Monitoring Sleep-related Breathing Irregularities with Computer Vision Techniques

Sleep monitoring plays an essential role in the diagnosis of sleep disturbances. While the polysomnogram represents the gold standard, it has the disadvantage of assessing the patient’s sleep in an unfamiliar environment, usually a sleep clinic. This proposal targets the development of new computer vision-based methods for home-based sleep monitoring in partnership with Vigil Health Solutions, a company providing healthcare services and products internationally. The implementation of these methods will result in a software package that will be compatible with inexpensive, unobtrusive bed sensors composed of a flat array of pressure-sensitive devices. The project will target the automatic detection and measurement of sleep apnea, of bidirectional transitions from profound to shallow breathing, of whole body motion occurring during sleep and other sleep quality indicators.

Faculty Supervisor:

Dr. Alexandra Albu

Student:

Kaveh Malakuti

Partner:

Vigil Health Solutions Inc.

Discipline:

Engineering

Sector:

Information and communications technologies

University:

University of Victoria