Minute ventilation measurement using Plethysmographic Imaging and lighting parameters

TL;DR

This paper presents a deep learning approach to remotely measure minute ventilation using plethysmographic imaging and lighting parameters, aiming to enhance real-time health monitoring of breathing disorders.

Contribution

It introduces a lightweight deep neural network model for remote ventilation measurement from wearable device data, with a publicly available dataset upon publication.

Findings

Effective estimation of minute ventilation using deep learning.

Lightweight models suitable for real-time health monitoring.

Potential for remote detection of breathing disorders.

Abstract

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.