Novel approach to computerized breath detection in lung function diagnostics

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11130%2F18%3A10381328" target="_blank" >RIV/00216208:11130/18:10381328 - isvavai.cz</a>

Alternative codes found

RIV/00216208:11320/18:10381328 RIV/00064203:_____/18:10381328

Result on the web

<a href="https://doi.org/10.1016/j.compbiomed.2018.07.017" target="_blank" >https://doi.org/10.1016/j.compbiomed.2018.07.017</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.compbiomed.2018.07.017" target="_blank" >10.1016/j.compbiomed.2018.07.017</a>

Result language

angličtina

Original language name

Novel approach to computerized breath detection in lung function diagnostics

Original language description

Background: Breath detection, i.e. its precise delineation in time is a crucial step in lung function data analysis as obtaining any clinically relevant index is based on the proper localization of breath ends. Current threshold or smoothing algorithms suffer from severe inaccuracy in cases of suboptimal data quality. Especially in infants, the precise analysis is of utmost importance. The key objective of our work is to design an algorithm for accurate breath detection in severely distorted data. Methods: Flow and gas concentration data from multiple breath washout test were the input information. Based on universal physiological characteristics of the respiratory tract we designed an algorithm for breath detection. Its accuracy was tested on severely distorted data from 19 patients with different types of breathing disorders. Its performance was compared to the performance of currently used algorithms and to the breath counts estimated by human experts. Results: The novel algorithm outperformed the threshold algorithms with respect to their accuracy and had similar performance to human experts. It proved to be a highly robust and efficient approach in severely distorted data. This was demonstrated on patients with different pulmonary disorders. Conclusion: Our newly proposed algorithm is highly robust and universal. It works accurately even on severely distorted data, where the other tested algorithms failed. It does not require any pre-set thresholds or other patient-specific inputs. Consequently, it may be used with a broad spectrum of patients. It has the potential to replace current approaches to the breath detection in pulmonary function diagnostics.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10600 - Biological sciences

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2018

Confidentiality

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

Name of the periodical

Computers in Biology and Medicine

ISSN

0010-4825

e-ISSN

—

Volume of the periodical

101

Issue of the periodical within the volume

October

Country of publishing house

US - UNITED STATES

Number of pages

6

Pages from-to

1-6

UT code for WoS article

000446282300001

EID of the result in the Scopus database

2-s2.0-85050890394