An End-to-End Framework for Neonatal Asphyxia Detection Through Video Analysis
Identifying asphyxia using computer vision in real-world settings poses challenges due to varying video quality, diverse lighting conditions, and subtle color changes in the newborn’s skin. This study presents an end-to-end framework for automated neonatal asphyxia detection using time se...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10975012/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850203779046047744 |
|---|---|
| author | Kurnianingsih Sou Nobukawa Melyana Nurul Widyawati Cipta Pramana Nurseno Bayu Aji Afandi Nur Aziz Thohari Dwiana Hendrawati Eri Sato-Shimokawara Naoyuki Kubota |
| author_facet | Kurnianingsih Sou Nobukawa Melyana Nurul Widyawati Cipta Pramana Nurseno Bayu Aji Afandi Nur Aziz Thohari Dwiana Hendrawati Eri Sato-Shimokawara Naoyuki Kubota |
| author_sort | Kurnianingsih |
| collection | DOAJ |
| description | Identifying asphyxia using computer vision in real-world settings poses challenges due to varying video quality, diverse lighting conditions, and subtle color changes in the newborn’s skin. This study presents an end-to-end framework for automated neonatal asphyxia detection using time series video analysis and makes three key contributions. First, the proposed framework integrates YOLOv8-based instance segmentation with advanced feature extraction across multiple color spaces and texture analysis to detect neonatal asphyxia through the multi-modal analysis of skin features in video streams. Second, we introduce a new quality-aware temporal analysis framework that includes adaptive quality assessment for evaluating frames in real time, multi-stage feature stability tracking across temporal windows, hysteresis-based decision logic for ensuring temporal consistency, and LightGBM classification with comprehensive feature engineering to assess severity. Third, we provide a curated time series video dataset of 12,973 frames from 45 neonates, of which some were healthy, and some had asphyxia of varying severity. The findings show that the YOLOv8-based instance segmentation achieved a mean average precision (mAP@0.5) of 0.925 for accurate skin region isolation, and the LightGBM classifier outperformed traditional models with an accuracy of 0.998 and an F1-score of 0.998. The system maintains real-time processing at 30 FPS for normal and mild asphyxia cases with a minor reduction to 20 FPS in more challenging scenarios and exhibits robust temporal stability across severity levels, with consistency scores above 0.90. This framework has the potential to enhance neonatal care through continuous monitoring and timely intervention. |
| format | Article |
| id | doaj-art-fcf00e5ff1e84363a12c14aebff54021 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-fcf00e5ff1e84363a12c14aebff540212025-08-20T02:11:25ZengIEEEIEEE Access2169-35362025-01-0113746897470410.1109/ACCESS.2025.356370210975012An End-to-End Framework for Neonatal Asphyxia Detection Through Video Analysis Kurnianingsih0https://orcid.org/0000-0001-7339-7449Sou Nobukawa1https://orcid.org/0000-0001-7003-6912Melyana Nurul Widyawati2https://orcid.org/0000-0003-2039-2892Cipta Pramana3https://orcid.org/0000-0001-8991-0147Nurseno Bayu Aji4https://orcid.org/0009-0005-7486-3524Afandi Nur Aziz Thohari5https://orcid.org/0000-0003-3468-1994Dwiana Hendrawati6https://orcid.org/0009-0008-1834-6209Eri Sato-Shimokawara7https://orcid.org/0000-0002-4189-9724Naoyuki Kubota8https://orcid.org/0000-0001-8829-037XDepartment of Electrical Engineering, Politeknik Negeri Semarang, Semarang, Central Java, IndonesiaDepartment of Computer Science, Chiba Institute of Technology, Narashino, Chiba, JapanDepartment of Postgraduate Program, Poltekkes Kemenkes Semarang, Semarang, Central Java, IndonesiaDepartment of Obstetrics and Gynecology, KRMT Wongsonegoro Hospital, Semarang, Central Java, IndonesiaDepartment of Electrical Engineering, Politeknik Negeri Semarang, Semarang, Central Java, IndonesiaDepartment of Electrical Engineering, Politeknik Negeri Semarang, Semarang, Central Java, IndonesiaDepartment of Mechanical Engineering, Politeknik Negeri Semarang, Semarang, Central Java, IndonesiaGraduate School of Systems Design, Tokyo Metropolitan University, Hino, Tokyo, JapanGraduate School of Systems Design, Tokyo Metropolitan University, Hino, Tokyo, JapanIdentifying asphyxia using computer vision in real-world settings poses challenges due to varying video quality, diverse lighting conditions, and subtle color changes in the newborn’s skin. This study presents an end-to-end framework for automated neonatal asphyxia detection using time series video analysis and makes three key contributions. First, the proposed framework integrates YOLOv8-based instance segmentation with advanced feature extraction across multiple color spaces and texture analysis to detect neonatal asphyxia through the multi-modal analysis of skin features in video streams. Second, we introduce a new quality-aware temporal analysis framework that includes adaptive quality assessment for evaluating frames in real time, multi-stage feature stability tracking across temporal windows, hysteresis-based decision logic for ensuring temporal consistency, and LightGBM classification with comprehensive feature engineering to assess severity. Third, we provide a curated time series video dataset of 12,973 frames from 45 neonates, of which some were healthy, and some had asphyxia of varying severity. The findings show that the YOLOv8-based instance segmentation achieved a mean average precision (mAP@0.5) of 0.925 for accurate skin region isolation, and the LightGBM classifier outperformed traditional models with an accuracy of 0.998 and an F1-score of 0.998. The system maintains real-time processing at 30 FPS for normal and mild asphyxia cases with a minor reduction to 20 FPS in more challenging scenarios and exhibits robust temporal stability across severity levels, with consistency scores above 0.90. This framework has the potential to enhance neonatal care through continuous monitoring and timely intervention.https://ieeexplore.ieee.org/document/10975012/Neonatal asphyxiavideo analysisinstance segmentationdeep learningmedical imaging |
| spellingShingle | Kurnianingsih Sou Nobukawa Melyana Nurul Widyawati Cipta Pramana Nurseno Bayu Aji Afandi Nur Aziz Thohari Dwiana Hendrawati Eri Sato-Shimokawara Naoyuki Kubota An End-to-End Framework for Neonatal Asphyxia Detection Through Video Analysis IEEE Access Neonatal asphyxia video analysis instance segmentation deep learning medical imaging |
| title | An End-to-End Framework for Neonatal Asphyxia Detection Through Video Analysis |
| title_full | An End-to-End Framework for Neonatal Asphyxia Detection Through Video Analysis |
| title_fullStr | An End-to-End Framework for Neonatal Asphyxia Detection Through Video Analysis |
| title_full_unstemmed | An End-to-End Framework for Neonatal Asphyxia Detection Through Video Analysis |
| title_short | An End-to-End Framework for Neonatal Asphyxia Detection Through Video Analysis |
| title_sort | end to end framework for neonatal asphyxia detection through video analysis |
| topic | Neonatal asphyxia video analysis instance segmentation deep learning medical imaging |
| url | https://ieeexplore.ieee.org/document/10975012/ |
| work_keys_str_mv | AT kurnianingsih anendtoendframeworkforneonatalasphyxiadetectionthroughvideoanalysis AT sounobukawa anendtoendframeworkforneonatalasphyxiadetectionthroughvideoanalysis AT melyananurulwidyawati anendtoendframeworkforneonatalasphyxiadetectionthroughvideoanalysis AT ciptapramana anendtoendframeworkforneonatalasphyxiadetectionthroughvideoanalysis AT nursenobayuaji anendtoendframeworkforneonatalasphyxiadetectionthroughvideoanalysis AT afandinurazizthohari anendtoendframeworkforneonatalasphyxiadetectionthroughvideoanalysis AT dwianahendrawati anendtoendframeworkforneonatalasphyxiadetectionthroughvideoanalysis AT erisatoshimokawara anendtoendframeworkforneonatalasphyxiadetectionthroughvideoanalysis AT naoyukikubota anendtoendframeworkforneonatalasphyxiadetectionthroughvideoanalysis AT kurnianingsih endtoendframeworkforneonatalasphyxiadetectionthroughvideoanalysis AT sounobukawa endtoendframeworkforneonatalasphyxiadetectionthroughvideoanalysis AT melyananurulwidyawati endtoendframeworkforneonatalasphyxiadetectionthroughvideoanalysis AT ciptapramana endtoendframeworkforneonatalasphyxiadetectionthroughvideoanalysis AT nursenobayuaji endtoendframeworkforneonatalasphyxiadetectionthroughvideoanalysis AT afandinurazizthohari endtoendframeworkforneonatalasphyxiadetectionthroughvideoanalysis AT dwianahendrawati endtoendframeworkforneonatalasphyxiadetectionthroughvideoanalysis AT erisatoshimokawara endtoendframeworkforneonatalasphyxiadetectionthroughvideoanalysis AT naoyukikubota endtoendframeworkforneonatalasphyxiadetectionthroughvideoanalysis |