Enrolment options

ELEC-E5810 - Biosignal Processing D, Lecture, 2.9.2024-14.10.2024

Please note! Course description is confirmed for two academic years, which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.

LEARNING OUTCOMES

After completing the course, the student should be able to

  • Understand and explain the origin of bioelectrical signals and their relevance in clinical applications.
  • Explain sampling and acquisition of continuous-time bioelectrical signals and their conversion to discrete-time representations.
  • Analyse the properties of common deterministic and stochastic signal models, in particular sinusoidal and linear models.
  • Interpret and explain frequency and time-frequency representations of deterministic and stochastic signals originating from biological systems.
  • Formulate mathematical models for real bioelectrical signals and to design and implement processing methods such as smoothing, filtering, and denoising.
  • Use a computer language such as, e.g., Matlab, Python, or Julia, to implement signal processing operations for bioelectrical.

Credits: 5

Schedule: 02.09.2024 - 14.10.2024

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Filip Elvander, Filip Elvander

Contact information for the course (applies in this implementation):

CEFR level (valid for whole curriculum period):

Language of instruction and studies (applies in this implementation):

Teaching language: English. Languages of study attainment: English

CONTENT, ASSESSMENT AND WORKLOAD

Content

  • valid for whole curriculum period:

    - Deterministic and stochastic signals originating from biomedical systems and sensors, such as, e.g., EEG and ECG.

    - Single- and multi-channel signals.

    - Sampling of analog continuous-time signals.

    - Rational models for deterministic signals (FIR/IR) and stochastic signals (AR/MA/ARMA).

    - Frequency and time-frequency representations of signals.

    - Parametric and non-parametric spectral analysis of bioelectrical signals

    - Filtering, denoising, and estimation of model parameters.

Assessment Methods and Criteria

  • valid for whole curriculum period:

    Exam, exercises, homework assignments. See course page in Mycourses.

Workload

  • valid for whole curriculum period:

    - Lectures

    - Tutorials 

    - Self-study

    - Exam

DETAILS

Study Material

  • valid for whole curriculum period:

    Course literature as well as lecture slides. See course page in Mycourses.

Substitutes for Courses
Prerequisites
SDG: Sustainable Development Goals

    3 Good Health and Well-being

FURTHER INFORMATION

Further Information

  • valid for whole curriculum period:

    Teaching Language: English

    Teaching Period: 2024-2025 Autumn I
    2025-2026 Autumn I

Rules of Conduct - Students´ Rights and Responsibilities (in force from 1 August 2020)

Aalto University Code of Academic Integrity and Handling Violations Thereof

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