Signal Processing and AI for Biomedical Engineering - Interactive curriculae of TU Ilmenau
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| module properties module number 201388 - common information | |
|---|---|
| module number | 201388 |
| department | Department of Computer Science and Automation |
| ID of group | 2225 (Data Processing in the Life Sciences) |
| module leader | Prof. Dr. Patrique Fiedler |
| language | Deutsch |
| term | Wintersemester |
| previous knowledge and experience | - Signals and Systems - Fundamentals of biosignal processing - Fundamentals of Statistics - Anatomy and physiology - Electro- and Neurophysiology - Fundamentals of Measurement electronics - Imaging in biomedical engineering |
| learning outcome | Students are familiar with measurement systems, signal characteristics and technological solutions in selected areas of diagnostics and therapy, such as electrography, oxygen saturation, bioimpedance and electrical current therapy. . Students are able to apply special methods of signal processing, analysis and detection, incl. selected approaches of AI, in interdisciplinary areas of biosignal processing and, if necessary, adapt and develop them. . Students have subject-specific skills acquired in courses, especially in the exercises, in teams through discussion and experimental investigations on real biosignals. |
| content | Theory, methodology and approaches to pulse oximetric determination of oxygen saturation in the blood, SpO2 . Electrography: overview of electrographic recording methods, measurement principles, signal analysis and diagnostic value: EGG, EOlfG, GEP, ECochG, EHG . ECG: recording, processing, computer-aided signal detection and curve measurement, pathological patterns and suggested diagnosis . Bioimpedance: theory and methodology of electrically based metrological recording, aspects of the measurement setup, recording and evaluation of the plethysmographic curve . Detection of biosignals: Theory of signal detection, energy and matched detector, classification methods using AI, application examples on EEG and ECG . Electrotherapy: effect of low-frequency and high-frequency electrical current . - Signal forms for electrotherapy: galvanization, iontophoresis, diadynamics, high-voltage current, TENS, faradic currents, electrode systems and technologies. |
| media of instruction and technical requirements for education and examination in case of online participation | Transparencies with projector for lectures, blackboard, computer simulations. Whiteboard and computing cabinet in seminars. |
| literature / references | Bronzino, J. D. (Ed.): The Biomedical Engineering Handbook, Vol. I + II, 2nd ed., CRC Press, Boca Raton 2000 2. Husar, P.: Electrical Biosignals in Biomedical Engineering , Springer, 2023, 1st edition. 3. Akay M.: Time-Frequency and Wavelets in Biomedical Signal Processing. IEEE Press, 1998 4. Bendat J., Piersol A.: Measurement and Analysis of Random Data. John Wiley, 1986. 5. Proakis, J.G, Manolakis, D.G.: Digital Signal Processing, Pearson Prentice Hall, 2007 |
| evaluation of teaching | |
| Details reference subject | |
|---|---|
| module name | Signal Processing and AI for Biomedical Engineering |
| examination number | 2200916 |
| credit points | 5 |
| SWS | 12 (2 V, 1 Ü, 9 P) |
| on-campus program (h) | 135 |
| self-study (h) | 15 |
| obligation | obligatory module |
| exam | written examination performance, 90 minutes |
| details of the certificate | |
| link to Moodle course | |
| teacher | Prof. Dr. Patrique Fiedler |
| signup details for alternative examinations | |
| maximum number of participants | |