The course aims at providing students with the basic methodological tools to face problems of signal processing, with particular reference to those related to clinical diagnosis. The course is mainly methodological, so it is necessary that the student is able to manage with friendlyness mathematical and physical basics in order to address the topics. The student will acquire the knowledge for basic processing of more general use: the relationship between dynamic models in continuous time (analog) and discrete-time numerical calculations in environment (digital), the basics of the frequency description of signal and image processing in this domain (filtering), both analog and digital design methods of linear digital filter.
Course Prerequisites
Basics of Math and Physics,
Teaching Methods
Lectures with exercises in computer rooms for the use of specific software
Assessment Methods
The final proof will consist of a test to be carried out in a computer classroom (B2, C2-C3 or D8). The test will include written exercises, computer exercises using the specific software used in the course and theory questions. The possible results for the student will be "Approved" or "Not Approved"
Texts
Willis J. Tompkins “Biomedical Digital Signal Processing”, Prentice Hall, 1993. Semmlow J.L., “Biosignal and Medical Image Processing”, CRC Press, 2009.
Contents
1. Introduction to biosignals with examples. 2. Brief introduction to complex numbers 3. Analog signals and systems: summary of the Fourier transform and the Laplace transform; frequency response. 4. Signals and discrete systems: sampling of signals, Sampling theorem (Shannon), reconstruction of a sampled signal, A / D conversion and quantization; discrete time signals and sequences, signals originating from invariant linear systems; Discrete Time Fourier Transform; Z-transform for sampled signals; inverse Z-transform. 5. Digital signal conditioning: non-recursive digital filters (FIR); synthesis of derivative filters; frequency response and design of FIR filters (time windows, frequency sampling, zero placement) ; recursive filters (IIR); synthesis of IIR filters from analog filters; elimination of network interference, notch filter; notes on the accuracy of the FIR and IIR filters; optimization of digital filters (laboratory). 6. Spectral analysis: introduction to autoregressive models; power spectra and energy spectra; spectrum estimation by numerical methods.
