Attention Networks for Multi-Task Signal Analysis
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Description
Recent advances in deep learning have enabled the development of automated frameworks for analysing medical images and signals. For analysis of physiological recordings, models based on temporal convolutional networks and recurrent neural networks have demonstrated encouraging results and an ability to capture complex patterns and dependencies in the data. However, representations that capture the entirety of the raw signal are suboptimal as not all portions of the signal are equally important. As such, attention mechanisms are proposed to divert focus to regions of interest, reducing computational cost and enhancing accuracy. Here, we evaluate attention-based frameworks for the classification of physiological signals in different clinical domains. We evaluated our methodology on three classification scenarios: neurogenerative disorders, neurological status and seizure type. We demonstrate that attention networks can outperform traditional deep learning models for sequence modelling by identifying the most relevant attributes of an input signal for decision making. This work highlights the benefits of attention-based models for analysing raw data in the field of biomedical research.
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ID Code: | 213526 | ||||||
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Item Type: | Chapter in Book, Report or Conference volume (Conference contribution) | ||||||
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Measurements or Duration: | 4 pages | ||||||
DOI: | 10.1109/EMBC44109.2020.9175730 | ||||||
ISBN: | 978-1-7281-1991-5 | ||||||
Pure ID: | 83354917 | ||||||
Divisions: | Current > Research Centres > Centre for Data Science Current > Research Centres > Centre for Biomedical Technologies Past > QUT Faculties & Divisions > Science & Engineering Faculty Current > QUT Faculties and Divisions > Faculty of Science Current > QUT Faculties and Divisions > Faculty of Engineering Current > Schools > School of Electrical Engineering & Robotics |
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Copyright Owner: | 2020 IEEE | ||||||
Copyright Statement: | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||
Deposited On: | 26 Sep 2021 20:52 | ||||||
Last Modified: | 10 Apr 2024 03:31 |
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