This seminar focuses on the game changing applications of novel signal and image processing approaches based on deep-learning techniques for the accurate identification of translational Electroencephalogram (EEG) biomarkers of HypoxicIschemic (HI) brain injury from an exclusive data bank of clinical recordings of preterm infants, at birth.

Dr. Hamid Abbasi will discuss how the developed pattern recognition strategies are able to precisely classify EEG biomarkers from background activity and artifact, in real-time, with highly competitive accuracies compared to a clinical expert (tested on TBs of data).

Delving further into how such techniques can help with early diagnosis of HI brain injury, at bed-side, within the most critical first hours of life, where the current therapeutic protocols have been shown to be optimally neuroprotective in.

Finally, the seminar will share insights for future developments, including further capabilities of deep machine-learning strategies in biomedical engineering and neuroscience, along with the importance of employing high-tech facilities to achieve desirable outcomes.

Refreshments and networking from 12:10pm.

Hamid is a Research Fellow in the Department of Physiology at the University of Auckland with a longer-standing interest in advanced signal and image processing of biomedical data. He received his Ph.D. in 2017 in Engineering Science and holds M.E and B.E. degrees in Control, Electronics and Instrumentation engineering. His research interests expands to finding translations from lab experiments to the clinic, brain development, pattern recognition and classification, medical image processing and data science.

Co-organized by the IEEE NZ North Section EMBS Chapter and Auckland Bioengineering Institute (ABI) at the University of Auckland.

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