1D-FHRNet: Automatic Diagnosis of Fetal Acidosis from Fetal Heart Rate Signals
Identifikátory výsledku
Kód výsledku v 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>
Výsledek na webu
<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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Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
1D-FHRNet: Automatic Diagnosis of Fetal Acidosis from Fetal Heart Rate Signals
Popis výsledku v původním jazyce
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.
Název v anglickém jazyce
1D-FHRNet: Automatic Diagnosis of Fetal Acidosis from Fetal Heart Rate Signals
Popis výsledku anglicky
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.
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
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OECD FORD obor
30209 - Paediatrics
Návaznosti výsledku
Projekt
<a href="/cs/project/VI04000039" target="_blank" >VI04000039: Systém včasného záchytu infekce COVID-19 pro bezpečnost ohrožených skupin obyvatelstva s využitím umělé inteligence</a><br>
Návaznosti
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Ostatní
Rok uplatnění
2021
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
BIOMED SIGNAL PROCES
ISSN
1746-8094
e-ISSN
1746-8108
Svazek periodika
2021
Číslo periodika v rámci svazku
68
Stát vydavatele periodika
GB - Spojené království Velké Británie a Severního Irska
Počet stran výsledku
10
Strana od-do
1-10
Kód UT WoS článku
000718843100005
EID výsledku v databázi Scopus
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