Data mining for automated diagnosis of neurological and psychiatric disorders

In this keynote lecture, the author presents novel algorithms for data mining of time-series data and automated electroencephalogram (EEG)-based diagnosis of neurological and psychiatric disorders based on adroit integration of three different computing technologies and problem solving paradigms: neural networks, wavelets, and the chaos theory. Examples of the research performed by the author and his associates for automated diagnosis of epilepsy, the Alzheimer's Disease, Attention Deficit Hyperactivity Disorder (ADHD), autism spectrum disorder (ASD), and Parkinson's disease (PD) are reviewed.