Fetal Health Assessment Using Multimodal Time-Series Data and Attention Neural Networks

Abstract

To address the issues of high subjectivity in traditional interpretation methods, the widespread neglect of gestational age and maternal age in existing research, small dataset sizes, class imbalance, and limited model performance, this paper proposes a convolutional long shortterm memory network based on a multimodal attention mechanism (MACLM) to assist in the assessment of fetal health status. Based on 22,813 fetal monitoring records, the study embeds gestational age information using sinusoidal positional encoding and combines an attention mechanism with a convolutional neural network–long short-term memory (CNN-LSTM) architecture to achieve multimodal feature fusion and temporal modeling. Experimental results show that positional encoding improves the model’s accuracy by 4%–7% and reduces the loss by 46%–50%. MACLM ultimately achieved a classification accuracy of 97.55%, with an interpretation time of only 0.0234 seconds for 100 fetal monitoring cases. Its overall performance significantly outperformed other comparison models, with a maximum improvement of 92.7%.