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Course Content
Part [1] Biomedical Signal Processing.
Programme of lessons and exercises
Introduction to biomedical signal processing. General block diagram of biomedical signal processing operations. Recall of analog and digital filters.
Cardiovascular System. ECG signal: main superimposed noises.  Most significant configurations from clinical standpoint. High resolution ECG. Ventricular late potentials: pathophysiological aspects and processing methods. Study of Autonomic Nervous System (ANS) by means of heart rate variability  signal processing in  short- and long-term bases. Pathophysiological aspects: neural control in heart rate and arterial blood pressure. Fetal ECG: processing methods and enhancement of useful clinical parameters. Fetal monitoring. Arterial blood pressure signal: detection systems and main clinical parameters. Interaction models among signals related to autonomic nervous system: ECG, arterial blood pressure and respiration; open- and closed-loop models. Pathophysiological interpretations.
Diagnostic Classification. Classification methods. Principal Component Analysis (PCA): Â general introduction and applications. Entropy Analysis: methods and applications.
Neurosensorial System. EEG (electroencephalographic) signal analysis, evoked potentials (EPs) Â and event-related potentials (ERPs). Review on traditional processing methods with main applications in clinical and research environments: cerebral activity detected on the scalp, at cortical level and in deep brain stimulation (DBS).
Signal processing and parametric identification. Time series analysis detected from biological signals. Examples in ECG, EEG, EP in pre-processing, filtering, prediction, extraction and estimation of parameters. Diagnostic classification. Deterministic and stochastic identification. Review on stochastic identification approaches. Model families, in particular AR/MA/ARMA (autoregressive and moving average) and models with exogenous input X. Parametric spectral analysis, including backward and forward methods, maximum entropy methods, Pisarenko, Prony, etc. Comparison with the traditional non-parametric techniques: various examples on heart rate variability signals, EEGs and EPs.
Laboratory activities Exercises with Matlab implementing signal analysis tools. Application to biomedical signals. Home work execises with evaluation.
Expected Learning Outcomes The formative objective is that the student takes possession of the signal processing methods in biomedical signals. At the end they should be able to work autonomously in different practical cases also different from those addressed in the lessons and/or in the laboratory practical exercises.
Prerequisites: No mandatory prerequisites; it is advisable to be aware of the fundamentals of digital signal processing.
Part [1] Biomedical Signal processing (short summary)
The course is intended to recall general signal processing aspects, to introduce new methods beyond those considered in the bachelor degree and examine various clinical and research application fields. Methods. From deterministic filtering to stochastic parametric analysis: mono and multi-variate AR/MA/ARMA models and non parametric as well as parametric spectral analysis. Principal component analysis. Entropy in signal processing. Applications. Automatic analysis and classification of the electrocardiographic signal (ECG). The autonomic nervous system, cardiovascular variability signals, and cardiorespiratory interactions. Fetal ECG signal. High resolution ECG and late ventricular potentials. The central nervous system: processing of the electroencephalographic signal (EEG) and of evoked (EP) and event-related potentials (ERP).
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