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Biomedical Data Analysis (ELE922)

The course is for students with medical background. 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 2022-2023

Facts
Course code

ELE922

Version

1

Credits (ECTS)

10

Semester tution start

Spring, Autumn

Number of semesters

1

Exam semester

Spring, Autumn

Language of instruction

English, Norwegian

Content

Theoretical: Introduction to biomedical signals (and/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 students with clinical background 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 candidate's project will decide whether the subject will emphasize signal or image processing.  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
Form of assessment Weight Duration Marks Aid
Project and oral presentation 1/1 Passed / Not Passed

Course teacher(s)
Course teacher: Stein Ørn
Course coordinator: Ketil Oppedal
Course coordinator: Trygve Christian Eftestøl
Course teacher: Kjersti Engan
Head of Department: Tom Ryen
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
The syllabus can be found in Leganto