Artificial Intelligence; Electroencephalography; Medical Informatics; Neural Networks (Computer); Pattern Recognition, Automated; Signal Processing, Computer-Assisted; Sleep Stages; Support Vector Machines
My main research focus is on applying state-of-the-art methods from digital signal processing and pattern recognition to the development of diagnostic support in various clinical fields. One strong focus in recent years has been on recognizing patterns in electroencephalograms (EEG), and scoring such data in sleep medicine. An example is the automated staging of sleep based on EEG and other biosignals such as electrooculography (EOG) and electromyography (EMG) according to international standards. We also work on novel pattern recognition techniques to extract information from those biosignals beyond visually identifiable states. Part of our work has been on using Bayesian estimation techniques to account for uncertainty in data and to arrive at most robust results. More recently my focus has also been moving toward "Data Science for Personalized Medicine".
Techniques, methods & infrastructure
Pattern recognition, machine learning, neural networks, support vector machines, Gaussian mixture models, Bayesian learning, time series processing, signal processing, hidden Markov models, Matlab
- Imaging neuronal circuits of the preforntal cortex during a gambling task (2015)
Source of Funding: WWTF (Vienna Science and Technology Fund), Life Sciences
- Vyssoki, B. et al., 2014. Direct Effect of Sunshine on Suicide. JAMA Psychiatry, 71(11), p.1231. Available at: http://dx.doi.org/10.1001/jamapsychiatry.2014.1198.
- Aschauer, S. et al., 2014. A prediction tool for initial out-of-hospital cardiac arrest survivors. Resuscitation, 85(9), pp.1225-1231. Available at: http://dx.doi.org/10.1016/j.resuscitation.2014.06.007.
- Lewandowski, A., Rosipal, R. & Dorffner, G., 2012. Extracting more information from EEG recordings for a better description of sleep. Computer Methods and Programs in Biomedicine, 108(3), pp.961-972. Available at: http://dx.doi.org/10.1016/j.cmpb.2012.05.009.
- Anderer, P. et al., 2010. Computer-Assisted Sleep Classification according to the Standard of the American Academy of Sleep Medicine: Validation Study of the AASM Version of the Somnolyzer 24 × 7. Neuropsychobiology, 62(4), pp.250-264. Available at: http://dx.doi.org/10.1159/000320864.
- Hoever, P, Dorffner G. et al., 2012. Orexin Receptor Antagonism, a New Sleep-Enabling Paradigm: A Proof-of-Concept Clinical Trial. Clinical Pharmacology & Therapeutics, 91(6), pp.975-985. Available at: http://dx.doi.org/10.1038/clpt.2011.370.