Artifact detection in cardiovascular time series monitoring data from preterm infants

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.