  In recent years, significant attention has been devoted towards integrating
deep learning technologies in the healthcare domain. However, to safely and
practically deploy deep learning models for home health monitoring, two
significant challenges must be addressed: the models should be (1) robust
against noise; and (2) compact and energy-efficient. We propose REST, a new
method that simultaneously tackles both issues via 1) adversarial training and
controlling the Lipschitz constant of the neural network through spectral
regularization while 2) enabling neural network compression through sparsity
regularization. We demonstrate that REST produces highly-robust and efficient
models that substantially outperform the original full-sized models in the
presence of noise. For the sleep staging task over single-channel
electroencephalogram (EEG), the REST model achieves a macro-F1 score of 0.67
vs. 0.39 achieved by a state-of-the-art model in the presence of Gaussian noise
while obtaining 19x parameter reduction and 15x MFLOPS reduction on two large,
real-world EEG datasets. By deploying these models to an Android application on
a smartphone, we quantitatively observe that REST allows models to achieve up
to 17x energy reduction and 9x faster inference. We open-source the code
repository with this paper: https://github.com/duggalrahul/REST.
