Brain Connectivity Analysis with Classification

Brain Connectivity Analysis with Classification

Panuwat Janwattanapong

Panuwat Janwattanapong

Miami, Florida

Classification of epileptiform discharges using connectivity analysis as a main feature

Artificial Intelligence

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Description

This research aims to take full advantage of connectivity analysis, where the method can be used to reveal and extract the hidden features presented in neurological disorders. The study would improve the fundamental understanding of the disease and enhance the diagnosis significantly. Functional connectivity extracted from epileptic patients will be explored in different frequency bands and compared with the control population to generate distinct patterns that can be used as a key parameters of classification algorithm. The comparison of connectivity patterns for different stages of seizure will be investigated as well.

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Panuwat J. added photos to project Brain Connectivity Analysis with Classification

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Brain Connectivity Analysis with Classification

This research aims to take full advantage of connectivity analysis, where the method can be used to reveal and extract the hidden features presented in neurological disorders. The study would improve the fundamental understanding of the disease and enhance the diagnosis significantly. Functional connectivity extracted from epileptic patients will be explored in different frequency bands and compared with the control population to generate distinct patterns that can be used as a key parameters of classification algorithm. The comparison of connectivity patterns for different stages of seizure will be investigated as well.

Medium 0 1zzucvr2azty7vdzqv5fg6a7fw0aszdj3 ketks20xmbpjdy3 5wxca7ov1apveykwkelgkoshyloxd9c1div3r2vhyaox41n1dmhv2u7vf4opwsnbj76vevkh

Panuwat J. created project Brain Connectivity Analysis with Classification

Medium 17135bfe bda1 4dab 94d7 4ad170fc2b26

Brain Connectivity Analysis with Classification

This research aims to take full advantage of connectivity analysis, where the method can be used to reveal and extract the hidden features presented in neurological disorders. The study would improve the fundamental understanding of the disease and enhance the diagnosis significantly. Functional connectivity extracted from epileptic patients will be explored in different frequency bands and compared with the control population to generate distinct patterns that can be used as a key parameters of classification algorithm. The comparison of connectivity patterns for different stages of seizure will be investigated as well.

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