Classification of Central and Obstructive Sleep Apnea Using Respiratory Inductance Plethysmography and Convolutional Neural Networks

Author:

Stewart Matthew1,Higginson Caitlin2,Lariviere-Chartier Julien3,Lee Elliott4,Green James1,Goubran Rafik1,Knoefel Frank5,Robillard Rebecca6

Affiliation:

1. Carleton University,Systems and Computer Engineering,Ottawa,Canada

2. University of Ottawa Institute of Mental Health Research at The Royal,Sleep Research Unit,Ottawa,Canada

3. Bruyere Research Institute Systems and Computer Engineering, Carleton University,Ottawa,Canada

4. University of Ottawa Sleep Research Unit, University of Ottawa Institute of Mental Health Research at The Royal,Department of Psychiatry,Ottawa,Canada

5. University of Ottawa Bruyere Research Institute Systems and Computer Engineering, Carleton University,Faculty of Medicine,Ottawa,Canada

6. School of Psychology, University of Ottawa Sleep Research Unit, University of Ottawa Institute of Mental Health Research at The Royal,Ottawa,Canada

Publisher

IEEE

Reference17 articles.

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

2. Sleep Apnea Detection Based on Thoracic and Abdominal Movement Signals of Wearable Piezoelectric Bands

3. Automatic apnea-hypopnea events detection using an alternative sensor

4. Non-invasive Diagnosis of Sleep Apnoea Using ECG and Respiratory Bands

5. Automated Detection of Obstructive Sleep Apnoea by Single-lead ECG through ELM Classification This study aims to provide automated screening of obstructive sleep apnoea (GSA) by ECG signal processing. Using ECG as an GSA diagnosis tool is an attractive;Sadr;Comput. Cardiol.,2010

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