Research focus The Biomedical Engineering research team (BME) focuses on the development and testing of new methods and technologies for improved diagnostics for better stratification of heart and brain diseases. The BME aims its main research effort at progressive analyses of weak electrical signals emitted by the body organs through an advanced acquisition of these signals. The BME team also offers high-quality technical engineering services for researchers in terms of biological signal measurement and analyses, as well as the research data management and mining.
To research various frequency components in the ECG signal and their contribution to clinical practice – spatial and temporal distribution properties in the QRS complex on different frequencies using new ultra-high frequency ECG (UHF-ECG) methods and technologies.
To analyze EEG high-frequency oscillations (HFOs) and their meaning in physiology and pathophysiology of the epileptic brain.
To research EEG signal propagation – the brain connectivity in physiology and pathophysiology in epilepsy and Parkinson’s disease.
Mayo Clinic, Rochester, MN, USA
Institute of Scientific Instruments, Brno, Czech Republic
University Hospital Královské Vinohrady, Prague, Czech Republic
Gdansk University of Technology, Gdansk, Poland
Offered services and expertise
Biological data acquisition and analysis.
Clinical/research data management and mining.
Electro-magnetically clean laboratories with shielding from low frequencies (0.1Hz) and specific multichannel acquisition systems.
Development of new diagnostic technologies and tools in neurology and cardiology.
Jurak P., Curila K., Leinveber P., Prinzen FW., Viscor I., Plesinger F., Smisek R., Prochazkova R., Osmancik P., Halamek J., Matejkova M., Lipoldova J., Novak M., Panovsky R., Andrla P., Vondra V., Stros P., Vesela J., Herman D.: Novel ultra-high-frequency electrocardiogram tool for the description of the ventricular depolarization pattern before and during cardiac resynchronization. Journal of Cardiovascular Electrophysiology. 2020, 31(1), 300-307.
Halámek J., Leinveber P., Viscor I., Smisek R., Plesinger F., Vondra V., Lipoldova J., Matejkova M., Jurak P.: The relationship between ECG predictors of cardiac resynchronization therapy benefit. 2019, PLoS One, 14(5).
Cimbalnik J., Klimes P., Sladky V., Nejedly P., Jurak P., Pail M., Roman R., Daniel P., Guragain H., Brinkmann B., Brazdil M., and Worrell GA.: Multi-feature localization of epileptic foci from interictal, intracranial EEG. Clin. Neurophysiol., 2019, 130(10), 1945–1953.
Klimes P., Cimbalnik J., Brazdil M., Hall J., Dubeau F., Gotman J., and Frauscher B.: NREM sleep is the state of vigilance that best identifies the epileptogenic zone in the interictal electroencephalogram, Epilepsia, 2019, 60(12), 2404-2415.
Brázdil M., Pail M., Halámek J., Plešinger F., Cimbalnik J., Roman R., Klimeš P., Daniel P., Chrastina J., Brichtova E., Rektor I., Worrell GA., and Jurák P.: Very high frequency oscillations: Novel biomarkers of the epileptogenic zone. Ann. Neurol., 2017, 82(2), 299-310.
Other selected results
Ultra-High-Frequency ECG for the diagnostics of electrical conduction disturbances of the heart ventricles – the new methodology of processing ECG signal. Technology and methodology are covered by US patent and by EU and Czech patent applications.
Machine learning models for localization of epileptogenic tissue – new methodology utilizing various intracranial EEG features in support vector machine models or convolutional neural networks to localize seizure generating tissue. Filed US patent.
INTERNATIONAL CLINICAL RESEARCH CENTER
OF ST. ANNE’S UNIVERSITY HOSPITAL BRNO