Skip to main content Skip to navigation

Module Catalogue

ES97K-15 Computational Intelligence in Biomedical Engineering

Department
School of Engineering
Level
Taught Postgraduate Level
Module leader
Liam Weaver
Credit value
15
Module duration
10 weeks
Assessment
100% coursework
Study location
University of Warwick main campus, Coventry

Download as PDF

Introductory description

ES97K-15 Computational Intelligence in Biomedical Engineering

Module web page

Module aims

To further enhance the students’ skills in biomedical signal and data processing with the principles of computational intelligence as applied to biomedical engineering.

The module will provide the student with a firm grounding in methods and tools for extracting information from biomedical signals and data.

The module will introduce the practical implementation of computational intelligence techniques applied to digitally acquired biomedical signals.

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

  • Introduction
    o Fundamentals
    o Basic Signal Processing Techniques
    o The need for Computational Intelligence (CI) in BME
  • Artificial Neural Networks (ANNs)
    o Basics
    o Architectures
    o Optimization and Learning
    o Popular ANN architectures and learning algorithms
  • Support Vector Machines (SVM)
    o Classifiers and Classification
    o Support Vector Classifiers
    o Support Vector Regression
    o Training SVMs
  • Hidden Markov Models (HMMs)
    o The Markov Chain
    o The Hidden State
    o Types of HMMs
  • Fuzzy Sets and Fuzzy Logic
    o Fuzzy Sets
    o Fuzzy Membership Functions
    o Fuzzy Operations
    o Applications of Fuzzy Systems
  • Decision Trees and Random Forests
    o Training Decision Trees
    o Ensemble Learning
    o Applying Random Forests
  • Applications of CI to BME case studies

Learning outcomes

By the end of the module, students should be able to:

  • Demonstrate a systematic knowledge of the complex physical and physiological principles that underpin the measurement of biomedical signals/ data. [M1]
  • Demonstrate an advanced understanding of the principles of computational intelligence. [M1, M3]
  • Systematically apply computational intelligence techniques to extract relevant information from biomedical signal measurements/ data. [M1, M2, M3]
  • Critically assess the appropriateness of different computational intelligence techniques for various problems in the field.
  • Evaluate the effectiveness of techniques applied to biomedical signals/ data against specific benchmarks.
  • Evaluate and critique the application of CI techniques within current research literature [M1, M2, M4] .

Indicative reading list

Reading lists can be found in Talis

Subject specific skills

Basic understanding of MATLAB

Transferable skills

Teamworking, Communication

Study time

Type Required Optional
Lectures 8 sessions of 2 hours (11%)
Seminars 3 sessions of 2 hours (4%)
Practical classes 5 sessions of 2 hours (7%) 1 session of 2 hours
Private study 118 hours (79%)
Total 150 hours

Private study description

Guided Independent Learning 118 hours

Costs

No further costs have been identified for this module.

You must pass all assessment components to pass the module.

Assessment group A3
Weighting Study time Eligible for self-certification
Assessment component
In-class test on Lab Exercises 50% No

1.5 hour in-class test
Reassessment component is the same
Assessment component
Group Project - Computational Intelligence 50% No

15mins Presentation + 5mins Q&A. Includes peer review.
Reassessment component
Individual Project - Computational Intelligence No

10mins Presentation + 5mins Q&A
Feedback on assessment

Coursework marked with detailed comments
Face-to-face feedback in practicals
Cohort level feedback on examinations

Courses

This module is Core for:

  • TESA-H1CA Postgraduate Taught Diagnostics, Data and Digital Health
    • Year 1 of H1CA Diagnostics, Data and Digital Health
    • Year 1 of H1CB Diagnostics, Data and Digital Health (Medical Diagnostics)
    • Year 1 of H1CC Diagnostics, Data and Digital Health (Medical Imaging)

This module is Optional for:

  • Year 4 of UESA-H116 MEng Engineering with Exchange Year
  • Year 5 of UESA-H115 MEng Engineering with Intercalated Year
  • Year 1 of TESA-H800 Postgraduate Taught Biomedical Engineering

This module is Option list A for:

  • Year 4 of UESA-H163 MEng Biomedical Systems Engineering
  • Year 5 of UESA-H164 MEng Biomedical Systems Engineering with Intercalated Year
  • Year 4 of UESA-H114 MEng Engineering

This module is Option list B for:

  • Year 4 of UESA-H163 MEng Biomedical Systems Engineering
  • Year 4 of UESA-HH31 MEng Systems Engineering
  • Year 4 of UESA-HH33 MEng Systems Engineering with Exchange Year
  • Year 5 of UESA-HH32 MEng Systems Engineering with Intercalated Year