BI2SF1: Biomedical Signal Processing and Feedback Systems

Module code: BI2SF1

Module provider: School of Biological Sciences

Credits: 20

Level: 5

When you’ll be taught: Semester 1

Module convenor: Professor Ying Zheng , email: ying.zheng@reading.ac.uk

Module co-convenor: Dr Sillas Hadjiloucas, email: s.hadjiloucas@reading.ac.uk

Pre-requisite module(s): BEFORE TAKING THIS MODULE YOU MUST TAKE BI1MA3 OR TAKE BI1MA17 (Compulsory)

Co-requisite module(s):

Pre-requisite or Co-requisite module(s):

Module(s) excluded:

Placement information: NA

Academic year: 2025/6

Available to visiting students: Yes

Talis reading list: No

Last updated: 14 July 2025

Overview

Module aims and purpose

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. Applications of signal processing techniques and linear systems theory to solving biomedical problems will be emphasised.Ìý

Module learning outcomes

By the end of the module, it is expected that students will be able to:Ìý

1. Describe and analyse signals in both the time and the frequency domain, convert a simple time domain function into its Laplace transform and vice versaÌý
2. Understand the properties of different types of filters, use Matlab to design filters given frequency domain specifications, and apply filters appropriately to analyse biomedical signalsÌý
3. 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 interpretationsÌý
4. Explain the concept of the Nyquist frequency, be able to select the appropriate sampling frequency of a given continuous system, and analyse simple discrete systems using the z-transformÌý
5. Describe the dynamic characteristics of linear first and second order systems, establish mathematical models and transfer functions of simple systems, and analyse stability of feedback systemsÌý
6. Perform frequency domain analysis using Bode diagrams, discuss the principle of feedback control and describe the action potential of neurons by a simple first order systemÌý

Module content

Laplace transforms, inverse Laplace transforms and their application to solving differential equations.ÌýÌý

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.Ìý

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.ÌýÌý

Structure

Teaching and learning methods

The module comprises 4 hours of lectures per week for 10 weeks, associated with 8 hours Matlab tutorials on signal processing and feedback systems, and 4 hours of practical sessions on spectrum analysis and filter design. Matlab sessions are used to reinforce the relevant lectures.

Study hours

At least 40 hours of scheduled teaching and learning activities will be delivered in person,