Thesis supervisor: György Kozmann
co-supervisor: Zoltán Juhász
Location of studies (in Hungarian): University of Pannonia, Faculty of Information Technology, Department of Electrical Engineering and Information Systems Abbreviation of location of studies: PE
Description of the research topic:
The amount of digital data produced by modern health care and clinical diagnostic systems present an increasing problem for fast and effective processing, analysis and understanding of biomedical data. Various methods have been proposed to solve this problem and the application of advanced statistical and artificial intelligence-based pattern recognition methods seems unavoidable in many practical areas, such as diagnostics of critical organs (heart, lung, brain), evaluating cognitive experiments, creating more sophisticated brain-computer interface applications or analyzing nation-wide health care system data sets. The goal of this research is to advance the applications and algorithmic techniques of artificial intelligence in one of the above biomedical areas.
Preliminary results can be found in the following publications:
1. Vimla L. Patel, Edward H. Shortliffe, Mario Stefanelli, Peter Szolovits, Michael R. Berthold, Riccardo Bellazzi, Ameen Abu-Hanna, The coming of age of artificial intelligence in medicine, Artificial Intelligence in Medicine, Volume 46, Issue 1, May 2009, Pages 5-17, ISSN 0933-3657, http://dx.doi.org/10.1016/j.artmed.2008.07.017.
2. Debnath, Tanoy, Md Mehedi Hasan, and Tanwi Biswas. "Analysis of ECG signal and classification of heart abnormalities using Artificial Neural Network." Electrical and Computer Engineering (ICECE), 2016 9th International Conference on. IEEE, 2016.
3. Nicolas-Alonso LF, Gomez-Gil J. Brain Computer Interfaces, a Review. Sensors. 2012; 12(2):1211-1279.
Login
