Module Details
Module Code: |
PROC C4601 |
Module Title:
|
Biomedical Signal Processing
|
Title:
|
Biomedical Signal Processing
|
Module Level:: |
8 |
Module Coordinator: |
Cathal Nolan
|
Module Author:: |
Darren Kavanagh
|
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 Assessment | 20.00% |
Practical | 20.00% |
End of Module Formal Examination | 60.00% |
AssessmentsFull Time
End of Module 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 |
---|
|