Computer aided detection of breathing disorder from ballistocardiography signal using convolutional neural network
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F20%3A50017106" target="_blank" >RIV/62690094:18470/20:50017106 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0020025520304849?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0020025520304849?via%3Dihub</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.ins.2020.05.051" target="_blank" >10.1016/j.ins.2020.05.051</a>
Alternative languages
Result language
angličtina
Original language name
Computer aided detection of breathing disorder from ballistocardiography signal using convolutional neural network
Original language description
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. © 2020 Elsevier Inc.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/EF17_048%2F0007441" target="_blank" >EF17_048/0007441: PERSONMED - Center for the Development of Personalized Medicine in Age-Related Diseases</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Information sciences
ISSN
0020-0255
e-ISSN
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Volume of the periodical
541
Issue of the periodical within the volume
December
Country of publishing house
US - UNITED STATES
Number of pages
11
Pages from-to
207-217
UT code for WoS article
000573604400003
EID of the result in the Scopus database
2-s2.0-85087765663