A Vision-based System for Breathing Disorder Identification: A Deep Learning Perspective
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Description
Recent breakthroughs in computer vision offer an exciting avenue to develop new remote, and non-intrusive patient monitoring techniques. A very challenging topic to address is the automated recognition of breathing disorders during sleep. Due to its complexity, this task has rarely been explored in the literature on real patients using such marker-free approaches. Here, we propose an approach based on deep learning architectures capable of classifying breathing disorders. The classification is performed on depth maps recorded with 3D cameras from 76 patients referred to a sleep laboratory that present a range of breathing disorders. Our system is capable of classifying individual breathing events as normal or abnormal with an accuracy of 61.8%, hence our results show that computer vision and deep learning are viable tools for assessing locally or remotely breathing quality during sleep.
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ID Code: | 210837 | ||||
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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/EMBC.2019.8857662 | ||||
ISBN: | 9781538613122 | ||||
Pure ID: | 85370264 | ||||
Divisions: | Past > QUT Faculties & Divisions > Science & Engineering Faculty | ||||
Funding Information: | This research was supported by the German Federal Ministry of Education and Research within the SPHERE project. | ||||
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 Jun 2021 00:06 | ||||
Last Modified: | 15 Jul 2024 08:29 |
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