A Multimodal Dataset of 21,412 Recorded Nights for Sleep and Respiratory
  Research
Alon Diament Maria Gorodetski Adam Jankelow Ayya Keshet Tal Shor Daphna Weissglas-Volkov Hagai Rossman Eran Segal
This study introduces a novel, rich dataset obtained from home sleep apnea
tests using the FDA-approved WatchPAT-300 device, collected from 7,077
participants over 21,412 nights. The dataset comprises three levels of sleep
data: raw multi-channel time-series from sensors, annotated sleep events, and
computed summary statistics, which include 447 features related to sleep
architecture, sleep apnea, and heart rate variability (HRV). We present
reference values for Apnea/Hypopnea Index (AHI), sleep efficiency, Wake After
Sleep Onset (WASO), and HRV sample entropy, stratified by age and sex.
Moreover, we demonstrate that the dataset improves the predictive capability
for various health related traits, including body composition, bone density,
blood sugar levels and cardiovascular health. These results illustrate the
dataset's potential to advance sleep research, personalized healthcare, and
machine learning applications in biomedicine.