  Obstructive Sleep Apnea (OSA) is a highly prevalent but inconspicuous disease
that seriously jeopardizes the health of human beings. Polysomnography (PSG),
the gold standard of detecting OSA, requires multiple specialized sensors for
signal collection, hence patients have to physically visit hospitals and bear
the costly treatment for a single detection. Recently, many single-sensor
alternatives have been proposed to improve the cost efficiency and convenience.
Among these methods, solutions based on RR-interval (i.e., the interval between
two consecutive pulses) signals reach a satisfactory balance among comfort,
portability and detection accuracy. In this paper, we advance RR-interval based
OSA detection by considering its real-world practicality from energy
perspectives. As photoplethysmogram (PPG) pulse sensors are commonly equipped
on smart wrist-worn wearable devices (e.g., smart watches and wristbands), the
energy efficiency of the detection model is crucial to fully support an
overnight observation on patients. This creates challenges as the PPG sensors
are unable to keep collecting continuous signals due to the limited battery
capacity on smart wrist-worn devices. Therefore, we propose a novel Frequency
Extraction Network (FENet), which can extract features from different frequency
bands of the input RR-interval signals and generate continuous detection
results with downsampled, discontinuous RR-interval signals. With the help of
the one-to-multiple structure, FENet requires only one-third of the operation
time of the PPG sensor, thus sharply cutting down the energy consumption and
enabling overnight diagnosis. Experimental results on real OSA datasets reveal
the state-of-the-art performance of FENet.
