Biomedical signal and image processing

Computer Science and Mathematics, Second Cycle
1 in 2 year
slovenian, english

Franc Jager

Hours per week – 1. semester:
Content (Syllabus outline)

Introduction to biomedical signals and images like: electrocardiographic signals (ECG), neurophysiological signals (EEG, EMG), medical images (CT, MRI, ultrasound) and introduction to modern computer technologies in selected clinical settings.
International standardized reference databases of medical samples (MIT/BIH BD, LTST DB, TPEHG DB, EEGMMI DS, Internet servers).
Feature extraction (time domain, Fourier transform, wavelets, principal components – Karhunen-Loeve transform, feature representations).
Noise extraction (linear procedures in time domain, feature space procedures, weighted averaging, robust approaches).
Spectral analysis and characterization of samples and features (time-frequency representations, spaces of diagnostic and morphologic features).
Analysis of time series and nonstationary signals.
Modelling (linear stochastic and non-linear models, autoregressive modelling).
Event detection, clustering and classification (techniques in time domain and in feature space).
Image processing and processing of 3-dimensional CT and MRI images with the aim of noise reduction, conture extraction, and segmentation and visualization of anatomical structures.
Performance evaluation of biomedical computer systems (metrics, protocols, predictioning performance in real world, assessing robustness, standards).
Laboratory work:
Practical work will be performed in the form of project work in suitable equipped student laboratories. Students in the scope of projects independently implement procedures. Obligatory work on projects allows deepen and critical understanding of the subject topics and stimulates to independence and creativity.


Kayvan Najarian, Robert Splinter, Biomedical Signal and Image Processing, CRC Press., 2012.
Advanced Methods and Tools for ECG Data Analysis, Clifford G, Azuaje F, McSharry PE (editors), Artech House, Inc., 2006.
Sornmo L, Laguna P, Biological Signal Processing in Cardiac and Neurological Applications, Elsevier, Inc., 2005
Gonzales Rafael C., Woods Richard E. Digital Image Processing, Pearson Prentice Hall., 2008.
Selected articles from journals: IEEE Transactions on Biomedical Engineering, Medical and Biological Engineering and Computing, Physiological Measurements, PLOS ONE.

Objectives and competences

Objectives of the course are to represent students of computer and information science the basics of biomedical signal and image processing with the emphasis on the problems of biomedical researches and clinical medicine. The course covers principles and procedures for processing of deterministic signals, stochastic signals and images. The course topics cover signal acquisition, standardized databases of signal samples, filtering, feature extraction, visualization, spectral analysis, modelling, event detection, clustering, classification, image analysis and performance evaluation of automatic procedures.
Competences:The ability to define, understand and solve creative professional challenges in computer and information science, The ability of knowledge transfer and writing skills in the native language as well as a foreign language, The ability to apply acquired knowledge in independent work for solving technical and scientific problems in computer and information science, The ability to upgrade acquired knowledge, The ability to understand and apply computer and information science knowledge to other technical and relevant fields.

Intended learning outcomes

After the completion of the course, students should be able to:- know computer technologies and automatic procedures of biomedical signal and image analysis to develop automatic analyzers in help to diagnose,- analyze biomedical signals (electrocardiogram, electromyogram,electroencephalogram) in frequency domain,- develop algorithms for detecting and classifying events inbiomedical signals,- analyze biomedical 2D and 3D tomography images,- develop algorithms for conture extraction, and segmentation andvisualization of anatomic structures in tomographic images,- evaluate performance and robustness of biomedical computersystems.

Learning and teaching methods

Lectures, laboratory work with active cooperation, seminar type of work on individual projects. Special emphasize is given to prompt study and prompt work on laboratory work and seminars.


Continuing (homework, midterm exams, project work)
Final (written and oral exam)
grading: 5 (fail), 6-10 (pass) (according to the Statute of UL)

Lecturer's references

Pet najpomembnejših del:
AMON, M, JAGER, F. Electrocardiogram ST-segment morphology delineation method using orthogonal transformations. PloS one, Vol. 11(2), pp. 1-18, 2016.
TROJNER-BREGAR, A, LUČOVNIK, M, VERDENIK, I, JAGER, F, GERŠAK, K, GARFIELD, R. Uterine electromyography during active phase compared with latent phase of labor at term. Acta obstetricia et gynecologica Scandinavica, Vol. 95(2), pp. 197-202, 2016.
PANGERC, U, JAGER, F. Robust detection of heart beats in multimodal records using slope- and peak-sensitive band-pass filters. Physiological measurement, Vol. 36(8), pp. 1645-1664, 2015.
JAGER, F. Two chapters in Advanced Methods and Tools for ECG Data Analysis, G. Clifford, F. Azuaje, P.E. McSharry (editors), Artech House, Inc. 2006.
JAGER, F, TADDEI, A. MOODY G B, EMDIN, M, ANTOLIČ, G, DORN R, SMRDEL A, MARCHESI, C, MARK, R G. Long-term ST database: a reference for the development and evaluation of automated ischaemia detectors and for the study of the dynamics of myocardial ischaemia. Med. Biol. Eng. Comput., Vol. 41, pp.172-182, 2003.Celotna bibliografija je dostopna na SICRISu:,id=4815.