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SleepdB

SleepdB is a sound-proof laboratory that was developed to examine sleep-disordered breathing.

Chronic cardio-respiratory disorders share common risk factors such as older age, lack of activity, smoking, and obesity. The symptoms of these diseases, such as shortness of breath and coughing, often worsen during sleep. One potential reason for this is that sleep is associated with physiological changes in respiration, such as humoral factors and a reduction in respiratory muscle tone and lung volume. Another potential reason is that lying down during sleep causes body fluids to shift from the legs to the heart, lungs and neck. This fluid accumulation in the lungs and neck then causes narrowing of the airway; however, little is known about the link between sleep, fluid shifts and exacerbated respiratory disease.

SleepdB is a sound-proof laboratory developed to examine sleep-disordered breathing that leverages novel and non-invasive acoustic monitoring technologies.

SleepdB is one of the few laboratories in the world dedicated to understanding the intricate interplay between sleep, body fluid shifts and respiratory disease, including but not limited to obstructive sleep apnea, asthma, and COPD. We utilize physiological experiments with human participants, as well as an artificial intelligence based approach to investigate the pathophysiological mechanisms in the interaction between diseases. The research group also develops novel technologies for the long-term monitoring of physiological signals that occur during sleep and when awake, with the ultimate goal to improve disease management and prevent exacerbation and hospitalization.

Equipment Highlights

The SleepdB lab includes the following major groups of equipment:

  • an acoustical chamber large enough to contain a bed and monitoring equipment;
  • sleep assessment techniques, including polysomnography;
  • respiratory function assessment equipment such as a specially-configured forced oscillation technique device to measure airway resistance;
  • ultrasound imaging with elastography to visualize cardiopulmonary function and tissue stiffness; and
  • a fluid measurement and data acquisition module to integrate and synchronize all of the measurements highlighted above. This combination of infrastructure is entirely unique to SleepdB and will complement existing clinical laboratories to assess sleep apnea and cardiovascular diseases.

Quick Facts

COPD affects approximately 3 million Canadians, including 1.5 million Canadians who say they currently suffer from this disease and another 1.5 million undiagnosed Canadians. This makes COPD Canada’s fourth leading cause of death.

Sleep apnea is a potentially serious sleep disorder in which breathing repeatedly stops and starts. If you snore loudly and feel tired even after a full night's sleep, you might have sleep apnea.

Recent Publications

  • X. Cao, T.D. Bradley, S. A. Bhatawadekar, S. Saha, S. M. Tarlo, M. B. Stanbrook, M. D. Inman, K. Rana, R. J. Dandurand, A. Yadollahi, “Effect of simulated obstructive apnea on thoracic fluid volume and airway resistance in asthma”. Am J Respir Crit Care Med, 203(7), 908, 2021
  • N. Montazeri, S. Saha, K. Zhu, B. Gavrilovic, A. Yadollahi, “Sleep Apnea Severity based on Estimated Tidal Volume and Extracted Snoring from Tracheal Signals”, Sleep Research, Accepted, 2021.
  • N. Montazeri, M. Kabir, B. Gavrilovic, S. Saha, K. Zhu, B. Taati, H. Alshaer, A. Yadollahi, “Respiratory Signal Estimation using Tracheal Sound and Movements”, Sleep Research, e13279, 2021.
  • N. Montazeri, S. Akbarian, M. Hafezi, B. Gavrilovic, S. Saha, K. Zhu, B. Taati, A. Yadollahi, “Sleep/wakefulness Detection using Tracheal Sounds and Movements”. Nature and Science of Sleep, 12:1009-1021, 2020.
  • N. Montazeri, M. Kabir, B. Gavrilovic, S. Saha, K. Zhu, B. Taati, A. Yadollahi, “Breath-phase Identification using respiratory tracheal sound and movement during sleep”, Annals of Biomedical Engineering, pp. 1-13, 2020.
  • S. Akbarian, N. Montazeri, A. Yadollahi, B. Taati, “Distinguishing Obstructive vs. Central Sleep Apnea Events in Infrared Video”, Journal of Medical Internet Research, 2020.
  • M. Hafezi, N. Montazeri, S. Saha, K. Zhu, B. Gavrilovic, A. Yadollahi, and B. Taati. “Sleep Apnea Severity Estimation using a Deep Learning Model from Tracheal Movements”, IEEE Access, 8, pp. 22641-22649, 2020.
  • S. Saha, M. Kabir, N. Montazeri, B. Gavrilovic, K. Zhu, H. Alshaer, A. Yadollahi, “Portable diagnosis of sleep apnea with the validation of individual event detection”, Sleep Medicine, 69C, pp. 51-57, 2020.

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Knowledge . Innovation . Talent . Everywhere

Knowledge

Innovation

Talent

Everywhere

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