Syllabus Information

5.1. Methodological overview

The methodology followed in this course is oriented towards achievement of the learning objectives. A wide range of teaching and learning tasks are implemented, such as lectures where the main contents are presented and discussed; practice sessions with practical examples, demonstrations and problems; autonomous work; and an assignment consisting on the programming, analysis and study of methods for biomedical signal processing on a set of biomedical signals.

5.2. Learning tasks

The course includes the following learning tasks:

• A01 Lectures (22 hours). The teacher explains the main contents of the course. This activity will take place in the classroom and will include practical examples, demonstrations and problem solving. Student participation is encouraged.
• A03 Computer lab sessions (6 hours). 3 two-hour sessions are held in a computer room. The students must submit a report of the practical work, which will be evaluated (E3).
• A05 Assignments (20 hours). A task will be assigned consisting on programming, studying and analyzing biomedical signal processing methods on a set of signals given to the student. The student will show the degree of acquisition of skills relevant to the assignment and provide interpretations of the results. The evaluation (E2) will take into account the submitted report as well as the the suitability and originality of the proposed solution.
• A06 Tutorials. Teacher's office hours are available for students who want to review and discuss the materials and topics presented in both theoretical and practical classes.
  A08 Assessment (2 hours). A set of a final written test and the submission of assignments and tasks. The details can be found in the Assessment Section.

5.3. Syllabus

The course will address the following topics:

• Topic 1. Basics of statistical signal processing.
• Topic 2. Parameter estimation and detection of events. Applications: Averaging, EMG analysis, delays, heart rate variability, event detection, detection of T-wave alternans ... Methods: Parameter estimation. Bias and variance. Optimal estimation (maximum likelihood, least squares, Bayesian methods). optimal detection. MAP criterion. GLRT.
• Topic 3. Optimal and adaptive filtering. Applications: filtering and noise cancellation, source separation, adaptive estimation. Methods: Wiener Filtering, Adaptive Filtering Algorithms (LMS).
• Topic 4. Signal processing methods (PCA, ICA). Applications: single-trial EP Analysis, Separation of fetal ECG.  Methods: Principal component analysis (PCA and transformed KL), Independent component analysis (ICA).
• Topic 5. Time-frequency representation. Applications: Removing noise, signal segmentation, cardiorespiratory coupling. Methods: Short-time Fourier Transform, Spectrogram, wavelet transform.

5.4. Course planning and calendar

Further information concerning the timetable, classroom, office hours, assessment dates and other details regarding this course, will be provided on the first day of class or please refer to the EINA website.

5.5. Bibliography and recommended resources