AI improves sleep apnea classification by generating synthesized data
April 01, 2019 / KYLE WIGGERS
Sleep apnea, a disorder that occurs when a person’s breathing is interrupted during sleep, affects an estimated 22 million Americans. The trouble is, the bulk of cases 80% go undiagnosed, and if left untreated, sleep apnea can increase the risk of coronary artery disease, heart attack, heart failure, and stroke. One field of study automatic snore sound classification, or ASSC aims to develop a method for sleep apnea diagnosis based on snore sound (sleep apnea is characterized by repetitive episodes of decreased or completely halted airflow). But despite progress that’s been made in recent years, there remains a lack of labeled data on which ASSC systems can be trained.