  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.
