Application of mechanical trigger for unobtrusive detection of respiratory disorders from body recoil micro-movements

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F21%3A50018224" target="_blank" >RIV/62690094:18470/21:50018224 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0169260721002236?dgcid=rss_sd_all" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0169260721002236?dgcid=rss_sd_all</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.cmpb.2021.106149" target="_blank" >10.1016/j.cmpb.2021.106149</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Application of mechanical trigger for unobtrusive detection of respiratory disorders from body recoil micro-movements

Popis výsledku v původním jazyce

Background and Objectives Automatic detection of breathing disorders plays an important role in the early signalization of respiratory diseases. Measuring methods can be based on electrocardiogram (ECG), sound, oximetry, or respiratory analysis. However, these approaches require devices placed on the human body or they are prone to disturbance by environmental influences. To solve these problems, we proposed a heart contraction mechanical trigger for unobtrusive detection of respiratory disorders from the mechanical measurement of cardiac contractions. We designed a novel method to calculate this mechanical trigger purely from measured mechanical signals without the use of ECG. Methods The approach is a built-on calculation of the so-called euclidean arc length from the signals. In comparison to previous researches, this system does not require any equipment attached to a person. This is achieved by locating the tensometers on the bed. Data from sensors are fused by the Cartan curvatures method to beat-to-beat vector input for the Convolutional neural network (CNN) classifier. Results In sum, 2281 disordered and 5130 normal breathing samples was collected for analysis. The experiments with use of 10-fold cross validation show that accuracy, sensitivity, and specificity reach values of 96.37%, 92.46%, and 98.11% respectively. Conclusions By the approach for detection, the system offers a novel way for a completely unobtrusive diagnosis of breathing-related health problems. The proposed solution can effectively be deployed in all clinical or home environments.

Název v anglickém jazyce

Application of mechanical trigger for unobtrusive detection of respiratory disorders from body recoil micro-movements

Popis výsledku anglicky

Background and Objectives Automatic detection of breathing disorders plays an important role in the early signalization of respiratory diseases. Measuring methods can be based on electrocardiogram (ECG), sound, oximetry, or respiratory analysis. However, these approaches require devices placed on the human body or they are prone to disturbance by environmental influences. To solve these problems, we proposed a heart contraction mechanical trigger for unobtrusive detection of respiratory disorders from the mechanical measurement of cardiac contractions. We designed a novel method to calculate this mechanical trigger purely from measured mechanical signals without the use of ECG. Methods The approach is a built-on calculation of the so-called euclidean arc length from the signals. In comparison to previous researches, this system does not require any equipment attached to a person. This is achieved by locating the tensometers on the bed. Data from sensors are fused by the Cartan curvatures method to beat-to-beat vector input for the Convolutional neural network (CNN) classifier. Results In sum, 2281 disordered and 5130 normal breathing samples was collected for analysis. The experiments with use of 10-fold cross validation show that accuracy, sensitivity, and specificity reach values of 96.37%, 92.46%, and 98.11% respectively. Conclusions By the approach for detection, the system offers a novel way for a completely unobtrusive diagnosis of breathing-related health problems. The proposed solution can effectively be deployed in all clinical or home environments.

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

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

Computer Methods and Programs in Biomedicine

ISSN

0169-2607

e-ISSN

—

Svazek periodika

207

Číslo periodika v rámci svazku

August

Stát vydavatele periodika

IE - Irsko

Počet stran výsledku

9

Strana od-do

"Article number: 106149"

Kód UT WoS článku

000679356900005

EID výsledku v databázi Scopus

2-s2.0-85106245659