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Mieloszyk, R.;  Verghese, G.; Deitch, K.; Cooney, B.; Khalid, A.; Mirre-Gonzalez, M.; Heldt, T.; Krauss, B.

DOI: 10.1109/TBME.2014.2332954

We develop an approach to quantitative analysis of carbon dioxide concentration in exhaled breath, recorded as a function of time by capnography. The generated waveform – or capnogram – is currently used in clinical practice to establish the presence of respiration as well as determine respiratory rate and end-tidal CO2 concentration. The capnogram shape also has diagnostic value, but is presently assessed qualitatively, by visual inspection. Prior approaches to quantitatively characterizing the capnogram shape have explored the correlation of various geometric parameters with pulmonary function tests. These studies attempted to characterize the capnogram in normal subjects and patients with cardiopulmonary disease, but no consistent progress was made, and no translation into clinical practice was achieved. We apply automated quantitative analysis to discriminate between chronic obstructive pulmonary disease (COPD) and congestive heart failure (CHF), and between COPD and normal. Capnograms were collected from 30 normal subjects, 56 COPD patients, and 53 CHF patients. We computationally extract four physiologically based capnogram features. Classification on a hold-out test set was performed by an ensemble of classifiers employing quadratic discriminant analysis, designed through cross-validation on a labeled training set. Using 80 exhalations of each capnogram record in the test set, performance analysis with bootstrapping yields areas under the receiver operating characteristic (ROC) curve of 0.89 (95% CI: 0.72–0.96) for COPD/CHF classification, and 0.98 (95% CI: 0.82- 1.0) for COPD/normal classification. This classification performance is obtained with a run-time sufficiently fast for realtime monitoring.

Related Links:

Automated Quantitative Analysis of Capnogram Shape for COPD–Normal and COPDCHF Classification (IEEE)

Diagnostic exhalations (MIT News)

Professor George Verghese

Professor Thomas Heldt

Computational Physiology and Clinical Inference Group