SubjectsSubjects(version: 965)
Course, academic year 2019/2020
  
Analysis of Multidimensional Biomedical Signals - AP445003
Title: Analysis of Multidimensional Biomedical Signals
Guaranteed by: Department of Computing and Control Engineering (445)
Faculty: Faculty of Chemical Engineering
Actual: from 2019 to 2020
Semester: both
Points: 0
E-Credits: 0
Examination process:
Hours per week, examination: 3/0, other [HT]
Capacity: winter:unlimited / unknown (unknown)
summer:unknown / unknown (unknown)
Min. number of students: unlimited
State of the course: taught
Language: English
Teaching methods: full-time
Level:  
Note: course is intended for doctoral students only
can be fulfilled in the future
you can enroll for the course in winter and in summer semester
Guarantor: Vyšata Oldřich MUDr. Ph.D.
Mareš Jan prof. Ing. Ph.D.
Interchangeability : P445003
Examination dates   Schedule   
Annotation -
The subject deals with modern methods and tool in biomedisal and medical field (CT, NMR,...). Students will solve several case studies including real applications. For the exam it is necessary to propose a draft of publication form the field of disertation thesis.
Last update: Pátková Vlasta (08.06.2018)
Course completion requirements -

3 individual projects: 0 - 25 bodů

Oral exam: 0-75 bodů

100-90 A, 89-80 B, 79-70 C, 69-60 D, 59-50 E, méně než 50 F.

Last update: Pátková Vlasta (08.06.2018)
Literature -

Z: K. Narjarian, A. Splinter: Biomedical Signal and Image Processing, Taylor and Francis, CRC, 2006.

Z: D. Caramella, C. Bartolozzi, A.L. Baert: 3D Image Processing: Techniques and Clinical Applications

D: Rafael C. Gonzalez and Steven L. Eddins: Digital Image Processing Using MATLAB, 2nd ed. D: S.M. Dunn, A. Constantinides, P.V. Moghe: Numerical Methods in Biomedical Engineering, 2006.

Last update: Pátková Vlasta (08.06.2018)
Teaching methods -

lectures, project and solving of case stidies

Last update: Pátková Vlasta (08.06.2018)
Syllabus -

1) Advanced CT image analysis

2) Methods of NMR analysis

3) EEG data analysis

4) Biomedical signals modeling - overview

5) Adnaced biomedical signal visualisation in time and frequency domain

6) Chaos and dynamical analysis

7) Biomedical data formats

8) PWA model of EEG data

9) Modeling of neuron electrical activity

10) EEG synchronisation

11) EMG, detection, localisation and classification

12) Comunication models in biomedical objects

13) Biostaistics

14) Advanced modeling in biology and physiology

Last update: Pátková Vlasta (08.06.2018)
Learning resources -

www.honeywellprocess.com/

www.mathworks.com/

www.ni.com/

Last update: Pátková Vlasta (08.06.2018)
Learning outcomes -

Students will be able to:

  • model advanced biological processes,
  • proces 1D, 2D and 3D signals,
  • prepare the experiments for selected biomedical data acquisition
Last update: Pátková Vlasta (08.06.2018)
Entry requirements -

none

Last update: Pátková Vlasta (08.06.2018)
Registration requirements -

none

Last update: Pátková Vlasta (08.06.2018)
 
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