All students must validate 30 ECTS per semester.We offer two speciality tracks:

Track 1: Vision and Applications
The first track focuses on various applications of computer vision: biomedical applications, people detection, object tracking, computational photography.

Track 2: Vision and devices
The second track focuses on devices to capture images (intelligent sensors, medical imaging systems) and to visualise and interact with them (augmented reality)

 

 

 

Course name: Biomedical Signal Processing Credits: 4

  • Class type: lecture/practical
  • Hours per week: 2/1
  • Type of the exam: oral exam

Content

To learn the basic techniques in signal processing that are relevant for biomedical signals and to illustrate and learn the use of these techniques.

Throughout the lectures, we will encounter the following topics: biomedical signal genesis; signal representation; signal decomposition; source separation; AR estimation; Fourier analysis; frequency-time analysis; wavelets; sparse decomposition; data fusion; nonlinear methods. Examples of signal modalities we will consider: pulse oximetry, phonocardiography, ECG, EEG.

Required reading

downloadable slides of the lectures
Rangayyan: Biomedical Signal Analysis: A Case-Study Approach,
Sörnmo and Laguna: Bioelectrical Signal Processing in Cardiac and Neurological Applications

Recommended reading

  • Lecturer (name, position, degree): Dr. Miklós Gyöngy, associate professor, PhD

The European Credit Transfer and Accumulation System (ECTS) is a student-centred system based on the student workload required to achieve the objectives of a programme of study. Its aim is to facilitate the recognition of study periods undertaken abroad by mobile students through the transfer credits. The ECTS is based on the principle that 60 credits are equivalent to the workload of full-time student during one academic year.