BioDiffusion: A Versatile Diffusion Model for Biomedical Signal Synthesis
This addresses data scarcity and noise issues for biomedical machine learning tasks, though it appears incremental as an adaptation of diffusion models to a specific domain.
The paper tackles the problem of limited and noisy biomedical signal data by introducing BioDiffusion, a diffusion model for synthesizing high-fidelity signals, which outperforms existing time-series generative models in quality.
Machine learning tasks involving biomedical signals frequently grapple with issues such as limited data availability, imbalanced datasets, labeling complexities, and the interference of measurement noise. These challenges often hinder the optimal training of machine learning algorithms. Addressing these concerns, we introduce BioDiffusion, a diffusion-based probabilistic model optimized for the synthesis of multivariate biomedical signals. BioDiffusion demonstrates excellence in producing high-fidelity, non-stationary, multivariate signals for a range of tasks including unconditional, label-conditional, and signal-conditional generation. Leveraging these synthesized signals offers a notable solution to the aforementioned challenges. Our research encompasses both qualitative and quantitative assessments of the synthesized data quality, underscoring its capacity to bolster accuracy in machine learning tasks tied to biomedical signals. Furthermore, when juxtaposed with current leading time-series generative models, empirical evidence suggests that BioDiffusion outperforms them in biomedical signal generation quality.