A Machine Learning Algorithm for Detecting Abnormal Patterns in Continuous Capnography and Pulse Oximetry Monitoring
Feline L. Spijkerboer,
Frank J. Overdyk,
Albert Dahan
Abstract:Purpose: Continuous capnography monitors patient ventilation but can be susceptible to artifact, resulting in alarm fatigue. Development of smart algorithms may facilitate accurate detection of abnormal ventilation, allowing intervention before patient deterioration. The objective of this analysis was to use machine learning (ML) to classify combined waveforms of continuous capnography and pulse oximetry as normal or abnormal.
Methods: This analysis used data collected during the observational, prospective PRO… Show more
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