Motion signatures for the analysis of seizure evolution in epilepsy
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Accepted Version
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59288451. Available under License Creative Commons Attribution Non-commercial 4.0. |
Description
In epilepsy, semiology refers to the study of patient behavior and movement, and their temporal evolution during epileptic seizures. Understanding semiology provides clues to the cerebral networks underpinning the epileptic episode and is a vital resource in the pre-surgical evaluation. Recent advances in video analytics have been helpful in capturing and quantifying epileptic seizures. Nevertheless, the automated representation of the evolution of semiology, as examined by neurologists, has not been appropriately investigated. From initial seizure symptoms until seizure termination, motion patterns of isolated clinical manifestations vary over time. Furthermore, epileptic seizures frequently evolve from one clinical manifestation to another, and their understanding cannot be overlooked during a presurgery evaluation. Here, we propose a system capable of computing motion signatures from videos of face and hand semiology to provide quantitative information on the motion, and the correlation between motions. Each signature is derived from a sparse saliency representation established by the magnitude of the optical flow field. The developed computer-aided tool provides a novel approach for physicians to analyze semiology as a flow of signals without interfering in the healthcare environment. We detect and quantify semiology using detectors based on deep learning and via a novel signature scheme, which is independent of the amount of data and seizure differences. The system reinforces the benefits of computer vision for non-obstructive clinical applications to quantify epileptic seizures recorded in real-life healthcare conditions.
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ID Code: | 201647 | ||||||||
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Item Type: | Chapter in Book, Report or Conference volume (Conference contribution) | ||||||||
Series Name: | Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS | ||||||||
ORCID iD: |
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Measurements or Duration: | 7 pages | ||||||||
DOI: | 10.1109/EMBC.2019.8857743 | ||||||||
ISBN: | 978-1-5386-1312-2 | ||||||||
Pure ID: | 59288451 | ||||||||
Divisions: | Past > Institutes > Institute for Future Environments Past > QUT Faculties & Divisions > Science & Engineering Faculty |
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Funding Information: | This research was supported by Mater Centre for Neuroscience under Research Governance authorization RG-17-008 and the KIT research exchange sponsorship from the Deutschen Akademischen Auslandsdienst (DAAD). | ||||||||
Copyright Owner: | 2019 IEEE | ||||||||
Copyright Statement: | © 2019 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: | 02 Jul 2020 05:39 | ||||||||
Last Modified: | 19 Apr 2024 15:18 |
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