  The abnormal pause or rate reduction in breathing is known as the sleep-apnea
hypopnea syndrome and affects the quality of sleep of an individual. A novel
method for the detection of sleep apnea events (pause in breathing) from
peripheral oxygen saturation (SpO2) signals obtained from wearable devices is
discussed in this paper. The paper details an apnea detection algorithm of a
very high resolution on a per-second basis for which a 1-dimensional
convolutional neural network -- which we termed SomnNET -- is developed. This
network exhibits an accuracy of 97.08% and outperforms several lower resolution
state-of-the-art apnea detection methods. The feasibility of model pruning and
binarization to reduce the computational complexity is explored. The pruned
network with 80% sparsity exhibited an accuracy of 89.75%, and the binarized
network exhibited an accuracy of 68.22%. The performance of the proposed
networks is compared against several state-of-the-art algorithms.
