The Biosignal Processing and Quantitative/Systems Physiology Lab conducts research in the general area of biological/physiological systems mathematical modeling. We are interested in developing algorithms for biosignal processing and systems identification, with an emphasis on nonlinear and nonstationary systems. We are also interested in applying these to systems biology and physiology in order to:
- understand and predict the function of biological and physiological systems
- develop biomarkers for timely diagnosis
- develop therapeutic (e.g., control-based) approaches
In this context, we are specifically interested in the following topics:
- Bayesian and adaptive estimation of nonlinear (Volterra/Wiener-type) systems
- Model order selection for dynamic systems
- Modeling of cerebral hemodynamics from blood pressure, CO2 and blood flow velocity measurements
- Stationary and nonstationary analysis of human brain resting state networks from multimodal functional neuroimaging measurements (fMRI, EEG, MEG and simultaneous fMRI/EEG)
- Modeling the fMRI hemodynamic response
- Glucose metabolism and control
- Epileptic seizure detection and prediction
- Cancer progression modeling and therapy planning
If you are interested in conducting research in the above areas please contact us at gmitsisucy.ac.cy
We are currently coordinating the following research projects:
- Cancer Chemotherapy: Mathematical Modelling and Optimal Control (DIDAKTOR program funded by the Cyprus Research Promotion Foundation). This is a project in collaboration with Cambridge University, the Catholic University of Valparaiso, Chile and the Hellenic Cooperative Oncology Group.
- Detection and prediction of epileptic seizures by integrating multimodal (EEG and ECG) physiological measurements and subjective prodromal symptoms (funded by the Cyprus Research Promotion Foundation). This is a project in collaboration with the Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus and the National Technical University of Athens.
- Mathematical modeling of cancer progression and development of therapy strategies with model-based control methods (YPERTHEN - funded by the European Union Territorial Cooperation program and national funds of Greece and Cyprus). This is a project in collaboration with the Computational Medicine Laboratory at FORTH-ICS (Foundation of Research and Technology Hellas - Institute of Computer Science), Heraklion, Crete and the Digital Signal & Image Processing Lab, Technical University of Crete.