/awesome-Sleep-Health-AI

MIT LicenseMIT

awesome-AI-sleep-health Awesome

Sleep is a crucial biological process, and has long been recognised as an essential determinant of human health and performance.It is now understood that the associations between diet, physical activity, mental health and sleep are bidirectional. Thus, poor sleep, high levels of inactivity and a poor diet comprise inter-related public health priorities. The mental and physical impairments associated with a single night of poor sleep can outweigh those caused by an equivalent lack of exercise or food. In recent years, AI for sleep and health has attracted the attention of the industry and academia. An up-to-date & curated list of Awesome IMU-based Human Activity Recognition(Ubiquitous Computing) papers, methods & resources.

Contributions welcome. Add links through pull requests or create an issue to start a discussion.

Background

  • Interdependency between heart rate variability and sleep EEG: linear/non-linear? (Clinical Neurophysiology)
  • An Overview of Heart Rate Variability Metrics and Norms[Paper]

Papers

Based on Deep Learning/Machine Learning

2021

  • XSleepNet: Multi-view sequential model for automatic sleep staging (T-PAMI) [Paper]
  • Heart rate variability and obstructive sleep apnea: Current perspectives and novel technologies [Paper]
  • Ubi-SleepNet: Advanced Multimodal Fusion Techniques for Three-stage Sleep Classification Using Ubiquitous Sensing (IMWUT) [Paper]
  • A deep transfer learning approach for wearable sleep stage classification with photoplethysmography (Nature NPJ Digital Medicine) [Paper]

2020

  • Deep Learning for Diagnosis and Classification of Obstructive Sleep Apnea: A Nasal Airflow-Based Multi-Resolution Residual Network (Nature, Scientific report) [paper]
  • A machine learning-based test for adult sleep apnoea screening at home using oximetry and airflow (Nature, Scientific report) [paper]
  • Application of machine learning to predict obstructive sleep apnea syndrome severity [paper]
  • Making Sense of Sleep: Multimodal Sleep Stage Classification in a Large, Diverse Population Using Movement and Cardiac Sensing (IMWUT)[Paper]
  • Deep learning enables sleep staging from photoplethysmogram for patients with suspected sleep apnea (Sleep) [Paper]
  • Robust, ECG-based detection of Sleep-disordered breathing in large population-based cohorts (Sleep) [Paper]

2019

  • Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification [Paper]
    • SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging [Paper]

2017

  • Single Sensor Techniques for Sleep Apnea Diagnosis Using Deep Learning [Paper]

Based on Domain Knowledge

  • An Electrocardiogram-Based Technique to Assess Cardiopulmonary Coupling During Sleep (Sleep)(This method is adopted as the standard for Huawei Watch Sleep Monitoring)
  • Big-Data, AI and Sleep[paper]