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Paper Abstract and Keywords
Presentation 2022-05-26 13:45
Unsupervised Learning-based Non-invasive Fetal ECG Signal Quality Assessment
Xintong Shi, Kohei Yamamoto, Tomoaki Ohtsuki (Keio Univ.), Yutaka Matsui, Kazunari Owada (Atom Medical Co., Ltd.) SeMI2022-4
Abstract (in Japanese) (See Japanese page) 
(in English) For fetal heart rate (FHR) monitoring, the non-invasive fetal electrocardiogram (FECG) obtained from abdomen surface electrodes has been widely employed. The accuracy of FECG-based FHR estimation, on the other hand, is highly dependent on the quality of the FECG signals, which can be influenced by a variety of interference sources, including maternal cardiac activities and fetal movements. Thus, FECG signal quality assessment (SQA) is a critical task in improving FHR estimation accuracy by eliminating or interpolating FHRs estimated from low-quality FECG signals. Various SQA approaches based on supervised learning have been proposed in recent studies. These approaches are capable of accurate SQA, however, they require big datasets with annotations. Nevertheless, the annotated datasets for the FECG SQA are extremely limited. In this research, to deal with this problem, we introduce an unsupervised learning-based SQA approach for identifying high and low-quality FECG signal segments. Some features associated with signal quality are extracted, including four entropy-based features, three statistical features, and four ECG signal quality indices (SQIs). In addition to these features, we introduce an autoencoder (AE)-based feature, which is based on the reconstruction error of AE that reconstructs spectrograms obtained from FECG signal segments. The collected features are then input into the self-organizing map (SOM) to classify the high and low-quality FECG segments. The experimental results indicated that our approach classified high and low-quality signals with a 98% accuracy.
Keyword (in Japanese) (See Japanese page) 
(in English) Non-invasive fetal ECG / Unsupervised learning / Signal quality assessment / Autoencoder / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 46, SeMI2022-4, pp. 15-19, May 2022.
Paper # SeMI2022-4 
Date of Issue 2022-05-19 (SeMI) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF SeMI2022-4

Conference Information
Committee SeMI IPSJ-DPS IPSJ-MBL IPSJ-ITS  
Conference Date 2022-05-26 - 2022-05-27 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SeMI 
Conference Code 2022-05-SeMI-DPS-MBL-ITS 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Unsupervised Learning-based Non-invasive Fetal ECG Signal Quality Assessment 
Sub Title (in English)  
Keyword(1) Non-invasive fetal ECG  
Keyword(2) Unsupervised learning  
Keyword(3) Signal quality assessment  
Keyword(4) Autoencoder  
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1st Author's Name Xintong Shi  
1st Author's Affiliation Keio University (Keio Univ.)
2nd Author's Name Kohei Yamamoto  
2nd Author's Affiliation Keio University (Keio Univ.)
3rd Author's Name Tomoaki Ohtsuki  
3rd Author's Affiliation Keio University (Keio Univ.)
4th Author's Name Yutaka Matsui  
4th Author's Affiliation Atom Medical Corporation (Atom Medical Co., Ltd.)
5th Author's Name Kazunari Owada  
5th Author's Affiliation Atom Medical Corporation (Atom Medical Co., Ltd.)
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Speaker Author-1 
Date Time 2022-05-26 13:45:00 
Presentation Time 18 minutes 
Registration for SeMI 
Paper # SeMI2022-4 
Volume (vol) vol.122 
Number (no) no.46 
Page pp.15-19 
#Pages 5 
Date of Issue 2022-05-19 (SeMI) 

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