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) |