Description:  Physiological origin, characterization, modeling, and analysis of biomedical signals, including EEG, MEG, and ECG signals.  Noise and artifact reduction; nonparameteric and model-based spectral estimation; join time-frequency analysis.

Prerequisites:  ECE 306 and ECE 345 or STA 301 or STA 368

Objectives: 

• Apply signal processing techniques to biomedical signals.
• Use spectral estimation, time-frequency analysis, and other techniques to analyze biomedical signals that may lead to identification of medical issues (examples:  mammographic analysis).
• Apply the above techniques to real data.
• Use MatLab programming meethods for biomedical signal processing.

Tentative Topics:

• Basics of biomedical signals; signal acquisition and analysis.
• EEG background: the nervous system, EEG rhythms and waveforms, characterication; EEG applications.
• EEG signal modeling; artifacts in EEG; non-parametric spectral estimation.
• Model-based spectral estimation; EEG segmentation; time-frequency analysis.
• Evoked potential modalities; noise reduction; single trial analysis; adaptive analysis.
• EMG introduction; signal model and ML estimation.
• Conduction velocity estimation; modeling intramuscular EMG; EMG decomposition.
• ECG introduction; hear rhythms, heartbeat morphologies.
• Time invariant filtering; time variant filter; QRS detection; wave delineation.
• Time domain measures; heart rhythms representations; spectral analysis of heart rate variability.