Many emerging technologies such as facial recognition, augmented reality and self-driving cars rely on computer vision. These technologies use computers that can capture, process and interpret images — just like the human eye and brain.
At Toronto Rehab-UHN's KITE Research Institute, Scientist Dr. Babak Taati (In Photo: UHN) and his research team have harnessed computer vision in a pioneering method for differentiating between different types of sleep apneas.
Sleep apnea is a chronic disorder in which breathing intermittently pauses during sleep. This interrupted breathing can dramatically increase the risk of heart disease, stroke and other complications.
Two types are used to categorize sleep apneas: obstructive, in which the throat temporarily collapses, blocking the airway; and central, in which the brain fails to send signals to the muscles that control breathing.
"Distinguishing sleep apneas as either obstructive or central is challenging but crucial to selecting an appropriate treatment," says Dr. Taati. "This is because the treatments vary considerably depending on the type of sleep apnea—for example, continuous positive airway pressure therapy greatly benefits patients with obstructive sleep apneas, but is harmful for those with central sleep apneas."
The new method developed by Dr. Taati's team uses computer vision to monitor a sleeping patient and discern the type of apnea. Unlike other approaches, it does not disrupt the patient's sleeping conditions.
Read more at UHN.ca
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