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.
翻译:由于肺部缺乏控制/交换氧气和二氧化碳的能力,无法确保身体处于稳定的软软状态,呼吸系统紊乱,如睡眠动脉瘤等是一种严重的紊乱,影响到许多人。呼吸系统的测量,如小通风,可与其他生理测量相关,如心脏率和心率变化,用于远程监测健康状况和检测呼吸相关紊乱症状。在这项工作中,我们制定了基于深层次学习的办法来测量私人数据集的远程通风。一旦接受这项工作,数据集将公布于众。我们使用两个版本的深神经网络来估计通过耗损性心率和呼吸装置获得的数据流的微小通风。我们证明,我们管道的简单设计,包括轻量的深神经网络,可以很容易地纳入实时的健康监测系统。