A Nonlinear Approach to FECG Signal Enhancement Using an Ensemble of Convolutional Neural Networks

Abstract

Fetal electrocardiogram (FECG) denoising is a vital aspect of prenatal monitoring, critical for precise fetal health evaluation. While convolutional neural networks (CNNs) are widely recognized for their strength in processing multidimensional data, their adaptation for one-dimensional signals like FECG remains an emerging frontier. This article introduces a novel ensemble CNN approach specifically designed to enhance FECG signals by leveraging CNN architectures hierarchical feature extraction capabilities. Extensive testing on FECG datasets with varying noise levels highlights the model’s robust performance, showing significant improvements in performance metrics, including signal-to-noise ratio (SNR) and mean squared error (MSE). The ensemble CNN approach excels at isolating FECG components from noise, achieving enhanced signal clarity and fidelity. These finding reveal the promising potential of ensemble CNN-based techniques to advance FECG denoising, thereby enhancing the accuracy of fetal health assessments in clinical applications.