A Hybrid Deep Learning and Image-like Framework for Fetal Heart Rate Estimation Using Phonocardiography Signals

One of the most important parameters in fetal monitoring is the fetal heart rate (fHR). However, due to the nature of this signal, several non-invasive techniques have been developed for fetal monitoring, including Cardiotocography (CTG), Doppler Echocardiography (Doppler Echo), fetal electrocardiography (fECG), fetal phonocardiography (fPCG), fetal Magnetocardiography (fMCG), and fetal Photoplethysmography (fPPG). Fetal phonocardiography is a passive, safe, and non-invasive method for estimating fHR, in which an acoustic signal is acquired through a transducer placed on the mother’s abdomen to capture sounds associated with fetal cardiac activity. In this paper, we propose a hybrid framework for fHR estimation that integrates digital signal processing techniques with a deep learning approach. The method employs a Hermite Transform (HT)-based bio-inspired vision model for fPCG signal denoising, the Hilbert transform to extract the signal envelope, and Mel-Frequency Cepstral Coefficients (MFCCs) to generate spectrogram-based features. Then, these features, represented as image-like, are used to train and evaluate a hybrid architecture that synergistically combines convolutional neural networks (CNNs) with Bidirectional Long Short-Term Memory (BiLSTM). BiLSTM is a deep learning model that processes sequential information in both forward and backward directions, thereby extending the memory capabilities of conventional Recurrent Neural Networks (RNNs). Thus, CNNs are used to capture intricate spatial morphologies of the fPCG signals, whereas BiLSTM captures their temporal evolution across consecutive time windows. Additionally, to assess its generalization capability, the proposed method is evaluated on two publicly available fPCG datasets and benchmarked against several state-of-the-art approaches. The experimental results demonstrate that the proposed method achieves competitive performance across different datasets, highlighting its effectiveness and robustness.