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EEEN30160

Academic Year 2021/2022

Biomedical Signal Processing (EEEN30160)

Subject:
Electronic & Electrical Eng
College:
Engineering & Architecture
School:
Electrical & Electronic Eng
Level:
3 (Degree)
Credits:
5
Module Coordinator:
Assoc Professor Giacomo Severini
Trimester:
Autumn
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

An understanding of biomedical signal processing is at the core of many widely used diagnostic and therapeutic biomedical devices such as ECGs, pacemakers, cochlear implants, ventilatory support systems, etc. In this module, students will learn how biomedical signals are represented in digital format, and how they can be digitally processed through mathematical techniques in order to extract useful information for understanding physiological systems and/or diagnosis. Fundamental processing tools ranging from signal averaging to filtering and frequency-domain analysis will be covered.
Students will learn these principles and techniques through an even mixture of lectures and computer lab exercises in Matlab, and will carry out a short software-based research project to process a biomedical signal.

About this Module

Learning Outcomes:

On successful completion of this subject the student will be able to:
• Explain basic principles of how an analog sensor signal can be converted reliably to a digital format for processing
• Translate signal representations between the time and frequency domains
• Design and implement digital filters and use them to remove noise or improve signal detection
• Describe and know how to address the core challenges in biomedical signal analysis, such as the interference of noise and artifacts in the signals
• Perform basic feature extraction and pattern classification for diagnostic applications

Indicative Module Content:

- Basics of Biomedical signal processing
- Fourier Transform
- Sampling
- Linear systems
- Z-transform
- Digital Filters design
- De-noising signals and signal manipulation
- Feature extraction from biomedical signals
- Introduction to classification and machine learning
- Matlab programming

Student Effort Hours:
Student Effort Type Hours
Lectures

23
Computer Aided Lab

20
Autonomous Student Learning

60
Total

103

Approaches to Teaching and Learning:
The module will be made up of Lectures, labs and a final project.
The lectures will be pre-recorded and available online each week. The scheduled lecture time will be used for addressing questions regarding the material, either face-to-face or online. There will be biweekly lab assignments. Lab time can be either face to face or online, with no mandatory attendance. During the labs, students will work on their assignments and will have the chance to ask questions to the Lecturer and the TAs.

Requirements, Exclusions and Recommendations
Learning Requirements:

Sufficient mathematics background to be comfortable with integral calculus, probability theory, and basic linear algebra


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Assignment: Assignments and class activities Throughout the Trimester n/a Graded Yes
50
 Yes
Examination: 2-hour End of Semester Exam 2 hour End of Trimester Exam No Graded Yes
30
 Yes
Project: Project Throughout the Trimester n/a Graded No
20
 No

Carry forward of passed components
No
 

Resit In Terminal Exam
Spring Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

Name Role
Dr Elaine Corbett Lecturer / Co-Lecturer
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