| 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) |
| 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. |
ITE Tech. Rep. |
| Paper # |
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| Date of Issue |
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| ISSN |
Online edition: ISSN 2424-1970 |
| Download PDF |
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| Conference Information |
| Committee |
IEICE-IE ME IEICE-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) |
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| Paper Information |
| Registration To |
IEICE-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) |
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| Keyword(1) |
umbilical cord blood gas |
| Keyword(2) |
fetal heart rate |
| Keyword(3) |
pressure of carbon dioxide |
| Keyword(4) |
machine learning classifiers |
| Keyword(5) |
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| 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) |
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| Speaker |
Author-1 |
| Date Time |
2025-09-04 10:30:00 |
| Presentation Time |
20 minutes |
| Registration for |
IEICE-IE |
| Paper # |
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| Volume (vol) |
vol.49 |
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