title: Artifact detection in cardiovascular time series monitoring data from preterm infants.
abstract: Artifacts in clinical intensive care monitoring lead to false alarms and complicate data analysis. They must be identified and processed to obtain true information. In this paper, we present a method for detecting artifacts in heart-rate (HR) and mean blood-pressure (BP) data from a physiological monitoring system used in preterm infants. The method uses three different types of artifact detectors: limit-based detectors, deviation-based detectors, and correlation-based detectors. Each identifies artifacts in the monitoring data from a different perspective. By integrating the individual detectors, we develop a parametric artifact detector, called CVDetector. The CVDetector is parametric because its performance depends on the specific values for the parameters in its component detectors. In a huge space of CVDetector instances, we have successfully discovered an optimal CVDetector instance, denoted by CVDetector. The sensitivity and specificity of CVDetector for HR artifacts is 94.8% (SD = 7.6%) and 90.6% (SD = 6.9%), respectively. The sensitivity and specificity of CVDetector for BP artifacts is 94.2% (SD = 5.3%) and 80.0% (SD = 12.4%), respectively.
authors.affiliation: Informatics Program, Children's Hospital, Boston, MA 02115, USA.
authors.forename: C
authors.initials: C
authors.lastname: Cao
authors.forename: I S
authors.initials: IS
authors.lastname: Kohane
authors.forename: N
authors.initials: N
authors.lastname: McIntosh
mesh_terms: D016477:Artifacts; D001185:Artificial Intelligence; D001794:Blood Pressure; D006339:Heart Rate; D006801:Humans; D007231:Infant, Newborn; D007234:Infant, Premature / Q000502:physiology*; D007363:Intensive Care Units, Neonatal; D008991:Monitoring, Physiologic; D012680:Sensitivity and Specificity; D013995:Time