Computer aided detection of breathing disorder from ballistocardiography signal using convolutional neural network

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Computer aided detection of breathing disorder from ballistocardiography signal using convolutional neural network

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Computer aided detection of breathing disorder from ballistocardiography signal using convolutional neural network

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EF17_048%2F0007441" target="_blank" >EF17_048/0007441: PERSONMED - Centrum rozvoje personalizované medicíny u věkem podmíněných onemocnění</a><br>

Návaznosti

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

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Information sciences

ISSN

0020-0255

e-ISSN

—

Svazek periodika

541

Číslo periodika v rámci svazku

December

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

11

Strana od-do

207-217

Kód UT WoS článku

000573604400003

EID výsledku v databázi Scopus

2-s2.0-85087765663