Information: Join today and make your research activities more affordable! Technical workshop participation fees and annual registration fees are available at member rates.
Notice: [Important] Announcement of Changes to Registration Fee Payment and Manuscript Upload Procedures for IEICE Technical Meetings
IEICE Technical Committee Submission System
Conference Paper's Information		 Online Proceedings
[Sign in]
Tech. Rep. Archives
 Go Top Page Go Previous   [Japanese] / [English] 

Paper Abstract and Keywords
Presentation 2025-09-04 10:30
Bridging Fetal Monitoring and Umbilical Cord Blood Gas Parameter Prediction: A supervised learning approach using fetal heart rate variability
Tunn Cho Lwin, Thi Thi Zin, Pyke Tin, Emi Kino (Miyazakidai), Tsuyomu Ikenoue (Kyusyuiryouikadai) LOIS2025-18 IE2025-37
Abstract (in Japanese) (See Japanese page) 
(in English) Umbilical cord blood gas analysis from fetal heart rate signals, particularly partial pressure of carbon dioxide, is essential for assessing fetal acid-based status and detecting respiratory acidosis at birth. This study proposes a machine learning-based classification framework for predicting fetal carbon dioxide levels as either normal or abnormal using features derived from fetal heart rate variability. Preprocessing included correlation-based segment selection to manage inconsistencies in recording lengths, interpolation to address missing values, and Laplacian features for machine learning classifications. Among the tested window durations of 10, 30, and 60 minutes, the 30-minute segment showed the strongest correlation with carbon dioxide levels and was selected for model training. Three supervised learning models were evaluated: Support Vector Machine with linear and Gaussian kernels, and k-Nearest Neighbors. The results show that k-Nearest Neighbors and Gaussian kernel SVM achieved the best classification performance in detecting abnormal carbon dioxide cases. These findings suggest that supervised learning combined with heart rate variability analysis can offer a promising and non-invasive way to support early detection of fetal health risks during delivery.
Keyword (in Japanese) (See Japanese page) 
(in English) umbilical cord blood gas / fetal heart rate / pressure of carbon dioxide / machine learning classifiers / / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 170, IE2025-37, pp. 47-50, Sept. 2025.
Paper # IE2025-37 
Date of Issue 2025-08-27 (LOIS, IE) 
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 LOIS2025-18 IE2025-37

Conference Information
Committee IE ITE-ME LOIS IEE-CMN  
Conference Date 2025-09-03 - 2025-09-04 
Place (in Japanese) (See Japanese page) 
Place (in English) Hokkaido University of Science 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To IE 
Conference Code 2025-09-IE-ME-LOIS-CMN 
Language English (Japanese title is available) 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Bridging Fetal Monitoring and Umbilical Cord Blood Gas Parameter Prediction: A supervised learning approach using fetal heart rate variability 
Sub Title (in English)  
Keyword(1) umbilical cord blood gas  
Keyword(2) fetal heart rate  
Keyword(3) pressure of carbon dioxide  
Keyword(4) machine learning classifiers  
Keyword(5)  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Tunn Cho Lwin  
1st Author's Affiliation University of Miyazaki (Miyazakidai)
2nd Author's Name Thi Thi Zin  
2nd Author's Affiliation University of Miyazaki (Miyazakidai)
3rd Author's Name Pyke Tin  
3rd Author's Affiliation University of Miyazaki (Miyazakidai)
4th Author's Name Emi Kino  
4th Author's Affiliation University of Miyazaki (Miyazakidai)
5th Author's Name Tsuyomu Ikenoue  
5th Author's Affiliation Kyushu University of Medical Science (Kyusyuiryouikadai)
6th Author's Name  
6th Author's Affiliation ()
7th Author's Name  
7th Author's Affiliation ()
8th Author's Name  
8th Author's Affiliation ()
9th Author's Name  
9th Author's Affiliation ()
10th Author's Name  
10th Author's Affiliation ()
11th Author's Name  
11th Author's Affiliation ()
12th Author's Name  
12th Author's Affiliation ()
13th Author's Name  
13th Author's Affiliation ()
14th Author's Name  
14th Author's Affiliation ()
15th Author's Name  
15th Author's Affiliation ()
16th Author's Name  
16th Author's Affiliation ()
17th Author's Name  
17th Author's Affiliation ()
18th Author's Name  
18th Author's Affiliation ()
19th Author's Name  
19th Author's Affiliation ()
20th Author's Name  
20th Author's Affiliation ()
21st Author's Name  
21st Author's Affiliation ()
22nd Author's Name  
22nd Author's Affiliation ()
23rd Author's Name  
23rd Author's Affiliation ()
24th Author's Name  
24th Author's Affiliation ()
25th Author's Name  
25th Author's Affiliation ()
26th Author's Name / /
26th Author's Affiliation ()
()
27th Author's Name / /
27th Author's Affiliation ()
()
28th Author's Name / /
28th Author's Affiliation ()
()
29th Author's Name / /
29th Author's Affiliation ()
()
30th Author's Name / /
30th Author's Affiliation ()
()
31st Author's Name / /
31st Author's Affiliation ()
()
32nd Author's Name / /
32nd Author's Affiliation ()
()
33rd Author's Name / /
33rd Author's Affiliation ()
()
34th Author's Name / /
34th Author's Affiliation ()
()
35th Author's Name / /
35th Author's Affiliation ()
()
36th Author's Name / /
36th Author's Affiliation ()
()
Speaker Author-1 
Date Time 2025-09-04 10:30:00 
Presentation Time 20 minutes 
Registration for IE 
Paper # LOIS2025-18, IE2025-37 
Volume (vol) vol.125 
Number (no) no.169(LOIS), no.170(IE) 
Page pp.47-50 
#Pages 4 
Date of Issue 2025-08-27 (LOIS, IE) 

[Return to Top Page]

[Return to IEICE Web Page]
The Institute of Electronics, Information and Communication Engineers (IEICE), Japan