ dielectric properties. A significant contrast between electric field values of the normal and stroke-affected brain tissues was observed at the stroke location. This is a step towards generating MW scattering information for the development of an efficient image reconstruction algorithm.

scholarly journals Experimental Validation of Microwave Tomography with the DBIM-TwIST Algorithm for Brain Stroke Detection and Classification

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 840 ◽  
Author(s):  
Olympia Karadima ◽  
Mohammed Rahman ◽  
Ioannis Sotiriou ◽  
Navid Ghavami ◽  
Pan Lu ◽  
...  
Keyword(s):  
Inverse Problem ◽  
Ischemic Stroke ◽  
Dielectric Properties ◽  
Experimental Validation ◽  
Initial Guess ◽  
Microwave Tomography ◽  
Iterative Shrinkage ◽  
Brain Stroke ◽  
Brain Stroke Detection ◽  
Iterative Shrinkage Thresholding

We present an initial experimental validation of a microwave tomography (MWT) prototype for brain stroke detection and classification using the distorted Born iterative method, two-step iterative shrinkage thresholding (DBIM-TwIST) algorithm. The validation study consists of first preparing and characterizing gel phantoms which mimic the structure and the dielectric properties of a simplified brain model with a haemorrhagic or ischemic stroke target. Then, we measure the S-parameters of the phantoms in our experimental prototype and process the scattered signals from 0.5 to 2.5 GHz using the DBIM-TwIST algorithm to estimate the dielectric properties of the reconstruction domain. Our results demonstrate that we are able to detect the stroke target in scenarios where the initial guess of the inverse problem is only an approximation of the true experimental phantom. Moreover, the prototype can differentiate between haemorrhagic and ischemic strokes based on the estimation of their dielectric properties.

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