Localization of epileptic seizure focus by computerized analysis of fMRI recordings

• Published in: Brain informatics Vol. 7; no. 1; p. 13
• Main Authors: Hekmati, Rasoul, Azencott, Robert, Zhang, Wei, Chu, Zili D., Paldino, Michael J.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 31.10.2020; Springer; Springer Nature B.V; SpringerOpen
• Subjects: Artificial Intelligence; Cognitive Psychology; Computation by Abstract Devices; Computer Science; Convulsions & seizures; Deep learning; Epilepsy; fMRI; Health Informatics; Localization; Machine learning; Magnetic resonance imaging; Medical schools; Multilayers; Mutual information; Neurosciences; Seizure focus; Seizures; Seizures (Medicine); Software industry; Time series; United States; Deep learning; Seizure focus; fMRI; Epilepsy; Mutual information; Time series
• ISSN: 2198-4018; 2198-4026
• DOI: 10.1186/s40708-020-00114-0

By computerized analysis of cortical activity recorded via fMRI for pediatric epilepsy patients, we implement algorithmic localization of epileptic seizure focus within one of eight cortical lobes. Our innovative machine learning techniques involve intensive analysis of large matrices of mutual information coefficients between pairs of anatomically identified cortical regions. Drastic selection of pairs of regions with biologically significant inter-connectivity provides efficient inputs for our multi-layer perceptron (MLP) classifier. By imposing rigorous parameter parsimony to avoid overfitting, we construct a small-size MLP with very good percentages of successful classification.