phy678 - Processing and analysis of biomedical data
Module label | Processing and analysis of biomedical data |
Modulkürzel | phy678 |
Credit points | 6.0 KP |
Workload | 180 h
( Attendance: 56 hrs, Self study: 124 hrs )
|
Institute directory | Institute of Physics |
Verwendbarkeit des Moduls |
|
Zuständige Personen |
|
Prerequisites | Basic signal processing, algebra knowledge |
Skills to be acquired in this module | This course introduces basic concepts of statistics and signal processing and applies them to real-world examples of bio-medical data. In the second part of the course, recorded datasets are noise-reduced, analyzed, and discussed in views of which statistical tests and analysis methods are appropriate for the underlying data. The course forms a bridge between theory and application and offers the students the means and tools to set up and analyze their future datasets in a meaningful manner. |
Module contents | Normal distributions and significance testing, Monte- Carlo bootstrap techniques, Linear regression, Correlation, Signal-to-noise estimation, Principal component analysis, Confidence intervals, Dipole source analysis, Analysis of variance Each technique is explained, tested and discussed in the exercises. |
Literaturempfehlungen | Kirkwood B.R. and Sterne A.C., Essential Medical Statistics: 2nd editition. Blackwell Science. Oxford, 2003; Cho, Z.H. and Singh J. P. J.M.: Foundations of Medical Imaging. John Wiley, New York, 1993; Kutz, J.N. Data-Driven Modeling and Scientific Computation: Methods for complex systems and Big Data. Oxford University Press, Oxford, 2013 |
Links | |
Language of instruction | English |
Duration (semesters) | 1 Semester |
Module frequency | Wintersemester |
Module capacity | unlimited |
Examination | Prüfungszeiten | Type of examination |
---|---|---|
Final exam of module | Exam or presentation or oral exam or homework or practical report |
Lehrveranstaltungsform | Lecture |
SWS | 4 |
Frequency | SoSe oder WiSe |
Workload Präsenzzeit | 56 h |