Yolo4Apnea: Real-Time Detection of Obstructive Sleep Apnea

Sondre Hamnvik, Pierre Bernabé, Sagar Sen

IJCAI 2020 pp. 5234-5236

doi:10.24963/IJCAI.2020/754 /ijcai/2020/hamnvik2020ijcai-yolo/

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

PDF IJCAI Semantic Scholar

Cite

Text

Hamnvik et al. "Yolo4Apnea: Real-Time Detection of Obstructive Sleep Apnea." International Joint Conference on Artificial Intelligence, 2020. doi:10.24963/IJCAI.2020/754

Markdown

[Hamnvik et al. "Yolo4Apnea: Real-Time Detection of Obstructive Sleep Apnea." International Joint Conference on Artificial Intelligence, 2020.](https://mlanthology.org/ijcai/2020/hamnvik2020ijcai-yolo/) doi:10.24963/IJCAI.2020/754

BibTeX

@inproceedings{hamnvik2020ijcai-yolo,
  title     = {{Yolo4Apnea: Real-Time Detection of Obstructive Sleep Apnea}},
  author    = {Hamnvik, Sondre and Bernabé, Pierre and Sen, Sagar},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2020},
  pages     = {5234-5236},
  doi       = {10.24963/IJCAI.2020/754},
  url       = {https://mlanthology.org/ijcai/2020/hamnvik2020ijcai-yolo/}
}