Journal
INFORMATION SCIENCES
Volume 541, Issue -, Pages 207-217Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2020.05.051
Keywords
Disordered breathing; Ballistocardiography; Cartan curvature; Convolutional neural networks; Computer-aided diagnostic; Tensometers
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Funding
- Faculty of Informatics and Management UHK specific research project [2107]
- Faculty of Science, University of Hradec Kralove
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Sleep-related breathing disorders are diseases related to pharyngeal airway collapse. It can lead to several health problems such as somnolence, poorer daytime cognitive performance, and cardiovascular morbidity and mortality. However, computer-aided diagnostic (CAD) tools play a very important role in the detection of breathing disorders. It is possible to measure breathing activity, but most approaches require some type of device placed on the human body. This paper proposes a novel methodology of an unobtrusive CAD system to the breathing disorder detection. Unobtrusive approach is ensured by ballistocardiography (BCG) sensors located on the measured bed. The significant pieces of information from the signals are extracted by Cartan curvatures. Thereafter, important features are separated from individual samples as an input to our 9-layer deep convolutional neural network. We achieved an average accuracy of 98.00%, sensitivity of 94.26%, and specificity of 99.22% on 4009 regular and 1307 disordered breathing samples. (C) 2020 Elsevier Inc. All rights reserved.
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