Accepted_test

Analysis of paroxysmal neuronal activity in patients with traumatic brain injuries

by Gaisina Liana Ildarovna | Moscow Institute of Physics and Technology

Abstract ID: 611

Event: BGRS-abstracts

Sections: [Sym 10] Section “Neuroimaging and genotyping technologies for the diagnosis of neuropsychiatric disorders; methods of disease correction based on feedback technology; brain-computer interface technologies”

The goal of this study is to investigate paroxysmal neuronal activity in patients who have experienced traumatic brain injuries (TBI), employing machine learning methods to identify characteristic patterns in electroencephalograms (EEG). The relevance of the study stems from the potential to improve TBI outcomes by enabling earlier detection of complications through EEG data. The current state of knowledge in neuroscience and medicine reveals a significant gap in understanding how EEG data can be used to predict paroxysmal neuronal activity and its consequences. This research has successfully identified specific EEG metrics that predict characteristic patterns of paroxysmal neuronal activity in TBI patients. Using these metrics, a model was developed for classifying traumatic brain injuries. The findings offer a foundation for developing new treatment and rehabilitation methods for individuals affected by TBI, significantly enhancing diagnostic quality and potentially reducing the risk of complications.