Topic outline

  • General



    Welcome! In this course, modern signal processing techniques in biomedical applications are discussed. Subjects include different spectral analysis methods (incl. higher-order spectra), adaptive filters, pattern recognition methods, and time-frequency methods. Methods will be covered on the basis of real-life application examples as much as possible.

    The goal is that, after completing this course successfully, you can:

    1. recognise the main properties of a biosignal processing problem and assess what are appropriate techniques to use;  

    2. explain the underlying properties of the these signal processing techniques; 

    3. design components of a full 'signal processing and interpretation' chain for a real-life biomedical problem; 

    4. select and use methods to assess the usefulness/performance of this 'processing chain'; 

    5. understand and discuss the clinical / healthcare requirements for biosignal processing applications


    As pre-requisite it is assumed that you have followed an introductory digital signal processing course earlier. In the exercises we will make use of Matlab (Python support is under development).

    An example of a Basic Signal Processing Course is:

    "ELEC-C5230 - Digitaalisen signaalinkäsittelyn perusteet" and the therein suggested book by Mitra

    From that course, the material covered in Lecture 1-3, first part of lecture 4, are subjects that are assumed to be known for this course, even if we are going through them here as well (but in fast pace). If you have followed similar course on other universities, that is fine as well - discuss with Mark if in doubt.

    Lecturer of the course is: Mark van Gils (mark.vangils@tuni.fi )

    Course assistant is: Ivan Radevici ( ivan.radevici@aalto.fi )


    Lecture Schedule 


    There will be 8 lecture sessions, each of 3 hours. The first lecture will be on 14 Sept 2021

    Thus, Tuesdays 11:15 - 14:00, 14.9.21 to 23.11.21 with some weeks skipped. See schedule below.

    We will agree on breaks etc during the first lecture
     



    lecture

    date

    contents

    1

    14.09.21

    Introduction and recapitulation of essential techniques

    2

    21.09.21

    Frequency analysis

    3

    28.09.21

    Treatment of non-stationary signals, adaptive filters

    4

    12.10.21

    Time-frequency analysis, wavelets

    5

    19.10.21

    Non-linear signals and methods

    6

    02.11.21

    Regularity/complexity analysis

    7

    09.11.21

    Pattern recognition

     823.11 .21From signal processing to information interpretation: decision support