Presentation 2022-05-26
Unsupervised Learning-based Non-invasive Fetal ECG Signal Quality Assessment
Xintong Shi, Kohei Yamamoto, Tomoaki Ohtsuki, Yutaka Matsui, Kazunari Owada,
PDF Download Page PDF download Page Link
Abstract(in Japanese) (See Japanese page)
Abstract(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)
Keyword(in English) Non-invasive fetal ECGUnsupervised learningSignal quality assessmentAutoencoder
Paper # SeMI2022-4
Date of Issue 2022-05-19 (SeMI)

Conference Information
Committee SeMI / IPSJ-DPS / IPSJ-MBL / IPSJ-ITS
Conference Date 2022/5/26(2days)
Place (in Japanese) (See Japanese page)
Place (in English)
Topics (in Japanese) (See Japanese page)
Topics (in English)
Chair Koji Yamamoto(Kyoto Univ.)
Vice Chair Kazuya Monden(Hitachi) / Yasunori Owada(NICT)
Secretary Kazuya Monden(Cyber Univ.) / Yasunori Owada(Waseda Univ.) / (Osaka Univ.)
Assistant Yuki Katsumata(NTT DOCOMO) / Akihito Taya(Aoyama Gakuin Univ.) / Yu Nakayama(Tokyo Univ. of Agri. and Tech.)

Paper Information
Registration To Technical Committee on Sensor Network and Mobile Intelligence / Special Interest Group on Distributed Processing System / Special Interest Group on Mobile Computing and Pervasive Systems / Special Interest Group on Intelligent Transport Systems and Smart Community
Language ENG
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 ECGUnsupervised learningSignal quality assessmentAutoencoder
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.)
Date 2022-05-26
Paper # SeMI2022-4
Volume (vol) vol.122
Number (no) SeMI-46
Page pp.pp.15-19(SeMI),
#Pages 5
Date of Issue 2022-05-19 (SeMI)