CTGFusionNet: fusion of deep learning models for predicting fetal distress-a multimodal approach

Cardiotocography (CTG) is a widely used technique for fetal monitoring. This study presents CTGFusionNet, a novel multimodal adaptive framework designed for prenatal analysis. The framework integrates attention-based adaptive Bi-Directional Convolutional Neural Networks (Bi-CNN) with Long Short-Term Memory (LSTM) networks to improve the accuracy of fetal distress prediction. The methodology begins with an initial data preprocessing phase, followed by signal segmentation and enhancement. Thereafter, the FHR and UC signals are transformed into two-dimensional representations using embedding layers and subsequently integrated through concatenation. The spatial features of the synchronized signals are extracted using the proposed adaptive Bi-CNN. Multi-head attention is then applied to emphasize the most relevant information, and the temporal features are captured using an LSTM network. In the final stage, the most relevant features from the perinatal clinical data are identified using the Relief, Lasso, and Information Gain algorithms and then integrated with the processed signals. Furthermore, classification results are obtained using a fully connected layer and sigmoid function. The results demonstrate that CTGFusionNet leads to significant improvements in performance measures, namely accuracy, sensitivity, and specificity, with values of 97.85%, 97.07%, and 98.65%, respectively. This suggests that CTGFusionNet-a multimodal approach that combines FHR, UC, and clinical data, provides a more reliable and precise method for the early detection and prediction of fetal distress. The proposed approach has the potential to significantly improve prenatal care outcomes by enabling accurate interventions.