1D-FHRNet: Automatic Diagnosis of Fetal Acidosis from Fetal Heart Rate Signals

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F21%3APU140902" target="_blank" >RIV/00216305:26220/21:PU140902 - isvavai.cz</a>

Result on the web

<a href="https://authors.elsevier.com/a/1d81U6DBR31top" target="_blank" >https://authors.elsevier.com/a/1d81U6DBR31top</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

1D-FHRNet: Automatic Diagnosis of Fetal Acidosis from Fetal Heart Rate Signals

Original language description

Fetal heart rate (FHR) is used to monitor the fetal state by obstetricians as a screening tool. Common guidelines for visual interpretation of FHR signals results in significant subjective variability due to the fetal physiological dynamics complexity. Automated diagnostic technology can assist obstetricians in medical decisions based on artificial intelligence and also can be an automatic diagnostic tool for primary health care centres and remote areas. This work presents a machine learning-based automated diagnostic tool for classification and diagnosis of Fetal Acidosis using FHR. A 1D-CNN model has been proposed because of its ability to automatically diagnose Fetal Acidosis into healthy or pathological conditions with high accuracy. To make the method robust and to improve accuracy with the artefacts present in the signal, the signal pre-processing is performed before training and classification. The accuracy was evaluated on a comprehensive dataset and achieved 99.09% for the diagnosis of Fetal Acidosis. Low-cost electronic hardware integrated with the proposed methodology can perform in real-time and can achieve high accuracy and reliability. This method can be used to support the expert decision and as an automatic stand-alone diagnostic tool that can assist the obstetricians in the early diagnosis of fetal acidosis.

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

30209 - Paediatrics

Project

<a href="/en/project/VI04000039" target="_blank" >VI04000039: Early COVID-19 infection detection system for the safety of vulnerable groups using artificial intelligence</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2021

Confidentiality

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

Name of the periodical

BIOMED SIGNAL PROCES

ISSN

1746-8094

e-ISSN

1746-8108

Volume of the periodical

2021

Issue of the periodical within the volume

68

Country of publishing house

GB - UNITED KINGDOM

Number of pages

10

Pages from-to

1-10

UT code for WoS article

000718843100005

EID of the result in the Scopus database

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