SSSD-ECG-nle: New Label Embeddings with Structured State-Space Models for ECG generation
This work addresses privacy issues in ECG data for medical applications, but it is incremental as it builds on existing SSSD-ECG with modifications.
The paper tackles the problem of generating synthetic electrocardiogram (ECG) signals to address privacy concerns in medical data distribution, using diffusion models with structured state spaces, and demonstrates efficiency in downstream tasks with physician evaluations.
An electrocardiogram (ECG) is vital for identifying cardiac diseases, offering crucial insights for diagnosing heart conditions and informing potentially life-saving treatments. However, like other types of medical data, ECGs are subject to privacy concerns when distributed and analyzed. Diffusion models have made significant progress in recent years, creating the possibility for synthesizing data comparable to the real one and allowing their widespread adoption without privacy concerns. In this paper, we use diffusion models with structured state spaces for generating digital 10-second 12-lead ECG signals. We propose the SSSD-ECG-nle architecture based on SSSD-ECG with a modified conditioning mechanism and demonstrate its efficiency on downstream tasks. We conduct quantitative and qualitative evaluations, including analyzing convergence speed, the impact of adding positive samples, and assessment with physicians' expert knowledge. Finally, we share the results of physician evaluations and also make synthetic data available to ensure the reproducibility of the experiments described.