E-NN: FETAL HEART RATE ANALYSIS DURING PREGNANCY USING AN ENSEMBLE DENSENET-BC AND CONVOLUTIONAL NEURAL NETWORK-BASED FRAMEWORK - Yousuf Iqbal; Shabir Hussain; Muhammad Ayoub; Muhammad Faial Idrees; Sana Munir; Umair Rafiq; Shumaila Majeed (Journal of Population Therapeutics and Clinical Pharmacology, 2024)
Purpose: This study aims to develop and validate a deep learning framework based on a weighted voting mechanism to automatically analyze the fetal heart rate (FHR) in electronic fetal monitoring (EFM) during pregnancy. The aim is to accurately divide the FHR into normal or pathological states, thus reducing the dependence on doctors’ experience and potentially preventing unnecessary interventions such as cesarean section. Method: The study implemented CNN and DenseNet-BC models based on the weighted voting mechanism to analyze the FHR as normal or pathological. The multi-model training method based on a down-sampling algorithm deals with imbalanced data. The effectiveness of the proposed CNN combined with the multi-model training method is evaluated using an open database named CTU- UHB. Results: The experiment results show that the proposed method performs well and is stable on the CTU-UHB dataset. The CNN model based on a weighted voting mechanism and multi-model training method achieved high accuracy of 97.67% in automatically analyzing the FHR as normal or pathological. This demonstrates the potential for using deep learning techniques to improve the accuracy of FHR monitoring and reduce the dependence on doctors’ experience. Conclusion: The implemented deep learning framework combined with the multi-model training method shows promising results in automatically analyzing the FHR as normal or pathological, indicating the potential for using deep learning techniques to improve the accuracy and objectivity of FHR monitoring during pregnancy. This study has implications for reducing unnecessary interventions and improving maternal and fetal outcomes.