Physics-Informed Transfer Learning to Enhance Sleep Staging

Conclusion: Machine learning performance can be improved using data synthesized using physical models. Significance: Our approach represents a new form of transfer learning and demonstrates that incorporating domain knowledge of electrophysiological modeling can improve machine learning results for sleep staging tasks. We expect this approach to be particularly useful for EEG data which is hard to collect, or which is obtained using unusual electrode configurations.
Source: IEEE Transactions on Biomedical Engineering - Category: Biomedical Engineering Source Type: research