Radar2ECG: Multi-Scale Bottleneck Fusion and Cross-modal Semantic Distillation for Conditional Electrocardiogram Generation from Radar Heart Sound

The field of conditional Electrocardiogram(ECG) generation focuses on generating specified ECGs under given conditions for medical purposes. Existing methods are typically based on conditions of simple inputs like text or lead types. However, they struggle to handle the complexity of radar heart sound signals due to the lack of effective feature extraction, which hinders capturing the intricate waveform correlations between radar heart sounds and ECGs. Considering that radar-detected heart sound signals are contactless, the application is of essential value in a real-world deployment like sleep scenarios. Moreover, no prior approaches have addressed this specific task. To tackle this challenge, we propose a novel multi-scale feature fusion network framework, Radar2ECG. This model leverages pre-trained autoencoders for heart sound and ECG signals, aligning and integrating multi-layer features through a bottleneck structure to enhance receptive fields and reduce redundant features, thereby capturing the correlations between heart sounds and ECGs. Finally, we employ knowledge distillation to transfer knowledge from the ECG decoder to the heart sound decoder. We present three anomaly type datasets and extensive experiments conducted on both normal and abnormal datasets demonstrate that our method outperforms existing models in both accuracy and robustness. The multi-scale feature fusion significantly improves performance, showcasing strong potential in ECG generation and heart sound anomaly detection tasks.