BI2SP22-Biomedical signal processing and feedback systems

Module Provider: School of Biological Sciences
Number of credits: 20 [10 ECTS credits]
Level:5
Terms in which taught: Autumn / Spring term module
Pre-requisites: BI1MA17 Mathematics
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2023/4

Module Convenor: Prof Ying Zheng
Email: ying.zheng@reading.ac.uk

Module Co-convenor: Dr Sillas Hadjiloucas
Email: s.hadjiloucas@reading.ac.uk

Type of module:

Summary module description:

This module will introduce students to the fundamentals of processing biomedical signals, including analysing signals in both the time and the frequency domain. It will also familiarise students with feedback systems which are essential for almost all body functions and processes. The importance of system stability will be discussed.Ìý

Aims:

This module aims to introduce how Fourier and Laplace transform techniques can be used to describe and analyse signals, and how these techniques can also be used to describe systems as transfer functions and analyse systems in the frequency domain. Students will be introduced to the concept of feedback systems and be able to analyse the stability of a feedback system from its open-loop transfer function. Applications of signal processing techniques and linear systems theory to solving biomedical problems will be emphasised.

Assessable learning outcomes:

By the end of the module students will be expected to:

• Describe and analyse signals in both the time and the frequency domain, convert a simple time domain function into its Laplace transform and vice versa


• Understand the properties of different types of filters, use Matlab to design filters given frequency domain specifications, and apply filters appropriately to analyse biomedical signals


• Calculate the power spectrum of biomedical signals in Matlatb, perform correlation and coherence analysis in Matlab given multiple biomedical signals and able to provide clear interpretations


• Understand the concept of the Nyquist frequency, able to select appropriate sampling frequency of a given continuous system, and analyse simple discrete systems using the z-transform


• Understand the dynamic characteristics of linear first and second order systems, establish mathematical models and transfer functions of simple systems, and analyse stability of feedback systems


• Perform frequency domain analysis using Bode diagrams, understand the principle of feedback control and describe the action potential of neurons by a simple first order system

Additional outcomes:

Students will be familiar with tools for data analysis such as Matlab. Students will also appreciate the breadth of the subject of signal processing and that of feedback systems and see that techniques described in control system analysis can be used in analysisn biomedical signals.

Outline content:

Laplace transforms, inverse Laplace transforms and their application to solving differential equations. Fourier series and Fourier Transforms. Sampling theory and Nyquist frequency. Autocorrelation, correlation, convolution and their properties. Principles of filter design. Low-pass, high-pass, band-pass and notch filters. Order and band-width of a filter. Use Matlab to filter biomedical signals with a range of band-width specifications. Power spectral density analysis. Random noise and its power spectrum. Coherence analysis. An introduction to the z-transform. Difference equations.

Linear feedback systems. Block diagrams. Transfer function of linear systems. Modelling of simple Resistor-Capacitor-Inductor circuits. First order and second order systems. Time constant, damping ratio and natural frequency. PID controllers. Stability of feedback systems. Poles and zeros. Root locus analysis, State-space representation. Frequency domain analysis of linear time-invariant systems. Simple models of neurons.

Brief description of teaching and learning methods:

The module comprises 4 hr lectures per week for 10 weeks, associated with 8 hr Matlab tutorials on signal processing and feedback systems using Simulink. Matlab sessions are used to r