Yolo4Apnea: Real-time Detection of Obstructive Sleep Apnea

Sondre Hamnvik; Pierre Bernabé; Sagar Sen

IJCAI 2020poster0 citations

Yolo4Apnea: Real-time Detection of Obstructive Sleep Apnea

Sondre Hamnvik, Pierre Bernabé, Sagar Sen

Abstract

Obstructive sleep apnea is a serious sleep disorder that affects an estimated one billion adults worldwide. It causes breathing to repeatedly stop and start during sleep which over years increases the risk of hypertension, heart disease, stroke, Alzheimer's, and cancer. In this demo, we present Yolo4Apnea a deep learning system extending You Only Look Once (Yolo) system to detect sleep apnea events from abdominal breathing patterns in real-time enabling immediate awareness and action. Abdominal breathing is measured using a respiratory inductance plethysmography sensor worn around the stomach. The source code is available at https://github.com/simula-vias/Yolo4Apnea

Machine Learning: generalComputer Vision: generalUncertainty in AI: general

BibTeX

@inproceedings{ijcai2020p754,
  title     = {Yolo4Apnea: Real-time Detection of Obstructive Sleep Apnea},
  author    = {Hamnvik, Sondre and Bernabé, Pierre and Sen, Sagar},
  booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on
               Artificial Intelligence, {IJCAI-20}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Christian Bessiere},
  pages     = {5234--5236},
  year      = {2020},
  month     = {7},
  note      = {Demos},
  doi       = {10.24963/ijcai.2020/754},
  url       = {https://doi.org/10.24963/ijcai.2020/754},
}