35371 - Biomedical Data and Signal Processing M

Course Unit Page

SDGs

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

Good health and well-being Quality education Decent work and economic growth

Academic Year 2021/2022

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. Case studies: heart rate monitor, pedometer.

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. Introduction to machine learning techniques. Artificial Intelligence in medicine.

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.

Readings/Bibliography

Mandatory

  • Handouts and materials provided by the lecturer

Suggested

  • A.V. Oppenheim, R.W. Schafer, "Discrete-time Signal Processing (2nd ed.)", Prentice Hall, 1999

Further readings

  • R. Shiavi, "Introduction to Applied Statistical Signal Analysis. Guide to Biomedical and Electrical Engineering Applications", 3rd edition, Academic Press, 2007
  • 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, seminars related with industrial and clinical 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.

In consideration of the type of activity and teaching methods adopted, the attendance of this training activity requires the prior participation of all students in modules 1 and 2 of the training on safety in the study places [https: //elearning-sicurezza.unibo.it /] in e-learning mode.

Assessment methods

In different days, individual oral examination and 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 Sabato Mellone