Lectures:
Basic characteristics and classification of the biologic signals: computer representation, discretization, types, biological origin, diagnostic, time, frequency, and time-frequency analysis.
Convolution analysis of the biological signals: analysis of the continuous and discrete convolution.
Spectral analysis of the biologic signals: Fourier series, Fourier transformation, algorithms for the FFT, spectral density, spectral energy, frequency spectra.
Filtration of the biological signals: synthesis of the analog and digital filters, FIR and IIR filters, notch filter, recursive filters, and filter frequency analysis.
The EEG signal analysis: the ECG signal representation, compressed spectral array (CSA), topographic mapping of the electrophysiological activity, interpolation of the spatial information, amplitude and frequency mapping, local coherency and phase measuring.
The ECG signal analysis: the noise analysis of the ECG signal, ECG signal representation, algorithms for the QRS complex extraction, Pan Tompkins algorithm, the R peak detection, ECG signal classification and calculation of the heart rate variability (HRV).
The PPG signal analysis: the PPG signal noise analysis, representation of the PPG signal, detection of the heart systolic phase and comparison of the heart rate from the PPG and ECG signal.
Analysis of the EMG signal: genesis, representation, features, measuring of the EMG signal, and basic methods of the EMG processing.
Laboratories:
Introduction to MATLAB and basic signal characteristics.
Implementation of convolution for biological sig nal processing.
Implementation of basic methods for frequency analysis: Fourier series and transformation, calculation of FFT.
Proposal of basic digital filters in MATLAB.
Basic algorithms for EEG processing.
Implementation of algorithms for filtration and detection ECG significant parts.
Algorithms for PPG processing.
Algorithms for time-frequency signal analysis in application of EMG signal processing.
Basic characteristics and classification of the biologic signals: computer representation, discretization, types, biological origin, diagnostic, time, frequency, and time-frequency analysis.
Convolution analysis of the biological signals: analysis of the continuous and discrete convolution.
Spectral analysis of the biologic signals: Fourier series, Fourier transformation, algorithms for the FFT, spectral density, spectral energy, frequency spectra.
Filtration of the biological signals: synthesis of the analog and digital filters, FIR and IIR filters, notch filter, recursive filters, and filter frequency analysis.
The EEG signal analysis: the ECG signal representation, compressed spectral array (CSA), topographic mapping of the electrophysiological activity, interpolation of the spatial information, amplitude and frequency mapping, local coherency and phase measuring.
The ECG signal analysis: the noise analysis of the ECG signal, ECG signal representation, algorithms for the QRS complex extraction, Pan Tompkins algorithm, the R peak detection, ECG signal classification and calculation of the heart rate variability (HRV).
The PPG signal analysis: the PPG signal noise analysis, representation of the PPG signal, detection of the heart systolic phase and comparison of the heart rate from the PPG and ECG signal.
Analysis of the EMG signal: genesis, representation, features, measuring of the EMG signal, and basic methods of the EMG processing.
Laboratories:
Introduction to MATLAB and basic signal characteristics.
Implementation of convolution for biological sig nal processing.
Implementation of basic methods for frequency analysis: Fourier series and transformation, calculation of FFT.
Proposal of basic digital filters in MATLAB.
Basic algorithms for EEG processing.
Implementation of algorithms for filtration and detection ECG significant parts.
Algorithms for PPG processing.
Algorithms for time-frequency signal analysis in application of EMG signal processing.