New paper accepted @ BIBM 2020
Artifact Detection in Invasive Blood Pressure Data using Forecasting Methods and Machine Learning
New paper published on BIBM 2020: “Artifact Detection in Invasive Blood Pressure Data using Forecasting Methods and Machine Learning”
Mengqi Wu, Paula Branco, Janny Xue Chen Ke, MD and David B. MacDonald
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. In this paper, we study several solutions for artifact removal including forecasting methods and machine learning algorithms including standard and anomaly detection algorithms. We also performed experiments using the information of one or multiple feature variables. We observe that XGBoost algorithm achieves the best performance across all algorithms tested. Forecasting methods exhibit a poor performance when compared to other machine learning algorithms and anomaly detection methods show a good overall performance. However, these special purpose methods are not able to achieve a performance comparable to the XGBoost learner.