2021/01/07 by Stein Kristiansen, Konstantinos Nikolaidis, Kristiansen, Stein +28
Computer Science · Medicine · Psychology · #FOS: Computer and information sciences #I.2.1 #J.3 #Machine Learning (cs.LG) #Obstructive Sleep Apnea Research #Sleep and Work-Related Fatigue #cs.LG
paper · pdf · doi:10.48550/arxiv.2101.02595
arxiv created 2021/01/07 · openalex publication_date 2021/01/07 · arxiv updated 2021/01/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Sleep apnea is a serious and severely under-diagnosed sleep-related respiration disorder characterized by repeated disrupted breathing events during sleep. It is diagnosed via polysomnography which is an expensive test conducted in a sleep lab requiring sleep experts to manually score the recorded data. Since the symptoms of sleep apnea are often ambiguous, it is difficult for a physician to decide whether to prescribe polysomnography. In this study, we investigate whether helpful information can be obtained by collecting and automatically analysing sleep data using a smartphone and an inexpensive strain gauge respiration belt. We evaluate how accurately we can detect sleep apnea with wide variety of machine learning techniques with data from a clinical study with 49 overnight sleep recordings. With less than one hour of training, we can distinguish between normal and apneic minutes with an accuracy, sensitivity, and specificity of 0.7609, 0.7833, and 0.7217, respectively. These results can be achieved even if we train only on high-quality data from an entirely separate, clinically certified sensor, which has the potential to substantially reduce the cost of data collection. Data from a complete night can be analyzed in about one second on a smartphone.