Detecting Breaths in Capnography Time Series
نویسندگان
چکیده
Finding individual breaths is essential in the classification of respiratory sinus arrhythmia. The identification task may become quite difficult if the sampling rates of available physiological measurements are low, as in the HR3Modul decision support system. We introduce an improved respiration analysis of the HR3Modul system that uses an Euclidian distance based Nearest-Neighbour classification of low resolution capnography time-series in order to detect individual breaths. The classification uses an additional case library that is independent from the already existing one (in HR3Modul) in the identification process.
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