Contrastive Learning Advances Sleep Science: Superior Multi-Modal Model Enhances Disorder Detection | Synced

In a new paper SleepFM: Multi-modal Representation Learning for Sleep Across Brain Activity, ECG and Respiratory Signals, a research team introduces SleepFM, the first attempt at developing a multi-modal contrastive learning (CL) approach for PSG analysis, outperforming baselines in tasks like demographic attribute prediction and sleep stage classification.