Abstract：Distress or acidosis resulted from hypoxia can lead to irreversible consequences such as fetal organ damage even death. Intelligent cardiotocography (ICTG) is an important tool for monitoring fetal health by continuously and synchronously recording fetal heart rate (FHR) signals in late pregnancy. The existing FHR-based unimodal ICTG combined with machine learning algorithms for assisted diagnosis method neglects the complementarity between different modalities data of FHR and the influence of clinical physiological factors on the fetus. In this study, we proposed a multimodal learning model with adaptive weighted fusion of images and text. In particular, we designed a multimodal feature extraction network consisting of a Vision Transformer (ViT) image encoder and a convolutional neural networks (CNN) text encoder. Adaptive weighting fusion (AWF) was proposed to fuse multimodal features, instead of direct concatenates. Considering the impact of realistic risk factors, textual data can be constructed by combining the clinical data and the morphological features of FHR signals. Meanwhile, the Markov transfer field was employed to convert signals into images as complementary data. Using 200 sets of public clinical real-world FHR signals, multiple performance comparisons, parameter optimization, and ablation experiments were carried out. The experiment results indicated that the Multi-FHRNet outperformed traditional unimodal ICTG methods, withthe highest accuracy, precision, recall, and F1-score of 96.02%, 93.10%, 99.29%, 95.45% and 93.48%, respectively. The algorithm in this paper may help to detect and treat abnormal fetuses during labor.
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