Artifact Detection in Invasive Blood Pressure Data using Forecasting Methods and Machine Learning

Vital signs, such as blood pressure and heart rate, are continuously and closely monitored during surgery and in the intensive care unit to ensure patient health. There has been increasing interest in studying the large data sets of electronic vital sign records to improve patient outcomes, predict issues, and detect complications early. However, the records of vital signs, particularly one called invasive arterial blood pressure, may be populated with artifacts (noise) due to various situations. In order to use this large volume of data in research, it is essential to accurately remove the artifacts to ensure data quality and avoid drawing conclusions from non-physiologic data. Manual labelling of artifacts is not a viable solution because of the significant time required to go through large volumes of data. We studied several solutions for artifact removal, including forecasting methods and machine learning strategies such as standard and anomaly detection algorithms. We also performed experiments using the information of one or multiple feature variables. We observed that XGBoost algorithm achieved the best performance amongst all algorithms tested. Forecasting methods exhibited a poor performance when compared to other machine learning algorithms. Anomaly detection methods showed a good overall performance. However, these special-purpose methods were not able to achieve a performance comparable to the XGBoost learner.