This thesis investigates the application of Denoising Diffusion Probabilistic Models (DDPM) for synthesizing 12-lead Electrocardiogram (ECG) signals. Utilizing classifier-free guidance along with the bidirectional Mamba State Space Model (SSM) within a DiffWave framework, we developed a model capable of both unconditional and conditional ECG signal generation.

Despite the promising potential of Mamba for enhancing temporal signal encoding, its performance compared to the time-invariant SSM model, S4, was either worse or inconclusive, likely due to the limitations of current metrics. Visual assessments often contradicted the automated metrics, indicating a significant gap in current evaluation methods. We also explored the feasibility of training models on all 12 leads, contrasting previous studies that used fewer leads. Our findings indicate that diffusion models can adequately learn the linear relationships between leads without significantly increasing model size.

Overall, our work reduces the need for extensive data pre- or post-processing, streamlines ECG data generation, and highlights the limitations of existing evaluation methodologies, suggesting the need for further evaluation.