Using the features of the time and volumetric capnogram for classification and prediction

Journal of Clinical Monitoring and Computing
Michael B Jaffe

Abstract

Quantitative features derived from the time-based and volumetric capnogram such as respiratory rate, end-tidal PCO2, dead space, carbon dioxide production, and qualitative features such as the shape of capnogram are clinical metrics recognized as important for assessing respiratory function. Researchers are increasingly exploring these and other known physiologically relevant quantitative features, as well as new features derived from the time and volumetric capnogram or transformations of these waveforms, for: (a) real-time waveform classification/anomaly detection, (b) classification of a candidate capnogram into one of several disease classes, (c) estimation of the value of an inaccessible or invasively determined physiologic parameter, (d) prediction of the presence or absence of disease condition, (e) guiding the administration of therapy, and (f) prediction of the likely future morbidity or mortality of a patient with a presenting condition. The work to date with respect to these applications will be reviewed, the underlying algorithms and performance highlighted, and opportunities for the future noted.

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Citations

Dec 5, 2017·Anesthesia and Analgesia·Michael B Jaffe
Feb 27, 2018·Journal of Clinical Monitoring and Computing·D S KarbingM B Jaffe
Mar 4, 2017·Journal of Clinical Monitoring and Computing·D S KarbingM B Jaffe
Jul 28, 2020·Technology and Health Care : Official Journal of the European Society for Engineering and Medicine·Ismail M El-BadawyZaid Omar
May 23, 2021·Respiratory Research·Barak PertzovMordechai R Kramer
Aug 9, 2021·Pediatric Pulmonology·Emma WilliamsAnne Greenough

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