A new algorithm for the detection of sleep apnea events in respiration signals

Sleep apneas are the most common type of sleep-related breathing disorders which cause a patient to move from a good sleep into an inefficient sleep. In addition, sleep apnea widely impacts the American population and is a large cost for healthcare. Traditional detection methods of sleep apneas are complex, expensive, and invasive to most patients. Among the various physiological signals, respiration signals are relatively easy to be monitored. However, not many studies are conducted using respiration signal only, and most of the previous algorithms are insufficient to detect apnea events. In this paper, we propose a new algorithm based on only the respiration signal to detect the apnea events during sleep and conduct experiments comparing the performance of our algorithm against two apnea detection algorithms. We use 20 patients' data, all of whom have severe Apnea Hypopnea Index (AHI>30: over 30 events per hour). Our study shows that our algorithm outperforms the other two algorithms.