Course

Biomedical Data Analysis (ELE922)

The course starts with an introduction to biomedical signals (or images).  Furthermore the following topics are covered: basic concepts from time- frequency domain representation; noise cancellation; detection of events and objects; characterisation of shape- and complexity for waveforms and objects; frequency domain characterisation; machine learning and decision support.


Course description for study year 2020-2021. Please note that changes may occur.

See course description and exam/assesment information for this semester (2024-2025)

Semesters

Facts

Course code

ELE922

Credits (ECTS)

10

Semester tution start

Spring

Language of instruction

English

Number of semesters

1

Exam semester

Spring

Time table

View course schedule

Content

Theoretical: Introduction to biomedical signals (or images); basic concepts from time- frequency domain representation; noise cancellation; detection of events and objects; characterisation of shape- and complexity for waveforms and objects; fequency domain characterisation; machine learning and decision support.

Laboratory activities: Introduction to data analysis tools relevant to the theoretical part of the course.

Learning outcome

Knowledge: The subject aims to provide insight into concepts and skills important for handling problems in biomedical data analysis. Furthermore, insight into important applications of data analysis with examples from signal processing, image processing and machine learning.  The subject shall provide competencies enabling the candidate to understand and apply research methodology used by researchers with a technological background.  This will enable a more efficient collaboration between clinicians and technologists and contribute to translatory research. Skills: The student will also be able to handle basic data analysis tools like MATLAB or Python to handle the type of problems described above. An introduction will give the basics in programming with use of control structures.  The completion of the laboratory exercises in the course will depend on the student having acquires adequate programming skills. General competence: At the completion of the course the student will be able to recognize problems which can be handled by data analysis methods.  Furthermore, the student will be able to use the subject terminology of the course to define a problem  precisely. The solution to the problem implies extraction of relevant information (e.g. for diagnosis) from a biomedical signal (or image) and use this information for decision support.  This can be a diagnostic or therapeutic decision.The student also has to be able to handle various techniques for noise reduction and characterization of events and/or states in the biomedical signal (or objects in images).

Required prerequisite knowledge

None

Exam

Course teacher(s)

Course teacher:

Stein Ørn

Course coordinator:

Trygve Christian Eftestøl

Course teacher:

Kjersti Engan

Head of Department:

Tom Ryen

Course coordinator:

Ketil Oppedal

Course teacher:

Ketil Oppedal

Method of work

Guided self-tuition.  Lectures can in some instances be arranged.

Open for

Technology and Natural Science - PhD programme

Course assessment

form and/or discussion according to current guidelines.

Literature

Book

Biomedical signal analysis : a case-study approach Rangayyan, Rangaraj M., New York, Wiley-Interscience, xxxv, 516 s., c2002, isbn:0471208116,

Book

Biomedical image analysis Rangayyan, Rangaraj M., Boca Raton, Fla., CRC Press, xxxvii, 1272 s., c2005, isbn:0849396956,

Book

Bioelectrical signal processing in cardiac and neurological applications Sörnmo, Leif, Laguna, Pablo, Amsterdam, Elsevier Academic Press, XIII, 668 s., c2005, isbn:9780124375529; 0124375529,

Contact

Course teacher:

Stein Ørn

Course coordinator:

Trygve Christian Eftestøl

Course teacher:

Kjersti Engan

Head of Department:

Tom Ryen

Course coordinator:

Ketil Oppedal

Course teacher:

Ketil Oppedal

Offered by

Faculty of Science and Technology

Department of Electrical Engineering and Computer Science

The course description is retrieved from FS (Felles studentsystem). Version 1