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.

应对有限数据可用性、数据不平衡、标签复杂性和测量噪声干扰等问题，我们引入了 BioDiffusion，一种基于扩散的概率模型，用于合成多变量生物医学信号。通过利用这些合成信号，可以有效解决上述挑战，提高与生物医学信号相关的机器学习任务的准确性。此外，与当前领先的时间序列生成模型相比，实证证据表明 BioDiffusion 在生物医学信号生成质量方面表现优异。