Recognizable proof of relationships with image approaches in ischemic stroke utilizing computed Tomography (CT) and electroencephalogram (EEG)

The current paper focuses on correlating the region of ischemic stroke based on CT-scan and EEG image visualization, approaching using Brainstorm and BSI on each pair. Ischemic stroke occurs when the flow of blood containing oxygen and supplements to the brain is blocked, causing the body to break down. In arrange to identify stroke, we generally use CT and MRI, which have succeeded in determining the location with high accuracy. The factor is the high price of CT-scan or MRI, so we want to find another alternative with a low-cost and practical approach to using EEG. The most recent research on EEG in detecting stroke uses a signal processing approach based on time series and frequency to obtain abnormalities from the signal graph with the initial suspicion of blockage of blood vessels within the brain. Research is related to finding indications of stroke by using the brain symmetry index, which is to find the symmetry value of the relationship between the two-sided electrode locations. This study uses a signal processing approach to become an image with the help of the Brainstorm toolbox software released by the University of Southern California. The data used came from patients who experienced stroke symptoms with fewer than 72 hours. The current inquires about the investigation is based on recognizing the electrical current density in a data grouping based on the region of interest on the cortical surface in ischemic stroke conditions. All the patients were examined for ischemic stroke based on CT, which experts analyzed from the National Brain Center Hospital. Data processing is done by calculating the Brain Symmetry Index (BMI) and visualizing the data with Brainstorm on each EEG data. These results explain a correlation between expert CT-scan diagnoses to BMI values and are supported by data visualization using Brainstorm.