  Breathing disorders such as sleep apnea is a critical disorder that affects a
large number of individuals due to the insufficient capacity of the lungs to
contain/exchange oxygen and carbon dioxide to ensure that the body is in the
stable state of homeostasis. Respiratory Measurements such as minute
ventilation can be used in correlation with other physiological measurements
such as heart rate and heart rate variability for remote monitoring of health
and detecting symptoms of such breathing related disorders. In this work, we
formulate a deep learning based approach to measure remote ventilation on a
private dataset. The dataset will be made public upon acceptance of this work.
We use two versions of a deep neural network to estimate the minute ventilation
from data streams obtained through wearable heart rate and respiratory devices.
We demonstrate that the simple design of our pipeline - which includes
lightweight deep neural networks - can be easily incorporate into real time
health monitoring systems.
