Abstract
Cardiotocography (CTG) is a critical component of prenatal fetal monitoring, offering essential multivariate time series data that enables healthcare professionals to assess fetal growth and implement timely interventions for abnormal conditions, ensuring optimal fetal well-being. However, conventional CTG interpretation are susceptible to individual clinical experience and inconsistencies in assessment guidelines. To address these limitations, this study investigates artificial intelligence algorithms for developing an objective fetal assessment method based on multivariate time-series signals of fetal heart rate and uterine contractions. We preprocess data from an open-source fetal heart rate and contraction database, addressing missing values and noise reduction, and enhance the dataset for reliable experimentation. We also propose multivariate time-series signal models, including MT-1DCG, A-BiGRU, and ST-1DCG. The performance of the MT-1DCG model is validated through multiple experiments, demonstrating superior results compared to A-BiGRU and ST-1DCG models. Standard evaluation metrics, including accuracy, sensitivity, specificity, and ROC, are employed to assess model performance. The proposed MT-1DCG model yields an accuracy of 95.15%, sensitivity of 96.20%, and specificity of 94.09% in the test set. These findings indicate that our method effectively evaluates fetal health status and can support obstetricians in clinical decision-making.
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Lu, Y., Liang, H., Yu, Z., Fu, X. (2023). MT-1DCG: A Novel Model for Multivariate Time Series Classification. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science, vol 14087. Springer, Singapore. https://doi.org/10.1007/978-981-99-4742-3_18
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DOI: https://doi.org/10.1007/978-981-99-4742-3_18
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