Condition-fusion-and-hiding denoising diffusion model for electrocardiogram lead reconstruction

Single-lead electrocardiograms can be collected by portable devices, but such devices are not suitable for the mainstream 12-lead electrocardiogram diagnoses. Some studies have attempted to reconstruct missing electrocardiogram leads. However, these methods focus only on signal reconstruction without using annotation information of electrocardiogram records. In this paper, a "condition-fusion-and-hiding denoising diffusion probabilistic model" is proposed to use annotated text and diagnosis conclusions for promoting the generative model in learning electrocardiogram pathological knowledge. A two-step training strategy is designed. In the first stage, a condition-fusion module combines description text with electrocardiogram signals to guide pathological learning. In the second stage, a condition-hiding module supervises pathological feature reconstruction through representation learning, without inputting annotated text. With this strategy, the model reconstructs fixed-length, 10-s 12-lead electrocardiograms from single-lead inputs, emphasizing pathological waveforms and rhythms. Experimental results show that the proposed method outperforms state-of-the-art approaches in terms of reconstruction performance and classification consistency.