Forecasting-Based Biomedical Time-series Data Synthesis for Open Data and Robust AI
This addresses data scarcity and privacy issues for biomedical AI researchers, though it is incremental as it applies existing forecasting methods to a specific domain.
The paper tackles the problem of limited biomedical time-series data availability due to privacy and resource constraints by proposing a forecasting-based framework for synthetic data generation, which replicates EEG and EMG signals with high fidelity and boosts AI model performance.
The limited data availability due to strict privacy regulations and significant resource demands severely constrains biomedical time-series AI development, which creates a critical gap between data requirements and accessibility. Synthetic data generation presents a promising solution by producing artificial datasets that maintain the statistical properties of real biomedical time-series data without compromising patient confidentiality. We propose a framework for synthetic biomedical time-series data generation based on advanced forecasting models that accurately replicates complex electrophysiological signals such as EEG and EMG with high fidelity. These synthetic datasets preserve essential temporal and spectral properties of real data, which enables robust analysis while effectively addressing data scarcity and privacy challenges. Our evaluations across multiple subjects demonstrate that the generated synthetic data can serve as an effective substitute for real data and also significantly boost AI model performance. The approach maintains critical biomedical features while provides high scalability for various applications and integrates seamlessly into open-source repositories, substantially expanding resources for AI-driven biomedical research.