BIOMEDICAL ENGINEERING

Heart and brain signals. Read and understood.

Ing. Pavel Leinveber

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.

Research objectives

  • 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.

Main partners

  • 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.

Top publications

  • 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.