Intelligent antepartum fetal monitoring via deep learning and fusion of cardiotocographic signals and clinical data

Mar 23, 2023Health information science and systems

Smart fetal monitoring before birth using deep learning on heart signals and clinical data

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Abstract

An accuracy of 90.77% was achieved in fetal status assessment using a multimodal deep learning architecture.

Multimodal deep learning architecture integrates CTG feature extraction with clinical data for improved fetal monitoring.
The model combines high-level features from CTG signals and maternal information for classification purposes.
Results indicate an area under the curve (AUC) of 0.9201, suggesting strong performance in fetal health assessment.
The light gradient boosting machine (LGBM) classifier effectively addressed data imbalance, achieving normal-F1 and abnormal-F1 scores of 0.9376 and 0.8223, respectively.

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PURPOSE: Cardiotocography (CTG), which measures uterine contraction (UC) and fetal heart rate (FHR), is a crucial tool for assessing fetal health during pregnancy. However, traditional computerized cardiotocography (cCTG) approaches have non-negligible calibration errors in feature extraction and heavily rely on the expertise and prior experience to define diagnostic features from CTG or FHR signals. Although previous works have studied deep learning methods for extracting CTG or FHR features, these methods still neglect the clinical information of pregnant women.

METHODS: In this paper, we proposed a multimodal deep learning architecture (MMDLA) for intelligent antepartum fetal monitoring that is capable of performing automatic CTG feature extraction, fusion with clinical data and classification. The multimodal feature fusion was achieved by concatenating high-level CTG features, which were extracted from preprocessed CTG signals via a convolution neural network (CNN) with six convolution layers and five fully connected layers, and the clinical data of pregnant women. Eventually, light gradient boosting machine (LGBM) was implemented as fetal status assessment classifier. The effectiveness of MMDLA was evaluated using a dataset of 16,355 cases, each of which includes FHR signal, UC signal and pertinent clinical data like maternal age and gestational age.

RESULTS: With an accuracy of 90.77% and an area under the curve (AUC) value of 0.9201, the multimodal features performed admirably. The data imbalance issue was also effectively resolved by the LGBM classifier, with a normal-F1 value of 0.9376 and an abnormal-F1 value of 0.8223.

CONCLUSION: In summary, the proposed MMDLA is conducive to the realization of intelligent antepartum fetal monitoring.

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