Module Details

Module Code: PROC C4601
Module Title: Biomedical Signal Processing
Title: Biomedical Signal Processing
Module Level:: 8
Credits:: 5
Module Coordinator: Cathal Nolan
Module Author:: Darren Kavanagh
Domains:  
Module Description: The aim of this module is to provide the student with knowledge and understanding in relation to core signal processing methods and machine learning approaches for biomedical signals and images. This module focuses specifically on different analogue to digital conversion (ADC) methods, statistical analysis, feature engineering and characterisation, medical imaging systems and image processing, and finally, machine learning and artificial intelligence algorithms for different biomedical applications.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Examine the different digital conversion methods (ADCs and DACs).
LO2 Apply feature engineering, statistical analysis, and characterisation to biomedical signals.
LO3 Examine medical imaging systems and image processing.
LO4 Appraise different machine learning and artificial intelligence algorithms for biomedical applications.
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is recommended before enrolment in this module.
No recommendations listed
Co-requisite Modules
No Co-requisite modules listed
Additional Requisite Information
No Co Requisites listed
 
Indicative Content
Analogue to digital conversion (ADC) methods:
(i) Sampling and Quantisation, (ii) Multiplexed vs. single ADC per channel, (iii) Successive Approximation (SAR), (iv) Delta-sigma (ΔΣ), (v) Dual Slope, (vi) Pipelined, (vii) Flash.
Digital to analogue conversion (DAC) methods:
(i) Pulse Width Modulator, (ii) Delta Sigma Modulator, (iii) Binary-weighted, (iv) Successive Approximation (Cyclic).
Feature engineering and signal characterisation:
(i) Signal representations and time and frequency domain transformations, (ii) Fourier Analysis, (iii) Wavelet Analysis, (iv) Hilbert-Huang Transform.
Statistical analysis
(i) Principal Component Analysis, (ii) Linear Discriminant Analysis, (iii) Application of methods to ECG, EMG, EEG, MEG, SpO2, acoustic/speech, fMRI signals/data.
Medical imaging systems:
(i) Computed Radiography, (ii) Computed Tomography (CT or CAT), (iii) Magnetic Resonance Imaging (MRI), (iv) Nuclear Medicine, (v) Single-Photon Emission Computed Tomography, (vi) Positron Emission Tomography, (vii) Ultrasonography, (viii) Contrast agents.
Image processing:
(i) Image sensors, (ii) Image compression, (iii) Discrete cosine transform (DCT).
Machine learning and artificial intelligence:
(i) Biomedical and diagnostic applications, (ii) Dimensionality Reduction, (iii) Clustering, (iv) Supervised Learning (Regression and Classification), (v) K-Nearest Neighbour (k-NN), (vi) Support Vector Machines (SVM), (vii) Convolutional Neural Network (CNN).
Module Content & Assessment
Assessment Breakdown%
Continuous Assessment20.00%
Practical20.00%
End of Module Formal Examination60.00%

Assessments

Full Time

Continuous Assessment
Assessment Type Examination % of Total Mark 10
Timing Week 7 Learning Outcomes 1,2
Non-marked No
Assessment Description
Class Assessment
Assessment Type Project % of Total Mark 10
Timing Week 14 Learning Outcomes 2,3,4
Non-marked No
Assessment Description
Research Assignment/Exercise.
No Project
Practical
Assessment Type Practical/Skills Evaluation % of Total Mark 20
Timing Week 14 Learning Outcomes 1,2,3,4
Non-marked No
Assessment Description
Lab Reports – Formative Assessments.
End of Module Formal Examination
Assessment Type Formal Exam % of Total Mark 60
Timing End-of-Semester Learning Outcomes 1,2,3,4
Non-marked No
Assessment Description
Summative Assessment – Formal Examination.
Reassessment Requirement
Repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.

SETU Carlow Campus reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Workload Category Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Delivery will consist of lectures/tutorial sessions. Every Week 3.00 3
Laboratory Contact Delivery will consist of practical sessions using measurement and test equipment and computational environments (modelling and simulation). Every Week 2.00 2
Independent Learning Non Contact Reading, assignments, study, tutorials, applied laboratory experiments and exercises. Every Week 3.00 3
Total Weekly Contact Hours 5.00
 
Module Resources
Supplementary Book Resources
  • Edited by Dr. Walid Zgallai Series Editor Dr. Dennis Fitzpatrick. (2020), Biomedical Signal Processing and Artificial Intelligence in Healthcare, Academic Press, An Imprint of Elsevier, [ISBN: 978-0-12-8189].
  • Jianxin Wu. (2020), Essentials of Pattern Recognition, Cambridge University Press, p.398, [ISBN: 9781108483469].
This module does not have any article/paper resources
This module does not have any other resources
Discussion Note: