IEEE transactions on bio-medical engineering
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IEEE Trans Biomed Eng · Dec 2014
Automated quantitative analysis of capnogram shape for COPD-normal and COPD-CHF classification.
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 CO 2 concentration. The capnogram shape also has diagnostic value, but is presently assessed qualitatively, by visual inspection. ⋯ 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 real-time monitoring.
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A control scheme was designed in order to reduce the risks of hyperglycemia and hypoglycemia in type 1 diabetes mellitus (T1DM). This structure is composed of three main components: an H∞ robust controller, an insulin feedback loop (IFL), and a safety mechanism (SM). ⋯ The SM prevents dangerous scenarios by acting upon a prediction of future glucose levels, and the IFL modifies the loop gain in order to reduce postprandial hypoglycemia risks. The procedure is tested on the complete alic>in silico adult cohort of the UVA/Padova metabolic simulator, which has been accepted by the Food and Drug Administration (FDA) in lieu of animal trials.
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IEEE Trans Biomed Eng · Dec 2014
Remote detection of photoplethysmographic systolic and diastolic peaks using a digital camera.
We present a new method for measuring photoplethysmogram signals remotely using ambient light and a digital camera that allows for accurate recovery of the waveform morphology (from a distance of 3 m). In particular, we show that the peak-to-peak time between the systolic peak and diastolic peak/inflection can be automatically recovered using the second-order derivative of the remotely measured waveform. We compare measurements from the face with those captured using a contact fingertip sensor and show high agreement in peak and interval timings. ⋯ The mean systolic-diastolic peak-to-peak times measured using the contact sensor and the camera were highly correlated, ρ = 0.94 (p 0.001). The results were obtained with a camera frame-rate of only 30 Hz. This technology has significant potential for advancing healthcare.