Teaching plan for the course unit



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General information


Course unit name: Biomedical Instrumentation and Signals

Course unit code: 363755

Academic year: 2021-2022

Coordinator: Santiago Marco Colas

Department: Department of Electronic and Biomedical Engineering

Credits: 9

Single program: S



Estimated learning time

Total number of hours 225


Face-to-face and/or online activities



-  Lecture

Face-to-face and online






-  Group tutorial

Face-to-face and online




(Flipped Class Room Small Projects)


-  Problem-solving class

Face-to-face and online




(Problem-solving exercises.)


-  Laboratory session

Face-to-face and online




(Laboratory practice.)


-  Student presentation and discussion

Face-to-face and online



Supervised project


(Small project, readings, lab reports.)

Independent learning


(Study of theory, MATLAB programming learning.)





For this course, students should have a basic knowledge of continuous linear systems theory, including Laplace transform. This content is covered in the subject Applied Electronics. Students should have a working knowledge of complex variables and functions. Finally, some programming notions are needed.



Competences to be gained during study



To be able to work independently (Personal).


To be able to work in a team or a multidisciplinary group (Personal).


To be able to work in a multilingual environment and communicate and transmit knowledge, procedures, results, abilities and skills (oral and written) in a native and a foreign language (Instrumental).


To be able to analyse, assess and take technological decisions in accordance with the criteria of cost, quality, safety, social impact, sustainability, time and respect for the ethical principles of the profession (Instrumental).


To be able to analyse and summarize (Instrumental).


To gain an understanding of the interaction of engineering with other areas of knowledge (medicine, biology, biotechnology, pharmacy, veterinary science) and to be able to collaborate effectively in multidisciplinary teams, with a knowledge of the principles of complementary technologies.


To be able to conceive, design and produce equipment and systems, especially those for biology and medicine. In particular, to be able to develop the hardware needed to receive, adapt, digitalize and process signals with different characteristics.


To be able to focus the design of products systematically. To select which parts of the application ideally require a hardware or software solution, to know how to suitably integrate both parts in the final product and to be able to develop, when required, an interface that enables the integration of more complex architectures.


To be able to conceive, design and produce equipment and systems, especially those for biology and medicine. In particular, to be able to incorporate algorithms for processing information into the appropriate hardware.


To gain knowledge of the equipment and instruments used for disease diagnosis, treatment, prevention and research.


To gain the scientific and technical training to work on the design and development of measurement, control and communication systems for all of the biomedical activities required by society and by scientific knowledge.


To know the basic mathematical, physical and engineering concepts that are required to interpret, select, assess and create new concepts, theories, uses and technological developments in biology and medicine.


To be able to design devices and systems to meet the needs of bioelectric signal diagnosis. To establish management methods for these systems.

Learning objectives


Referring to knowledge

— Be able to list the major biomedical signals and succinctly explain their physiology.


— Understand the terminology used to describe the metrological performance of a biomedical instrument and to estimate (compute) the measurement errors in a particular measurement scenario.


— Select sensors for the measurement of major physiological signals and to evaluate the advantages and disadvantages of different measurement principles for a certain application.


— Design a signal conditioning chain for typical sensors including the use of instrumentation amplifiers and to predict the performance of the design on the basis of the circuitry and the data sheets of the chosen components.


— Describe a noise signal from a time and frequency perspective and, in the presence of noise, be able to locate the major noise sources and take practical measures to reduce the effect on the measured signal.


— Know the Nyquist theorem and be able to select the proper sampling frequency and set up the correct specifications for an anti-aliasing filter.


— Set up the specifications of a reconstruction filter.


— Know the typical specifications of A/D and D/A converters and be able to estimate the overall error introduced by the conversion process.


— Find the transfer function from a difference equation and be able to calculate the frequency response.


— Properly set the conditions for a spectral analysis, including choice of the analysis technique and the setting of the parameters.


Referring to abilities, skills

— Automate biomedical signal acquisition using data acquisition boards and a programming language.


— Build a simple signal conditioning system for basic transducers and test its performance using basic electronic instrumentation.


— Program an R (or similar) script for signal filtering and spectral analysis.



Teaching blocks


1. Introduction to biomedical signals: biopotentials and biomechanical signals

2. Introduction to linear systems theory

2.1. Introduction to signals, noise and systems

2.2. Basic signals

2.3. Convolution

2.4. Properties of linear time-invariant systems

2.5. Continuous-time Fourier series

2.6. Continuous-time Fourier transform

2.7. Fourier transform properties

2.8. Basic signal filtering concepts

2.9. Statistical description of random signals: auto-correlation and cross-correlation

2.10. Power spectral density; The Wiener theorem

2.11. Random signals through linear systems

3. Introduction to biomedical signal processing

3.1. Sampling theorem and aliasing

3.2. Signal reconstruction: ideal and zero-order interpolation

3.3. A/D and D/A converters, quantisation noise, oversampled converters

3.4. Discrete-time signal processing: difference equations, Z-transform, frequency response of digital filters

3.5. Discrete Fourier transform and spectral analysis: window properties; Spectrogram

3.6. Design of finite impulse response filters by windowing

3.7. Design of infinite impulse response filters

4. Biomedical instrumentation

4.1. Measurement science basics for biomedical instrumentation

4.2. Biomedical sensors and transducers: thermal sensors, pressure sensors, chemical sensors

4.3. Instrumentation amplifiers: isolation amplifiers, bioamplifiers 

4.4. Noise in biomedical amplifiers

5. Instrumentation laboratory activities

5.1. Introduction to LabVIEW: basics of visual programming and virtual instrumentation

5.2. Development of sensor systems based on microprocessors

6. Signal laboratory activities

6.1. Estimation of dead space volumes using capnography

6.2. Alignment methods and signal averaging techniques in high-resolution electrocardiogram signals

6.3. Decimation and interpolation

6.4. FIR filter design by windowing

6.5. Enhancing ECG signals

7. Small Project

7.1. Free Induction Decay Signal Processing with Fourier Transform in Nuclear Magnetic Resonance

7.2. Simulation and analysis of a bioamplifier in SPICE

7.3. QRS Detection, Heart Rate Variability Signal and the detection of Atrial Fibrillation with Machine Learning



Teaching methods and general organization


The methodology combines classroom lectures, problem-solving activities, and laboratory work. An important feature of this course is the development of problem-based activities in the form of small projects. All educational activities are carried out in English. 

If restrictions are applied due to the current health situation, the number of attendants to lectures and the laboratory will be reduced to avoid exceeding the maximum occupancy established. Face-to-face activities will be combined with online lectures and activities. Computational exercises will be carried out at home with online supervision or fully autonomously, followed by supervision in a flipped-classroom approach. 

When relevant, gender perspective will be incorporated in the development of the subject.



Official assessment of learning outcomes


  • Lab reports & Questionnaires: 18%
  • Small Project: 42% (in groups).
  • Instrumentation exam 20%.
  • Signals exam 20%.

A minimum mark of 4.5/10 in the instrumentation and signals exams is required. Completing the problem-solving exercises and laboratory work is required to pass the course. Attendance in the lab activities is mandatory. A minimum of 80% is required to pass the course. 

Students who did not pass the course in previous academic years have to do the full set of assessment activities and they will be re-assessed.

Repeat assessment takes the form of a problem-solving exam with a total weight equal to the weight of the activities that have not been passed.


Examination-based assessment

Problem-solving exam.



Reading and study resources

Consulteu la disponibilitat a CERCABIB


Biomedical Transducers and Instruments, Tatsuo Togawa, Toshio Tamura, P.Ake Öberg, CRC Press (1997)

Introduction to Biomedical Equipment Technology, J.J. Carr, J.M. Brown, Prentice-Hall (1998)

Medical Instrumentation, J.G.Webster, Wiley (1998)

Electronic Measurement Systems:Theory and Practice, A.F.P. Van Putten, IOP, (1996)

Sensors and Signal Conditioning, R. Pallas, J.G. Webster, Wiley (2001)

Introduction to Signal Processing, S. Orfanidis, Prentice-Hall (1996)

Señales y Sistemas, A.V Oppenheim, A.S Willsky, Prentice-Hall, 1998.

Signals and Systems for Bioengineers, J. Semmlow, Academic Press, (2011)

Biomedical Signal Analysis, R.J.M. Rangayyan, IEEE Press, (2002)