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Fetal Arrhythmia Detection Based on Labeling Considering Heartbeat Interval

Arrhythmia is one of the causes of sudden infant death, and it is very important to detect fetal arrhythmia for fetal well-being. Fetal electrocardiogram (FECG) is one of the methods to detect a heartbeat. Fetal arrhythmia can be detected based on the heartbeat detection results from FECG signals su...

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Autores principales: Nakatani, Sara, Yamamoto, Kohei, Ohtsuki, Tomoaki
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9855115/
https://www.ncbi.nlm.nih.gov/pubmed/36671621
http://dx.doi.org/10.3390/bioengineering10010048
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author Nakatani, Sara
Yamamoto, Kohei
Ohtsuki, Tomoaki
author_facet Nakatani, Sara
Yamamoto, Kohei
Ohtsuki, Tomoaki
author_sort Nakatani, Sara
collection PubMed
description Arrhythmia is one of the causes of sudden infant death, and it is very important to detect fetal arrhythmia for fetal well-being. Fetal electrocardiogram (FECG) is one of the methods to detect a heartbeat. Fetal arrhythmia can be detected based on the heartbeat detection results from FECG signals such as heartbeat intervals. However, the accuracy of arrhythmia detection easily degrades depending on the accuracy of heartbeat detection. In this paper, we propose a deep learning-based fetal arrhythmia detection method using FECG signals. Recently, arrhythmia detection methods using adult ECG signals have achieved a high arrhythmia detection accuracy based on deep learning. Motivated by this fact, in the proposed method, the acquired FECG signals are segmented, and the segments are input into a deep learning model that classifies them into normal or arrhythmia ones. Based on the classification results of multiple segments, a subject is judged as a healthy or arrhythmia subject. Each segment of the training data is divided into three categories based on the estimated heartbeat interval: (i) normal, (ii) arrhythmia, and (iii) a segment that could be both normal and arrhythmic. Only segments labeled as normal or arrhythmia are used for training a deep learning model to achieve a higher classification accuracy of the model. Through these procedures, the proposed method detects fetal arrhythmia with fewer effects of heartbeat detection results. The experimental results show that the proposed method achieves 96.2% accuracy, 100% specificity, and 100% recall, improving the values of conventional methods based on heartbeat detection and feature detection.
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spelling pubmed-98551152023-01-21 Fetal Arrhythmia Detection Based on Labeling Considering Heartbeat Interval Nakatani, Sara Yamamoto, Kohei Ohtsuki, Tomoaki Bioengineering (Basel) Article Arrhythmia is one of the causes of sudden infant death, and it is very important to detect fetal arrhythmia for fetal well-being. Fetal electrocardiogram (FECG) is one of the methods to detect a heartbeat. Fetal arrhythmia can be detected based on the heartbeat detection results from FECG signals such as heartbeat intervals. However, the accuracy of arrhythmia detection easily degrades depending on the accuracy of heartbeat detection. In this paper, we propose a deep learning-based fetal arrhythmia detection method using FECG signals. Recently, arrhythmia detection methods using adult ECG signals have achieved a high arrhythmia detection accuracy based on deep learning. Motivated by this fact, in the proposed method, the acquired FECG signals are segmented, and the segments are input into a deep learning model that classifies them into normal or arrhythmia ones. Based on the classification results of multiple segments, a subject is judged as a healthy or arrhythmia subject. Each segment of the training data is divided into three categories based on the estimated heartbeat interval: (i) normal, (ii) arrhythmia, and (iii) a segment that could be both normal and arrhythmic. Only segments labeled as normal or arrhythmia are used for training a deep learning model to achieve a higher classification accuracy of the model. Through these procedures, the proposed method detects fetal arrhythmia with fewer effects of heartbeat detection results. The experimental results show that the proposed method achieves 96.2% accuracy, 100% specificity, and 100% recall, improving the values of conventional methods based on heartbeat detection and feature detection. MDPI 2022-12-30 /pmc/articles/PMC9855115/ /pubmed/36671621 http://dx.doi.org/10.3390/bioengineering10010048 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Nakatani, Sara
Yamamoto, Kohei
Ohtsuki, Tomoaki
Fetal Arrhythmia Detection Based on Labeling Considering Heartbeat Interval
title Fetal Arrhythmia Detection Based on Labeling Considering Heartbeat Interval
title_full Fetal Arrhythmia Detection Based on Labeling Considering Heartbeat Interval
title_fullStr Fetal Arrhythmia Detection Based on Labeling Considering Heartbeat Interval
title_full_unstemmed Fetal Arrhythmia Detection Based on Labeling Considering Heartbeat Interval
title_short Fetal Arrhythmia Detection Based on Labeling Considering Heartbeat Interval
title_sort fetal arrhythmia detection based on labeling considering heartbeat interval
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9855115/
https://www.ncbi.nlm.nih.gov/pubmed/36671621
http://dx.doi.org/10.3390/bioengineering10010048
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