  Physiologically, the thoracic (THO) and abdominal (ABD) movement signals,
captured using wearable piezo-electric bands, provide information about various
types of apnea, including central sleep apnea (CSA) and obstructive sleep apnea
(OSA). However, the use of piezo-electric wearables in detecting sleep apnea
events has been seldom explored in the literature. This study explored the
possibility of identifying sleep apnea events, including OSA and CSA, by solely
analyzing {one or both the THO and ABD signals. An adaptive non-harmonic model
was introduced to model the THO and ABD signals, which allows us to design
features for sleep apnea events. To confirm the suitability of the extracted
features, a support vector machine was applied to classify three categories --
normal and hypopnea, OSA, and CSA. According to a database of} 34 subjects, the
overall classification accuracies were on average $75.9\%\pm 11.7\%$ and
$73.8\%\pm 4.4\%$, respectively, based on the cross validation. When the
features determined from the THO and ABD signals were combined, the overall
classification accuracy became $81.8\%\pm 9.4\%$. These features were applied
for designing a state machine for online apnea event detection. Two
event-by-event accuracy indices, S and I, were proposed for evaluating the
performance {of the state machine. For the same database, the} S index was
$84.01\%\pm 9.06\%$, and the I index was $77.21\%\pm 19.01\%$. The results
indicate the considerable potential of applying the proposed algorithm to
clinical examinations for both screening and homecare purposes.
