Deep machine learning for biosignal analysis
Thilakasiri Dissanayakage, Pubudu Theekshana Dissanayake (2024) Deep machine learning for biosignal analysis. PhD by Publication, Queensland University of Technology.
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Description
The application of deep-learning to biosignal data is a rapidly evolving research domain in the Medical AI field, with many potential applications such as cardiac anomaly detection, seizure prediction and pneumonia diagnosis. Research conducted in this thesis presents a series of solutions for biosignal applications, focusing on models' generalizability, generation, interpretation, and design aspects. To evaluate the universality of the solutions, this thesis presents benchmark studies on various biosignal modalities such as electrocardiograms, heart sounds, lung sounds, and electroencephalograms. It also focuses on the interpretability of the models and provides solid medically sound explanations for predictions made by the models.
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| ID Code: | 247177 |
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| Item Type: | QUT Thesis (PhD by Publication) |
| Supervisor: | Fookes, Clinton, Sridharan, Sridha, Denman, Simon, & Warnakulasuriya, Tharindu |
| Keywords: | Deep Learning, Biomedical Signal Processing, Biosignal, Machine Learning, Anomaly Detection, Generative Models, Segmentation, Domain Generalization, Seizure Prediction, Graph Neural Networks |
| DOI: | 10.5204/thesis.eprints.247177 |
| Pure ID: | 164879375 |
| Divisions: | Current > QUT Faculties and Divisions > Faculty of Engineering Current > Schools > School of Electrical Engineering & Robotics |
| Institution: | Queensland University of Technology |
| Deposited On: | 12 Mar 2024 15:34 |
| Last Modified: | 19 Jan 2025 00:47 |
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