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Estimation of Respiratory Signals using Tracheal Sound and Movement

Nasim Montazeri Ghahjaverestan, Muammar Kabir, Kaiyin Zhu, Shumit Saha, Bojan Gavrilovic, Babak Taati, Hisham Alshaer, Azadeh Yadollahi
European Respiratory Journal 2019 54: OA3278; DOI: 10.1183/13993003.congress-2019.OA3278
Nasim Montazeri Ghahjaverestan
1KITE, Toronto Rehabilitation Institute-University Health Network; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada
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  • For correspondence: montazeri.nasim@gmail.com
Muammar Kabir
1KITE, Toronto Rehabilitation Institute-University Health Network; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada
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Kaiyin Zhu
2KITE, Toronto Rehabilitation Institute-University Health Network, Toronto, ON, Canada
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Shumit Saha
1KITE, Toronto Rehabilitation Institute-University Health Network; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada
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Bojan Gavrilovic
1KITE, Toronto Rehabilitation Institute-University Health Network; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada
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Babak Taati
1KITE, Toronto Rehabilitation Institute-University Health Network; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada
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Hisham Alshaer
3KITE, Toronto Rehabilitation Institute-University Health Network; BresoTech, Toronto, ON, Canada
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Azadeh Yadollahi
1KITE, Toronto Rehabilitation Institute-University Health Network; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada
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Abstract

Introduction: Sleep apnea (SA) is clinically assessed using respiratory signals: thoracoabdominal (TA) movements and airflow, recorded during overnight polysomnography (PSG). A simpler portable device with the ability of estimating respiratory signals is more convenient and cost-effective. Tracheal sound and movements can be conveniently recorded with simple instruments.

Objectives: To estimate respiratory signals using tracheal sounds and movements.

Methods: Participants >18 yrs with suspected sleep apnea underwent overnight PSG. Simultaneously, tracheal sound and movements were recorded using an embedded microphone and accelerator placed over the tracheal notch. Linear regression was used to estimate TA movements from sound energy and the range of movements. Also, the sound energy envelope was extracted as a surrogate of airflow level. Repeated measure correlation was used to calculate the agreement between reference and estimated signals in normal breathing, apneas and hypopneas.

Results: Forty participants (21 females, age: 49 ± 16, BMI: 29.5 ± 6.1, and AHI: 17.9 ± 19.6) were included. Estimated movements correlated significantly with TA movements during normal breathing (0.53±0.06*), hypopneas (0.65±0.07*) and apneas (0.71±0.05*). Airflow correlated with PSG-airflow during normal breathing (0.29±0.08*), hypopneas (0.47±0.07*) and apneas (0.63±0.09*). (*P<0.001)

Conclusion: Tracheal sounds and movements are promising tools for SA assessment.

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Footnotes

Cite this article as: European Respiratory Journal 2019; 54: Suppl. 63, OA3278.

This is an ERS International Congress abstract. No full-text version is available. Further material to accompany this abstract may be available at www.ers-education.org (ERS member access only).

  • Copyright ©the authors 2019
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Estimation of Respiratory Signals using Tracheal Sound and Movement
Nasim Montazeri Ghahjaverestan, Muammar Kabir, Kaiyin Zhu, Shumit Saha, Bojan Gavrilovic, Babak Taati, Hisham Alshaer, Azadeh Yadollahi
European Respiratory Journal Sep 2019, 54 (suppl 63) OA3278; DOI: 10.1183/13993003.congress-2019.OA3278

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Estimation of Respiratory Signals using Tracheal Sound and Movement
Nasim Montazeri Ghahjaverestan, Muammar Kabir, Kaiyin Zhu, Shumit Saha, Bojan Gavrilovic, Babak Taati, Hisham Alshaer, Azadeh Yadollahi
European Respiratory Journal Sep 2019, 54 (suppl 63) OA3278; DOI: 10.1183/13993003.congress-2019.OA3278
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  • VALIDITY OF A DIAGNOSTIC METHOD BY “NO CONTACT” TECHNOLOGY IN DETECTION OF SLEEP APNEA THROUGH A THERMOGRAPHIC CAMERA AND ARTIFICIAL INTELLIGENCE
  • Validation study of a new analysis software to screen sleep respiratory disorders
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