  Sleep apnea syndrome requires early diagnosis because this syndrome can lead
to a variety of health problems. If sleep apnea events can be detected in a
noncontact manner using radar, we can then avoid the discomfort caused by the
contact-type sensors that are used in conventional polysomnography. This study
proposes a novel radar-based method for accurate detection of sleep apnea
events. The proposed method uses the expectation-maximization algorithm to
extract the respiratory features that form normal and abnormal breathing
patterns, resulting in an adaptive apnea detection capability without any
requirement for empirical parameters. We conducted an experimental quantitative
evaluation of the proposed method by performing polysomnography and radar
measurements simultaneously in five patients with the symptoms of sleep apnea
syndrome. Through these experiments, we show that the proposed method can
detect the number of apnea and hypopnea events per hour with an error of 4.8
times/hour; this represents an improvement in the accuracy by 1.8 times when
compared with the conventional threshold-based method and demonstrates the
effectiveness of our proposed method.
