Leads-Adaptive Fetal Electrocardiogram Extraction Using Attention-Based BiLSTM.

Extracting the fetal electrocardiogram (fECG) from the abdominal electrocardiogram (AECG) will  help clinicians accurately discern fetal cardiac rhythm patterns. Nevertheless, the intricacies inherent in the clinical setting often precipitate signal anomalies within AECG recordings, thereby rendering traditional extraction methodologies suboptimal. This work proposes a deep learning-based fECG extraction method designed for the adaptive extraction of fECG signals from multi-lead AECG inputs. The proposed methodology is predicated upon a bidirectional long short-term memory (BiLSTM) architecture, augmented with a deep supervision subnetwork and an attention mechanism module. The attention module quantifies the inter-channel relevance of the input AECG through the computation of attention weights, facilitating subsequent feature fusion along the channel axis. This process effectively mitigates the impact of defective channels on the output. Comprehensive evaluations were conducted on publicly available datasets, encompassing scenarios with channel loss and benchmarked against established models. The results demonstrate the proposed model's efficacy in extracting fECG signals under diverse channel defect conditions. Ablation studies further validate the critical role of the attention module in enhancing the model's resilience to channel anomalies. Additionally, the reliability of the extracted fECG signals was corroborated through experiments involving input signal masking. The method proposed in this work is helpful for the clinical deployment of the fECG extraction in fetal cardiac rhythm assessment.