Stud.IP Uni Oldenburg
University of Oldenburg
18.10.2019 08:14:50
phy678 - Processing and analysis of biomedical data (Complete module description)
Original version English Download as PDF
Module label Processing and analysis of biomedical data
Module code phy678
Credit points 6.0 KP
Workload 180 h
(Attendance: 56 hrs, Self study: 124 hrs)
Faculty/Institute Institute of Physics
Used in course of study
  • Master's Programme Engineering Physics (Master) >
  • Master's Programme Engineering Physics (Master) >
Contact person
Module responsibility
Entry requirements
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.
Reader's advisory
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
Modullevel MM (Mastermodul / Master module)
Modulart Wahlpflicht / Elective
Lern-/Lehrform / Type of program Lecture: 2hrs/week; Exersise: 2hrs/week
Vorkenntnisse / Previous knowledge
Examination Time of examination Type of examination
Final exam of module
Exam or presentation or oral exam or homework or practical report
Course type Lecture
SWS 4.00
Frequency SuSe or WiSe
Workload attendance 56 h