论文信息 - A Big-Data Approach to Defining Breathing Signatures for Identifying Respiratory Disease

A Big-Data Approach to Defining Breathing Signatures for Identifying Respiratory Disease

This project seeks to use wearable sensors to develop a novel method for measuring respiratory activity in human subjects. This is the first stage of an ongoing project under the Arizona Center for Accelerated Biomedical Innovation (ACABI) [1]. The ultimate ambition of this effort is to develop a baseline digital breathing signature for a particular individual, so that medical professionals equipped with big-data analysis tools can use deviations from one’s signature to differentiate between conventional breathing and abnormal breathing patterns, such as splinting and Kussmaul respirations.

[1] Xiaoying Wang,et al. Respiratory motion correction for free-breathing 3D abdominal MRI using CNN-based image registration: a feasibility study. , 2018, The British journal of radiology.

[2] John A Rogers,et al. Conformal piezoelectric energy harvesting and storage from motions of the heart, lung, and diaphragm , 2014, Proceedings of the National Academy of Sciences.