Estimating the Severity of Obstructive Sleep Apnea Using ECG, Respiratory Effort and Neural Networks-Reference-Cited by-同舟云学术

Estimating the Severity of Obstructive Sleep Apnea Using ECG, Respiratory Effort and Neural Networks

Published:2024-07 Issue:7 Volume:28 Page:3895-3906
ISSN:2168-2194
Container-title:IEEE Journal of Biomedical and Health Informatics
language:
Short-container-title:IEEE J. Biomed. Health Inform.

Author:

Fonseca Pedro¹^ORCID,Ross Marco²^ORCID,Cerny Andreas²^ORCID,Anderer Peter²^ORCID,Schipper Fons¹^ORCID,Grassi Angela¹^ORCID,van Gilst Merel³^ORCID,Overeem Sebastiaan³^ORCID

Affiliation:

1. Philips Research, Eindhoven, The Netherlands

2. Philips Sleep and Respiratory Care, Vienna, Austria

3. Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands

Funder

Technische Universiteit Eindhoven

Ministerie van Economische Zaken en Klimaat

STW/IWT

Eindhoven MedTech Innovation Center

NIH-NHLBI Association of Sleep Disorders with Cardiovascular Health Across Ethnic Groups

National Heart, Lung, and Blood Institute

National Center for Advancing Translational Sciences

National Institutes of Health

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6221020/10582272/10485422.pdf?arnumber=10485422

Reference33 articles.

1. Rules for Scoring Respiratory Events in Sleep: Update of the 2007 AASM Manual for the Scoring of Sleep and Associated Events

2. Clinical Practice Guideline for Diagnostic Testing for Adult Obstructive Sleep Apnea: An American Academy of Sleep Medicine Clinical Practice Guideline

3. Wearable monitoring of sleep-disordered breathing: estimation of the apnea–hypopnea index using wrist-worn reflective photoplethysmography

4. Robust, ECG-based detection of Sleep-disordered breathing in large population-based cohorts

5. The National Sleep Research Resource: towards a sleep data commons