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Sleep apnea is a serious and severely under-diagnosed sleep-relatedrespiration disorder characterized by repeated disrupted breathing eventsduring sleep. It is diagnosed via polysomnography which is an expensive testconducted in a sleep lab requiring sleep experts to manually score the recordeddata. Since the symptoms of sleep apnea are often ambiguous, it is difficultfor a physician to decide whether to prescribe polysomnography. In this study,we investigate whether helpful information can be obtained by collecting andautomatically analysing sleep data using a smartphone and an inexpensive straingauge respiration belt. We evaluate how accurately we can detect sleep apneawith wide variety of machine learning techniques with data from a clinicalstudy 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. Theseresults can be achieved even if we train only on high-quality data from anentirely separate, clinically certified sensor, which has the potential tosubstantially reduce the cost of data collection. Data from a complete nightcan be analyzed in about one second on a smartphone.

A Clinical Evaluation of a Low-Cost Strain Gauge Respiration Belt and Machine Learning to Detect Sleep Apnea