Biography:

Yanchun Zhang is Emeritus Professor at Victoria University, Australia. He is the founding Director of Centre for Applied Informatics at Victoria University.   His research interests include databases, data mining, social networking, web services and e-health. He has published over 400 research papers in international journals and conference proceedings including ACM Transactions on Computer and Human Interaction (TOCHI), IEEE Transactions on Knowledge and Data Engineering (TKDE), VLDBJ and ICDE conferences as well as medical journals. He authored/co-authored 5 monographs and edited a dozen of books in the related areas.  Dr. Zhang is a founding editor and editor-in-chief of World Wide Web Journal (Springer) and Health Information Science and Systems Journal (Springer).

Speech title:

Big Data and AI techniques for mental health / neurological disorder analysis and diagnosis

Abstract:

Electroencephalography (EEG) is the current reference standard for diagnosis of most of the mental and neurological disorders as it is inexpensive, non-invasive and portable compared to other tests. Measuring brain activity through EEG leads to the acquisition of a huge amount of data.  In current practice, massive EEG data are visually analyzed to identify and determine abnormalities within the brain how they propagate and function. Analyzing huge volumes of dynamic data in this way is time-consuming, subject to human error, and reduces decision-making reliability. Therefore, there is an ever-increasing need for developing artificial intelligent (AI) techniques that will produce accurate, timely and robust scientific evidence for reliable decision-making in the mental health disorders.

In this talk, we will report some of our recent work on the detection of mental health and neurological disorders such as mild cognitive impairment, schizophrenia, sleep stages problems, and epilepsy using EEG signal data.  We aim to develop reliable, robust and efficient analysis techniques and an integrated platform for the above-mentioned mental / neurological disorders from EEG signal data.