BIOMEDICAL SIGNAL PROCESSING - 2017/8

Module code: ENG3168

Module provider

Mechanical Engineering Sciences

Module Leader

ABASOLO D Dr (Mech Eng Sci)

Number of Credits

15

ECT Credits

7.5

Framework

FHEQ Level 6

JACs code

B800

Module cap (Maximum number of students)

N/A

Module Availability

Semester 1

Overall student workload

Independent Study Hours: 106

Lecture Hours: 24

Laboratory Hours: 22

Assessment pattern

Assessment type Unit of assessment Weighting
Examination EXAMINATION 2 Hours 55%
Coursework COURSEWORK 1 20%
Coursework COURSEWORK 2 25%

Alternative Assessment

N/A

Prerequisites / Co-requisites

Completion of the progress requirements of Level FHEQ5

Module overview

A biomedical engineer must have a qualitative understanding of the importance of biomedical signal processing. Furthermore, the student should be able to apply fundamental signal processing concepts quantitatively to biomedical engineering problems. This module builds a basic understanding of signal processing concepts relevant to biomedical engineering.

Module aims

An introduction to biomedical signals and biomedical signal acquisition

A systematic understanding and critical awareness of the importance of biomedical signal processing as an essential component of knowledge for a Medical Engineer

A comprehensive understanding of the importance of noise and their sources

A systematic understanding of spectral analysis techniques and their advantages and disadvantages

An introduction to time-frequency analysis and its need in biomedical signal processing

An introduction to wavelet analysis in biomedical signal processing

An introduction to chaos theory, fractals and non-linear analysis and their application in biomedical signal processing

An introduction to biomedical signal processing with Matlab

Learning outcomes

Attributes Developed
Identify the origin and characteristics of biomedical signals (SM1b, SM3b) K
Propose remedial action for different sources of noise (SM1b, SM2b) KC
Identify the advantages and disadvantages of spectral, wavelets and non-linear analysis methods (SM2b, EA1b, EA2, P4) KC
Evaluate the adequacy of different signal processing techniques for biomedical signal analysis (SM2b, EA2, EA3b, P3, P4) KCP
1. Interpret the nature of physical processes and pathological conditions based on observations of biomedical signals to evaluate the usefulness of biomedical signal processing in a clinical context (SM3b, EA1b, EA2, EL1, EL2, P4) KCP
Develop the computer programming skills to write basic programs in Matlab for biomedical signal processing (EA3b, P3) PT

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Module content

Indicative content includes:


Biomedical Signals. Origin and characteristics
Biomedical Signal Acquisition
Spectral analysis of biomedical signals. Fourier transform, the periodogram, Welch’s method and parametric and non-parametric methods
Filter design and noise removalTime-frequency analysis of biomedical signals. The spectrogram
Wavelet analysis of biomedical signals. The Continuous Wavelet Transform and the Discrete Wavelet Transform
Non-linear analysis of biomedical signals. Chaos theory, fractals, and other non-linear signal processing algorithms

Methods of Teaching / Learning

The learning and teaching strategy is designed to:

Introduce basic biomedical signal processing principles through theory with worked examples. This is delivered principally through lectures and tutorial classes. In the latter, students will have to write Matlab scripts and functions for the analysis of biomedical signals. Learning would be reinforced with the application of the biomedical signal processing principles to real biomedical signals in two coursework assignments.

The learning and teaching methods include:


2 hours lecture per week x 11 weeks
2 hour Matlab-based tutorials x 11 weeks
2 hours revision lecture

Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate understanding of biomedical signal processing methods as well as the ability to analyse time-domain and frequency-domain characteristics of different biomedical signals. The first coursework assignment allows students to demonstrate their knowledge and understanding of basic time-domain and spectral signal processing techniques. The second coursework assignment allows students to demonstrate their knowledge and understanding of filters, time-frequency and non-linear signal processing techniques. Both coursework assignments also test report writing skills as well as the students’ ability to comment critically on their results. The final examination tests the deeper understanding of the different biomedical signal processing methods presented in the module, including open-ended questions asking the students to come up with possible signal processing options for real-life biomedical engineering problems.

Thus, the summative assessment for this module consists of:


Coursework 1 [Learning outcomes 1, 3, 4, 6]       (20 hours)            Deadline W7   {20%}
Coursework 2 [Learning outcomes 1, 2, 3, 4, 6]   (25 hours)            Deadline W12 {25%}
Examination   [Learning outcomes 1, 2, 3, 4, 5]   (2 hours)                                     {55%}


Formative assessment and feedback


Formative verbal feedback is given in tutorials
Formative Multiple Choice Tests are available on SurreyLearn to give feedback on the understanding of biomedical signal processing techniques
Extensive written feedback is given on the coursework assessments

Reading list

Reading list for BIOMEDICAL SIGNAL PROCESSING : http://aspire.surrey.ac.uk/modules/eng3168

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2017/8 academic year.