35371 - Biomedical Data and Signal Processing M

Course Unit Page


This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.

Good health and well-being Quality education

Academic Year 2018/2019

Learning outcomes

The course aims at providing the students with the knowledge and methodology for extracting useful information from a biomedical signal, interpret the results and validate the descriptors obtained in the light of knowledge of the biological system involved, produce innovation within the scope of: i) the improvement of physiological knowledge, ii) the design of novel, smart medical equipment, iii) the definition of new clinical protocols for prevention, diagnosis, and treatment.

Course contents

1. Introduction to Signals Theory and Time-domain Analysis Techniques
Signals classification. Signals, data and information. Algorithms for event detection and waveform recognition. Template-based algorithms. Matched filters.

2. Spectral Analysis Techniques
Traditional non-parametric techniques (direct and indirect methods). Welch's method. Parametric techniques (AR models). Yule-Walker AR estimator. Examples of frequency-domain analysis in biomedical signals (ECG, EEG, HRV).

3. Time-Frequency and Time-Scale Methods for Biomedical Signal Processing
Linear and quadratic time-frequency representations. Short-time-Fourier-transform and the spectrogram. Multiresolution analysis. Continuous (CWT) and Discrete (DWT) Wavelet transform. Implementation of DWT with Quadrature Mirror Filters.Hilbert-Huang transform. Applications to the analysis of biomedical signal.

4. Statistical Tools for Clinical Reasoning
Bases for probability calculus. Bayes' theorem. Random variables. Random variables for clinical decision making. Sample statistics. Hypothesis testing and analysis of variance. Experimental design techniques in clinical practice. Introduction to multivariate statistical analysis. Principal Component Analysis. Bayes linear classifier. Data mining techniques.

5. Survey of a Collection of Biomedical Signals
Review of signals from electrophysiology, hemodynamics and biomechanics. Genesis and properties of relevant signals: action potentials, neural signals, ECG, EMG, EEG, evoked potentials. Spontaneous and induced signals. Rationale for biomedical signal processing. Analysis of relevant biomedical signals in Matlab.


- Handouts and materials provided by the lecturer

- A.V. Oppenheim, R.W. Schafer, "Discrete-time Signal Processing (2nd ed.)", Prentice Hall, 1999
- R.M. Rangayyan, "Biomedical Signal Processing - A Case-Study Approach", Wiley Interscience, 2002
- K.J. Blinowska, J. Zygierewicz, "Practical Biomedical Signal Analysis Using MATLAB®", CRC Press (only for UNIBO users)

Teaching methods

Classes, computer-aided problem solving with Matlab functions and toolboxes, lab experiences, seminars related with different applications of biomedical signal processing. Projects will be assigned to small groups of students, dealing with the design of algorithms to process real biomedical signal, in order to help students to familiarize with algorithms theoretically presented during the classes and to engage with a problem-solving approach.

Assessment methods

Oral examination preceded by a group presentation of the results of the project assigned in the early stage of the course.The final grade will be the average (expressed in 30eths) of the two grades.

Teaching tools

PPT slides, videoprojector, and PC with Matlab and its toolboxes for digital signal processing and statistical data analysis, representative biomedical instrumentation (ECG, EMG, EEG, wearable motion sensors), open access online datasets of biomedical signals (e.g. Physionet).

Office hours

See the website of Lorenzo Chiari

See the website of Luca Palmerini