Minute ventilation measurement using Plethysmographic Imaging and lighting parameters
This work addresses remote health monitoring for individuals with breathing disorders, but it appears incremental as it applies existing deep learning methods to a new dataset.
The paper tackled the problem of measuring minute ventilation for remote health monitoring of breathing disorders like sleep apnea, using a deep learning approach on a private dataset and achieving results that can be integrated into real-time systems.
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.