Course Title:
Semester: 2nd
Weekly teaching hours CREDITS (ECTS)
Lecture: 1 2.5
Laboratory: 1 2.5


This course will provide introductory knowledge of the nature of typical biomedical signals, providing their origins, characteristics, typical interferences and noise sources. Clinical and biomedical problems are presented giving the most popular tools to analyse and evaluate current signal processing solutions, proposing also alternative designs. Practical experience of applying signal processing methods to patient data improve students ability to critically analyse different signal processing approaches, identifying their strengths and limitations.

  1. Nature of Biomedical Signals Acquisition. The Cardiovascular System, Electrocardiogram (ECG), Phonocardiogram (PCG). The Nervous System, Electromyogram (EMG), Electroencephalogram (EEG). The Respiration system, Breath, Speech, Snoring, Cough. Transducers, Noise and artefacts. The multi-source, multi-sensor problem.
  2. Basic Signal Processing. Analog filters. Digital filters, FIR,IIR, Notch. Design of Digital filters. Adaptive filtering. Adaptive interference cancellation. Filtering of ECG, PCG, EEG signals using MATLAB.
  3. Statistical Processing of Biomedical signals. Random Variables & Processes. Intra- and inter-subject variability. Autocorrelation, Cross-correlation. Autoregressive models. Linear prediction. Spectrum Estimation. Parameter Estimation & Modeling. Noise cancellation. Parameters estimation and noise cancellation in ECG, EEG signals using MATLAB.
  4. Space-Time signal processing. Beamforming: Delay-and-sum and frequency-domain beamforming. MUSIC and ESPRIT algorithm. Signal separation. Principal Component Analysis (PCA). Independent Component Analysis (ICA). Array processing in Biomedical signals. EEG signal separation. Separation Of Maternal And Fetal ECG Signals. EEG/MEG Cordical Source Localization. Hearing Aid Arrays.

Learning outcomes

  • Identify and Understand Biomedical Signals: Students will be able to identify and describe the nature of various biomedical signals commonly encountered in clinical and biomedical contexts, such as ECG, EEG, EMG, and others.
  • Analyze Signal Characteristics: Students will develop the ability to analyze and interpret the characteristics of biomedical signals, including their frequency content, amplitude, duration, and waveform morphology.
  • Identify Interferences and Noise Sources: Students will learn to identify common interferences and noise sources that affect biomedical signals, such as power line interference, motion artifacts, electrode noise, and muscle artifacts.
  • Apply Signal Processing Techniques: Students will acquire knowledge and skills in applying signal processing techniques to biomedical signals, including methods for filtering, spectral analysis, time-frequency analysis, feature extraction, and noise reduction.
  • Evaluate Signal Processing Solutions: Students will develop critical thinking skills to evaluate existing signal processing solutions used in biomedical signal analysis. They will assess the strengths, limitations, and appropriateness of different techniques for specific applications.
  • Propose Alternative Signal Processing Designs: Students will be able to propose alternative signal processing designs or approaches to address specific clinical or biomedical problems, taking into consideration the specific requirements and constraints of the problem at hand.

General Competences

  • Technical Competence: Students will develop technical competence in the field of biomedical signal processing, including knowledge of signal processing techniques, mathematical principles, and tools used for analyzing and processing biomedical signals.
  • Problem-Solving Competence: Students will develop problem-solving skills by applying signal processing techniques to address clinical and biomedical problems related to biomedical signals. They will learn to identify issues, propose solutions, and evaluate the effectiveness of different approaches.
  • Critical Thinking: Students will enhance their critical thinking skills by critically analyzing and evaluating signal processing solutions for biomedical signals. They will learn to assess the strengths and limitations of different methods and propose alternative designs or improvements.
  • Data Analysis Competence: Students will develop competence in analyzing and interpreting patient data by applying signal processing techniques. They will learn to preprocess, analyze, and extract relevant information from biomedical signals, enhancing their ability to derive meaningful insights.
  • Communication Competence: Students will enhance their communication skills by effectively communicating about biomedical signals and signal processing techniques. They will learn to present their findings, methodologies, and conclusions both orally and in written form using appropriate terminology and concepts.
  • Interdisciplinary Competence: Students will develop interdisciplinary competence by integrating knowledge and concepts from different fields such as biomedical engineering, mathematics, and clinical sciences. They will understand how signal processing techniques can be applied to biomedical signals and bridge the gap between engineering and healthcare domains.
  • Ethical Competence: Students will gain an understanding of the ethical considerations involved in biomedical signal processing, such as data privacy, confidentiality, and informed consent. They will learn to apply ethical principles in handling patient data and conducting research in a responsible manner.
  • Lifelong Learning: Students will cultivate a mindset of lifelong learning and professional development in the field of biomedical signal processing. They will understand the need to stay updated with advancements in the field and adapt to new technologies and methodologies.
  • Collaboration and Teamwork: Students will develop competence in collaborating with peers, healthcare professionals, and researchers in multidisciplinary settings. They will learn to work effectively in teams, leveraging diverse perspectives and skills to solve complex problems.
  • Research and Innovation: Students will be encouraged to engage in research and innovation in the field of biomedical signal processing. They will learn to explore new approaches, propose novel solutions, and contribute to the advancement of knowledge in the field.