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Breathing disorders such as sleep apnea is a critical disorder that affects alarge number of individuals due to the insufficient capacity of the lungs tocontain/exchange oxygen and carbon dioxide to ensure that the body is in thestable state of homeostasis. Respiratory Measurements such as minuteventilation can be used in correlation with other physiological measurementssuch as heart rate and heart rate variability for remote monitoring of healthand detecting symptoms of such breathing related disorders. In this work, weformulate a deep learning based approach to measure remote ventilation on aprivate 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 ventilationfrom data streams obtained through wearable heart rate and respiratory devices.We demonstrate that the simple design of our pipeline - which includeslightweight deep neural networks - can be easily incorporate into real timehealth monitoring systems.

Minute ventilation measurement using Plethysmographic Imaging and lighting parameters