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We have developed a novel method for noncontact measurement of breathing function. The method is based on statistical modeling of dynamic thermal data captured through an infrared imaging system. The expired air has higher temperature than the typical background of indoor environments (e.g., walls). Therefore, the particles of the expired air emit at a higher power than the background, a phenomenon which is captured as a distinct thermal signature in the infrared imagery. There is significant technical difficulty in computing this signature, however, because the phenomenon is of very low intensity and transient nature. We use an advanced statistical algorithm based on the method of moments and the Jeffrey's divergence measure to address the problem. So far, we were able to compute correctly the breathing waveforms for ten (10) subjects at distances ranging from 6-8 feet. The results were checked against concomitant ground-truth data collected with a traditional contact sensor. The technology is expected to find applications in the next generation of touchless polygraphy and in preventive health care.

The strong correlation between the CVs for tidal breathing, FEV1, and FVC, and the statistically significant ability of CV for tidal breathing to distinguish between healthy subjects and CF patients, and between the studied CF disease states suggests that the CV may be useful for measuring the extent and severity of structural lung disease.

The results indicate the potential use of EIT-derived ventilation-perfusion index maps as a non-invasive method for identifying regions of air trapping.

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