Doctoral defence: Egils Avots “Brain Abnormality Detection Using Statistical Analysis of Individual Structural Connectivity Networks and EEG Signals”

On December 15 at 12:15 Egils Avots will defend his doctoral thesis “Brain Abnormality Detection Using Statistical Analysis of Individual Structural Connectivity Networks and EEG Signals”

Supervisors:
Professor Gholamreza Anbarjafari, University of Tartu
Associate Professor Maie Bachmann, Tallinn University of Technology

Opponent:
Professor Aušra Saudargiene, Lithuanian University of Health Sciences, Kaunas, Lithuania

Summary

Brain Abnormality Detection Using Statistical Analysis of Individual Structural Connectivity Networks and EEG Signals
Cutting-edge medical science and artificial intelligence research is poised to transform brain illness diagnosis. This thesis, "Brain Abnormality Detection Using Statistical Analysis of Individual Structural Connectivity Networks and EEG Signals," focuses on Alzheimer's disease and clinical depression and leverages cutting-edge technologies to drive transformation.
Brain anomalies, whether present at birth or as a result of trauma, disease, or other circumstances, have a significant impact on physical and mental health. This thesis delves on two major topics: Alzheimer's disease is diagnosed using MRI, and clinical depression is detected using EEG.
Machine learning algorithms interpret brain scans to discover disease-specific patterns such as alterations in brain structure for people with Alzheimer's disease. With data analysed using different image patterns modalities, the possibility for faster, more accurate diagnosis exists.In the case of clinical depression, machine learning analyses EEG recordings to detect trends and predict depression. EEG measures depression-related brain activity, which is then interpreted by machine learning for diagnosis. Various patterns in EEG recordings enable patient classification, allowing for faster diagnosis.This thesis emphasises human ingenuity and the potential of AI to change healthcare. It points to a new era in brain illness diagnosis, with earlier and more accurate Alzheimer's and clinical depression diagnoses. As these technologies advance, the future holds the potential of better health for many people.

Defence can be also followed in Zoom: https://ut-ee.zoom.us/j/9530588152?pwd=ZzgzMjY4YytzUkZ5aVRCd2pOdVNQQT09 (Meeting ID: 953 058 8152 Passcode: kaitsmine).