Hyatt Moore
Hyatt Moore has 2 papers in the ChatPPG research atlas (2024–2026), linked to 2 models, with work using 1 dataset and 19 co-authors in the atlas. Main topics: sleep, EEG, contrastive, PSG, PPG, accelerometry.
Papers
- Learning transferable human physiology from two million hours of sleep with SleepFM-22026 · arXiv preprint, cs.AI
Rahul Thapa, Christopher Sun, William Theodor Lehn-Schiøler, Sophia Claire Kivelson, Umaer Hanif, Hyatt Moore IV, Harrison G. Zhang, Hafsa Ahmed, Marcus Dige, Niels R. Lorenzen, Elisabeth Roxane M.
- Venue: arXiv preprint, cs.AI
- Subjects: 282,511 PSG recordings from 26 cohorts (235,865 used for pretraining); counts reported as recordings, not subjects
- Signals: EEG, EOG, EMG, ECG, respiration, PPG, ACC
- SleepFM: Multi-modal Representation Learning for Sleep Across Brain Activity, ECG and Respiratory Signals2024 · arXiv preprint
SleepFM is a multimodal sleep foundation model trained on more than 100,000 hours of polysomnography from over 14,000 people, covering brain, heart and breathing signals. A leave-one-out contrastive approach beat standard pairwise contrastive learning. Simple models on its embeddings beat an end-to-end CNN on sleep staging (macro AUROC 0.88 vs 0.72).
- Published: arXiv preprint, 2024
- Data: 100,000+ hours PSG from 14,000+ participants
- Signals: EEG, ECG, respiration
Models
Datasets used in this work
- DREAMT: Dataset for Real-time sleep stage EstimAtion using Multisensor wearable Technology