A Multimodal Dataset of 21,412 Recorded Nights for Sleep and Respiratory Research

TL;DR

This paper presents a comprehensive, multimodal sleep dataset from over 7,000 participants, enabling improved sleep and health research through detailed annotations, features, and predictive analyses.

Contribution

The study introduces a large, multi-level sleep dataset with extensive annotations and features, facilitating advanced research in sleep disorders and health prediction.

Findings

Dataset improves prediction of health traits

Provides normative sleep and HRV values

Enhances machine learning applications in sleep research

Abstract

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.