Biomedical Signal Processing

This course covers methods for the processing and analysis of biomedical signals and images.

• Signal processing fundamentals: sampling, quantisation, Fourier and discrete cosine transforms, filtering (FIR, IIR).
• Time–frequency analysis: short-time Fourier transform (STFT), wavelet transform, spectrograms.
• Statistical methods: principal component analysis (PCA), independent component analysis (ICA), adaptive filters.
• ECG processing: QRS complex detection, heart rate variability (HRV) analysis, arrhythmia classification.
• EEG processing: epileptic event detection, sleep analysis, brain–computer interfaces.
• EMG processing: motor unit decomposition, muscle fatigue estimation.
• Medical image processing: segmentation, registration, classification using machine learning.

To master signal and image processing methods for biomedical applications and to apply them practically to the analysis of physiological data.

After successful completion, students will be able to:

• select and apply appropriate transforms or filters for a given biomedical signal
• perform time–frequency analysis of non-stationary signals
• apply statistical methods for dimensionality reduction and source separation
• design an algorithm for detecting characteristic events in ECG, EEG, or EMG signals
• segment and classify medical images using modern methods