OVERVIEW

The Biomedical Signal Processing laboratory offers a practical introduction to the analysis of physiological signals, with a focus on the application of advanced methods for biomedical data processing. Students will explore the main signal transformation techniques, particularly the Fourier Transform and the Hilbert Transform, fundamental tools for analysis in the frequency and phase domains. The design and application of digital filters will also be addressed, essential for selecting components of interest in signals. Through guided exercises, real signals from electroencephalography, electrocardiography, and electromyography will be analyzed. Finally, the foundations of inferential statistics applied to signal analysis will be explored through practical exercises, with the objective of developing concrete skills in processing, visualization, and interpretation of biomedical data.

AIMS AND CONTENT

LEARNING OUTCOMES

The laboratory aims to provide students with practical skills in biomedical signal processing. Upon completion of the course, students will be able to apply transforms (Fourier and Hilbert), design and use digital filters, analyze EEG, ECG, and EMG signals in Python, and perform simple inferential statistical analyses on data.

AIMS AND LEARNING OUTCOMES

At the end of the laboratory, students will be able to:

1. Knowledge and understanding

   • Describe the fundamental principles of biomedical signal processing, with particular reference to the Fourier Transform, Hilbert Transform, and digital filtering techniques.
   • Understand the main characteristics of physiological signals such as EEG, ECG, and EMG, and the issues associated with their analysis.

2. Ability to apply knowledge and understanding

   • Apply transformation and digital filtering techniques to real signals using the Python programming language.
   • Perform pre-processing operations, time and frequency domain analysis, and visualization of biomedical signals.
   • Conduct basic analyses using inferential statistical tools applied to physiological signals.

3. Making judgments

   • Critically evaluate the results obtained from processing, choosing appropriate methods based on the type of signal and analysis objectives.
   • Demonstrate progressive autonomy in designing and developing simple signal analysis pipelines.

4. Communication skills

   • Communicate clearly and coherently the methodological choices adopted and the results obtained, including through documentation of required assignments.

5. Learning skills

   • Address new problems in the field of biomedical signal processing with a practical and solution-oriented approach, developing autonomous learning capabilities through laboratory experience.

TEACHING METHODS

The laboratory is based on guided computer activities, conducted in the classroom under the supervision of the instructor and/or tutor, during which theoretical and computational tools for biomedical signal processing will be introduced and applied. Each module also includes additional practical activities to be carried out independently, individually or in groups, which consist of developing and documenting analyses on provided datasets. These exercises represent an integral part of the educational pathway and will be subject to evaluation. The teaching approach is strongly applicative and problem-solving oriented, with the objective of stimulating active participation, critical thinking, and the ability to work in groups.

SYLLABUS/CONTENT

Module 1 – Pre-processing and filter design on ECG signals

• Introduction to ECG signals: characteristics and main components
• Noise and artifact removal: low-pass, high-pass, and notch filters
• Design of FIR and IIR filters in Python
• Identification and segmentation of QRS complexes
• Recognition of pathological patterns (arrhythmias, morphological alterations)

Module 2 – Signal decomposition and transforms on EEG signals

• Introduction to EEG signals: physiological frequencies and common noise
• Fourier Transform for spectral analysis
• Hilbert Transform and instantaneous amplitude calculation
• Frequency band analysis and biomarker estimation (e.g., alpha power, theta)
• Comparison between thresholds, events, and cognitive states

Module 3 – Advanced analysis of EMG signals

• Introduction to EMG signals: temporal and spectral characteristics
• Advanced design of adaptive filters and narrow-band filters
• Empirical Mode Decomposition (EMD) and analysis of Intrinsic Mode Functions (IMF)
• Temporal correlation analysis between EMG channels
• Study of muscle activation profiles and neuromuscular synergies

TEACHERS AND EXAM BOARD

Exam Board

GABRIELE ARNULFO (President)

LESSONS

EXAMS

ASSESSMENT METHODS

Assessment methods The exam consists of the evaluation of three assignments, each referring to one of the laboratory modules. The exercises will be carried out in working groups and will include code documentation and interpretation of the obtained results. At the end of the course, a group oral discussion of the presented work is scheduled, aimed at verifying critical understanding of the tools used and the theoretical-practical content addressed during the laboratory.

The final evaluation will take into account:

• quality and technical correctness of the assignments (preprocessing, analysis, visualization);
• ability to justify the methodological choices adopted;
• active participation in laboratory activities;
• clarity and completeness of the oral discussion.