In intensive care, decision-making is often based on trend analysis of physiological parameters. Artifact detection is a pre-requisite for interpretation of trends both for clinical and research purposes. In this study, we developed and tested three methods of artifact detection in physiological data (systolic, mean and diastolic artery and pulmonary artery pressures, central venous pressure, and peripheral temperature) using pre-filtered physiological signals (2-min median filtering) from 41 patients after cardiac surgery. These methods were: (1) the Rosner statistic; (2) slope detection with rules; and (3) comparison with a running median (median detection). After tuning the methods using data from 20 randomly chosen patients, the methods were tested using the data from the remaining patients. The results were compared with those obtained by manual identification of artifacts by three senior intensive care unit physicians. Out of an average of 22 480 data points for each variable, the three observers labelled 0.98% (220 data points) as artifacts. The inter-observer agreement was good. The average (range) sensitivity for artifact detection in all variables in the test database was 66% (33-92%) for the Rosner statistic, 64% (24-98%) for slope detection and 72% (41-98%) for median detection. All methods had a high specificity (≥94%). Slope detection had the highest mean positive prediction rate (53%; 21-85%). When the performance was measured by the cost function, slope detection and running median performed equally well and were superior to Rosner statistics for systemic arterial and central venous pressure and peripheral temperature. None of the methods produced acceptable results for pulmonary artery pressures. We conclude that median filtering of physiological variables is effective in removing artifacts. In post-operative cardiac surgery patients, the remaining artifacts are difficult to detect among physiological and pathophysiological changes. This makes large databases for tuning artifact algorithms mandatory. Despite these limitations, the performance of running median and slope detection were good in selected physiological variables. (C) 2000 Elsevier Science Ireland Ltd.
- Intensive care
- Monitored trends