Biomedical Digital Signal Processing

CMPUT 605

(Summer 2018)

General Information

Instructor: Pierre Boulanger
Tel: 780-492-3031
Email: pierreb@cs.ualberta.ca

URL: www.cs.ualberta.ca/~pierreb
Office: 411 Athabasca Hall
Office hours: By appointment only.

Lectures: Every Friday 14h00 to 15h00 in Room ATH 411

Course Description

This class addresses the representation, processing, and analysis of biomedical discrete time signals like ECG, EEG, etc. The major concepts covered include: biological basis of biomedical signals, discrete-time processing of continuous-time signals; decimation, interpolation, and sampling rate conversion, time-and frequency-domain design techniques for recursive (IIR) and non-recursive (FIR) filters; linear prediction; discrete Fourier transform, FFT algorithm; short-time Fourier analysis and filter banks; multivariate techniques; Wavelet Transform; Cepstral analysis, Wiener and Kalman Filters, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and various applications.

Purpose

To introduce Computer Scientists to advanced biomedical signal processing theory that can be applied to various projects involving multi-dimensional datasets. The emphasis is based on stochastic view of multi-dimensional signals and how to extract useful and reliable information from those signals.

Prerequisites

Basic statistical analysis is preferred. It is also assumed that you already have some familiarity with C Language.

Homework

Homework will generally be handed out in lecture and be due in lecture on the following week.

There will be approximately 4 problem sets. Don't be misled by the relatively few points assigned to homework grades in the final grade calculation. While the grade that you get on your homework is at most a minor component of your final grade, working the problems is a crucial part of the learning process and will invariably have a major impact on your understanding of the material.

Course Project

There will be an individual semester project, culminating in a final 8 pages report in IEEE format and a presentation at a day workshop. Progress and check points before the final due date will count toward the final grade.

Course Grade

The final grade for the course is based on our best assessment of your understanding of the material, as well as your commitment and participation. The problem sets and final projects are combined to give a final grade:

ACTIVITIES	Weight
Final Project	60%
Problem Sets	40%

Lecture Notes

Course calendar.
LECTURE DATE	TOPICS	Extras
Week 1	Course Overview	Read Clifford Chapter 1
Week 2	Discrete-Time Signals and Systems Physiological Origin of Biomedical Signals
Week 3	Analysis of ECG Signals Read Clifford Chapter 2 Read Clifford Chapter 3 Read Clifford Chapter 5	Assignment 1
Week 4	Discrete Fourier Analysis	.
Week 5	Z-Transform	Assignment 2
Week 6	Wavelet Transform	Extra notes on Wavelet
Week 7	Infinite and Finite Impulse Filter	Digital Filter Design
Week 8	QRS Detection QT Dispersion Algorithm Heart Rate Variability	Assignment 3
Week 9	Wiener Filters Kalman Filters	More on Wiener Filter Assignment 4
Week 10	Adaptive Filters Introduction
Week 11	Convolutional Neural Net Understanding How CNN Works CNN for ECG Analysis Adaptive Filtering and Neural Net
Week 12	Recurrent Neural Networks RNN for ECG Analysis
Week 13	Particle Filters
Week 14	Project Presentation and Final Report	Send report to pierreb@ualberta.ca

Extra Material for the Course

o B. Champagne and F. Lebeau, Discrete Time Signal Processing, Course note of ECSE-412, Winter 2004

o MATLAB Primer

o MATLAB Tutorials