Cross-Database Evaluation of Deep Learning Methods for Intrapartum Cardiotocography Classification
Continuous monitoring of fetal heart rate (FHR) and uterine contractions (UC), otherwise known as cardiotocography (CTG), is often used to assess the risk of fetal compromise during labor. However, interpreting CTG recordings visually is challenging for clinicians, given the complexity of CTG patter...
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IEEE
2025-01-01
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| Series: | IEEE Journal of Translational Engineering in Health and Medicine |
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| Online Access: | https://ieeexplore.ieee.org/document/10912500/ |
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| author | Lochana Mendis Debjyoti Karmakar Marimuthu Palaniswami Fiona Brownfoot Emerson Keenan |
| author_facet | Lochana Mendis Debjyoti Karmakar Marimuthu Palaniswami Fiona Brownfoot Emerson Keenan |
| author_sort | Lochana Mendis |
| collection | DOAJ |
| description | Continuous monitoring of fetal heart rate (FHR) and uterine contractions (UC), otherwise known as cardiotocography (CTG), is often used to assess the risk of fetal compromise during labor. However, interpreting CTG recordings visually is challenging for clinicians, given the complexity of CTG patterns, leading to poor sensitivity. Efforts to address this issue have focused on data-driven deep-learning methods to detect fetal compromise automatically. However, their progress is impeded by limited CTG training datasets and the absence of a standardized evaluation workflow, hindering algorithm comparisons. In this study, we use a private CTG dataset of 9,887 CTG recordings with pH measurements and 552 CTG recordings from the open-access CTU-UHB dataset to conduct a cross-database evaluation of six deep-learning models for fetal compromise detection. We explore the impact of input selection of FHR and UC signals, signal pre-processing, downsampling frequency, and the influence of removing intermediate pH samples from the training dataset. Our findings reveal that using only FHR and pre-processing FHR with artefact removal and interpolation provides a significant improvement to classification performance for some model architectures while excluding intermediate pH samples did not significantly improve performance for any model. From our comparison of the six models, ResNet exhibited the strongest fetal compromise classification performance across both databases at a downsampling rate of 1Hz. Finally, class activation maps from highly contributing signal regions in the ResNet model aligned with clinical knowledge of compromised FHR patterns, highlighting the model’s interpretability. These insights may serve as a standardized reference for developing and comparing future works in this domain. Clinical and Translational Impact: This study provides a standardized workflow for comparing deep-learning methods for CTG classification. Ensuring new methods show generalizability and interpretability will improve their robustness and applicability in clinical settings. |
| format | Article |
| id | doaj-art-4acf503492264be7bbd5c11d879bdbc2 |
| institution | DOAJ |
| issn | 2168-2372 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Translational Engineering in Health and Medicine |
| spelling | doaj-art-4acf503492264be7bbd5c11d879bdbc22025-08-20T02:49:20ZengIEEEIEEE Journal of Translational Engineering in Health and Medicine2168-23722025-01-011312313510.1109/JTEHM.2025.354840110912500Cross-Database Evaluation of Deep Learning Methods for Intrapartum Cardiotocography ClassificationLochana Mendis0https://orcid.org/0009-0003-6670-6719Debjyoti Karmakar1https://orcid.org/0009-0007-2301-1326Marimuthu Palaniswami2https://orcid.org/0000-0002-3635-4252Fiona Brownfoot3Emerson Keenan4https://orcid.org/0000-0003-1966-2293Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, VIC, AustraliaDepartment of Obstetrics and Gynaecology, Obstetric Diagnostics and Therapeutics Group, The University of Melbourne, Heidelberg, VIC, AustraliaDepartment of Electrical and Electronic Engineering, The University of Melbourne, Parkville, VIC, AustraliaDepartment of Obstetrics and Gynaecology, Obstetric Diagnostics and Therapeutics Group, The University of Melbourne, Heidelberg, VIC, AustraliaDepartment of Electrical and Electronic Engineering, The University of Melbourne, Parkville, VIC, AustraliaContinuous monitoring of fetal heart rate (FHR) and uterine contractions (UC), otherwise known as cardiotocography (CTG), is often used to assess the risk of fetal compromise during labor. However, interpreting CTG recordings visually is challenging for clinicians, given the complexity of CTG patterns, leading to poor sensitivity. Efforts to address this issue have focused on data-driven deep-learning methods to detect fetal compromise automatically. However, their progress is impeded by limited CTG training datasets and the absence of a standardized evaluation workflow, hindering algorithm comparisons. In this study, we use a private CTG dataset of 9,887 CTG recordings with pH measurements and 552 CTG recordings from the open-access CTU-UHB dataset to conduct a cross-database evaluation of six deep-learning models for fetal compromise detection. We explore the impact of input selection of FHR and UC signals, signal pre-processing, downsampling frequency, and the influence of removing intermediate pH samples from the training dataset. Our findings reveal that using only FHR and pre-processing FHR with artefact removal and interpolation provides a significant improvement to classification performance for some model architectures while excluding intermediate pH samples did not significantly improve performance for any model. From our comparison of the six models, ResNet exhibited the strongest fetal compromise classification performance across both databases at a downsampling rate of 1Hz. Finally, class activation maps from highly contributing signal regions in the ResNet model aligned with clinical knowledge of compromised FHR patterns, highlighting the model’s interpretability. These insights may serve as a standardized reference for developing and comparing future works in this domain. Clinical and Translational Impact: This study provides a standardized workflow for comparing deep-learning methods for CTG classification. Ensuring new methods show generalizability and interpretability will improve their robustness and applicability in clinical settings.https://ieeexplore.ieee.org/document/10912500/Cardiotocographydeep learningfetal compromisefetal heart ratetime-series classification |
| spellingShingle | Lochana Mendis Debjyoti Karmakar Marimuthu Palaniswami Fiona Brownfoot Emerson Keenan Cross-Database Evaluation of Deep Learning Methods for Intrapartum Cardiotocography Classification IEEE Journal of Translational Engineering in Health and Medicine Cardiotocography deep learning fetal compromise fetal heart rate time-series classification |
| title | Cross-Database Evaluation of Deep Learning Methods for Intrapartum Cardiotocography Classification |
| title_full | Cross-Database Evaluation of Deep Learning Methods for Intrapartum Cardiotocography Classification |
| title_fullStr | Cross-Database Evaluation of Deep Learning Methods for Intrapartum Cardiotocography Classification |
| title_full_unstemmed | Cross-Database Evaluation of Deep Learning Methods for Intrapartum Cardiotocography Classification |
| title_short | Cross-Database Evaluation of Deep Learning Methods for Intrapartum Cardiotocography Classification |
| title_sort | cross database evaluation of deep learning methods for intrapartum cardiotocography classification |
| topic | Cardiotocography deep learning fetal compromise fetal heart rate time-series classification |
| url | https://ieeexplore.ieee.org/document/10912500/ |
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