Universitat Internacional de Catalunya
Signals and Systems Theory
Other languages of instruction: Catalan, English
Teaching staff
A face-to-face meeting can be arranged with the teacher by writing to his email.
Introduction
Our bodies constantly communicate information about our health. This information can be captured through physiological instruments that measure heart rate, blood pressure, oxygen saturation levels, blood glucose, nerve conduction, brain activity, etc. Traditionally, these measurements are taken at specific times and recorded in the patient's medical record. Doctors actually see less than one percent of these values as they go on their rounds, and treatment decisions are made based on these isolated readings.
Biomedical signal processing involves analyzing these measurements to provide useful information on which clinicians can make decisions. Engineers are discovering new ways to process these signals using a variety of mathematical formulas and algorithms. Working with traditional bio-measurement tools, signals can be calculated by software to provide clinicians with real-time data and increased insights to aid in clinical evaluations. By using more sophisticated means to analyze what our bodies are saying, we can potentially determine a patient's health status through more non-invasive measures.
Pre-course requirements
Algebra, Calculus, Fundamentals and Electronic Systems, Biomedical Instrumentation
Objectives
The specific objectives are:
- Know and classify biomedical signals according to their nature.
- To know software tools that allow to mathematically process biomedical signals.
- Have the ability to manipulate signals through filters in discrete time.
- Understand the relationships of the temporal and frequency domain, and be able to extract relevant information from the biomedical signals in both domains.
- Design simple digital filters and apply basic techniques for the reduction of artifacts in biomedical signals.
- Know standard diagnostic algorithms to process mathematically. biomedical signals for clinical application.
Competences/Learning outcomes of the degree programme
- CN01 - Describe aspects related to bioengineering based on subject-specific books together with scientific publications at the forefront of knowledge.
- HB12 - Evaluate manufacturing systems and processes, metrology and quality control.
- HB13 - Apply the principles of electronics, sensors, conditioners and biomedical signal acquisition systems, individually and in an integrated way
Learning outcomes of the subject
Upon completion of this course, students will be able to:
• Distinguish signal processing electronics from electrical energy conversion electronics.
• Describe basic electronic components, the general structure of an electronic system, and the basic functions performed within it.
• List the sensors used in biomedical applications, as well as analyze and interpret them.
• Differentiate between a combinational system and a sequential system.
• Describe various combinational blocks, sequential blocks, and the operational amplifier.
• Define negative and positive feedback in an amplifier.
• Describe linear and nonlinear operators.
Syllabus
Chapter 0. Introduction. Analog and digital signals.
Practical examples: Recording of analog signals from electronic transducers and visualization using an oscilloscope.
Chapter 1. The sinusoidal signal
Practical examples: Graphic representation of sinusoidal signals by computer. Variable frequency sinusoidal signal generation for audiometry applications. Mathematical modeling of EMG signals with sinusoidal signals.
Chapter 2. Sampling of analog signals
Practical examples: acquisition of a signal. Use of educational signal acquisition systems (Biopac, Labtutor, etc.). Study of the effect of the sampling theorem (Nyquist) in the acquisition of biomedical signals.
Chapter 3. Convolution and correlation
Practical examples: Computer graphic representation and calculation of correlation, application of normalized cross-correlation for detection of similarity between signals, detection of similar EMG signals with correlation in a configuration of electrode arrays.
Chapter 4. The Fourier Transform
Practical examples: Graphic representation and calculation of the fast Fourier transform (FFT) by computer, acquisition of the ECG signal with educational signal acquisition systems (Biopac, Labtutor, etc.) and obtaining the frequency spectrum.
Chapter 5. Digital filters
Practical examples: Calculation of the transfer function of digital filters using a computer, computer application of the convolution theorem, computer application of filtering the noise of the electrical network and its harmonics, digital filtering of the ECG signal acquired in the laboratory. Acquisition and filtering of the EEG signal with educational signal acquisition systems (Biopac, Labtutor, etc.) and obtaining the different frequency bands by filtering.
Chapter 6. Time-frequency representation, spectrogram
Practical examples: Graphic representation and calculation of the spectrogram of heart sounds (Phonocardiogram). Graphic representation and calculation of the spectrogram of the ECG signal acquired by the laboratory using different parametric and non-parametric methods.
Teaching and learning activities
In person
| TRAINING ACTIVITY | METHODOLOGY | COMPETENCES |
| Cooperative learning plays a significant role in the Bachelor’s degree in Bioengineering, its approach is based on organising activities inside the classroom so they become both a social and an academic learning experience. This type of learning depends on an exchange of information between students, who are motivated both to achieve their own learning and to increase the achievements of others. This activity covers practicums undertaken in a laboratory environment. Lectures are the setting for: learning and managing the terminology and language structures related to each scientific field. Practicing and developing oral and written communication skills. And learning how to analyse the bibliography and literature on Bioengineering. Using guidelines to identify and understand the main ideas during lectures. This academic activity has been an essential tool in education since it first began and should have a significant presence within the framework of this degree programme. Case studies are a learning technique in which the subject is faced with a description of a specific situation that involves a problem, that must be understood, evaluated and resolved by a group of people through a process of debate. Case studies will generally be undertaken through group work, which promotes student participation, thus developing their critical thinking skills. It also prepares students for decision-making, teaching them to defend their arguments and contrast them with opinions from others in the group. An activity for outside the classroom. This activity means students can allow their knowledge to settle and rest, which is always necessary before beginning a new task. The professor sets out exercises and problems, helps students to progress in terms of the engineering process the design involves, and guides the student, thus partial goals are achieved that facilitate the incorporation of the theoretical knowledge acquired. An activity for outside the classroom. During this activity, students complete exercises autonomously, without the presence of a lecturer/professor. At this stage many questions always arise, but since they cannot be asked immediately then the student has to make more effort to understand them | Student activities guided by the lecturer/professor will be undertaken on-site and the student’s evolution will be monitored consistently. Practical classes allow students to interact at first hand with the tools they will need to use in their work. In small groups or individually practical demonstrations will be carried out based on the theoretical knowledge acquired during the theory classes. In theory classes the fundamental and scientific knowledge that forms the basis of the knowledge and rigour that engineering studies require must be established. Individual work, involving study, the search for information, data processing and the internalisation of knowledge will allow students to consolidate their learning. | CB1 CB2 CB3 CB4 CB5 CE1 CE13 CE15 CE16 CE17 CE3 CG10 CG2 CG3 CG4 CG5 CG6 CG7 CT3 CT4 CT5 CT6 CT7 |
Evaluation systems and criteria
In person
Fully face-to-face mode in the classroom
The final grade for the course will be obtained as;
Note = 0,3·Nef +0,2·Nlab+0,1·Ntrab+0,4·Nparc
where
Nparc: Partial exam grade
Nef: Final exam grade
Nlab: Laboratory practice note
Ntreb: Note works of the subject
To qualify for the apt it is essential to carry out the subject's laboratory practices and a minimum grade of 4 points in the final exam.
Important considerations:
Plagiarism, copying or any other action that can be considered cheating will result in a zero in that evaluation section. Taking it in the exams will mean the immediate failure of the subject.
In the second call, the qualification of "Matriculation of Honor" will not be able to be obtained, reason why the maximum qualification will be of "Excellent".
Changes in the calendar, exam dates or in the evaluation system will not be accepted.
Exchange students (Erasmus and others) or repeaters will be subject to the same conditions as the rest of the students.
Important considerations
- Plagiarism, copying or any other form of academic dishonesty will result in a grade of zero for the corresponding component.
- If academic dishonesty is detected during an exam, it will result in the immediate failure of the course, with no chance of resitting.
- The use of artificial intelligence tools for the completion of assessment activities is strictly prohibited, except where their use is expressly authorized by the lecturer as part of the activity.
- The use or possession of electronic devices (mobile phones, smartwatches, earbuds, etc.) during exams is strictly prohibited.
Mere possession, even if the device is turned off, will be considered an attempt to cheat.
- If this occurs during the first call, it will result in the automatic failure of the exam, and the student will be required to attend the second call.
- If it occurs during the second call, it will result in the definitive failure of the course, and the student must re-enrol in the next academic year.
- No changes to the academic calendar, exam dates or evaluation system will be accepted under any circumstances.
- Exchange students (Erasmus or others) and repeaters are subject to the same evaluation and attendance conditions as all other students.
Bibliography and resources
[1] Proakis, John G ; Manolakis, Dimitris G. Digital Signal Processing. 4th ed. Madrid: Prentice-Hall, 2006. ISBN-10 : 0131873741.
[2] John Enderle, Joseph Bronzino. 2011. Introduction to Biomedical Engineering, 3 ed. ISBN : 978-0123749796.
[3] Alan V. Oppenheim, Alan S. Willsky. Signals And Systems. Pearson. 2nd Edition. 2017. ISBN-10 : 9332550239.
Evaluation period
- E1 20/05/2026 A14 12:00h