Artificial arterial blood pressure artifact models and an evaluation of a robust blood pressure and heart rate estimator
Name
Li-2009-Artificial arterial.pdf
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1.96 MB
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Author(s)
Li, Qiao
Clifford, Gari D.
Mark, Roger Greenwood
Date Issued
July 2009
Journal
BioMedical Engineering OnLine
Publisher
BioMed Central
Citation
Li, Qiao, Roger Mark, and Gari Clifford. “Artificial arterial blood pressure artifact models and an evaluation of a robust blood pressure and heart rate estimator.” BioMedical Engineering OnLine 8.1 (2009): 13.
Version
Final published version
Abstract
Background: Within the intensive care unit (ICU), arterial blood pressure (ABP) is typically recorded at different (and sometimes uneven) sampling frequencies, and from different sensors, and is often corrupted by different artifacts and noise which are often non-Gaussian, nonlinear and nonstationary. Extracting robust parameters from such signals, and providing confidences in the estimates is therefore difficult and requires an adaptive filtering approach which accounts for artifact types.
Methods: Using a large ICU database, and over 6000 hours of simultaneously acquired electrocardiogram (ECG) and ABP waveforms sampled at 125 Hz from a 437 patient subset, we documented six general types of ABP artifact. We describe a new ABP signal quality index (SQI), based upon the combination of two previously reported signal quality measures weighted together. One index measures morphological normality, and the other degradation due to noise. After extracting a 6084-hour subset of clean data using our SQI, we evaluated a new robust tracking algorithm for estimating blood pressure and heart rate (HR) based upon a Kalman Filter (KF) with an update sequence modified by the KF innovation sequence and the value of the SQI. In order to do this, we have created six novel models of different categories of artifacts that we have identified in our ABP waveform data. These artifact models were then injected into clean ABP waveforms in a controlled manner. Clinical blood pressure (systolic, mean and diastolic) estimates were then made from the ABP waveforms for both clean and corrupted data. The mean absolute error for systolic, mean and diastolic blood pressure was then calculated for different levels of artifact pollution to provide estimates of expected errors given a single value of the SQI.
Results: Our artifact models demonstrate that artifact types have differing effects on systolic, diastolic and mean ABP estimates. We show that, for most artifact types, diastolic ABP estimates are less noise-sensitive than mean ABP estimates, which in turn are more robust than systolic ABP estimates. We also show that our SQI can provide error bounds for both HR and ABP estimates.
Conclusion: The KF/SQI-fusion method described in this article was shown to provide an accurate estimate of blood pressure and HR derived from the ABP waveform even in the presence of high levels of persistent noise and artifact, and during extreme bradycardia and tachycardia. Differences in error between artifact types, measurement sensors and the quality of the source signal can be factored into physiological estimation using an unbiased adaptive filter, signal innovation and signal quality measures.
Subjects
United States National Institute of Biomedical Imaging and Bioengineering (Grant Number R01 EB001659)
Information & Communications University, Daejeon, South Korea (Grant Number 6914565)
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
http://dx.doi.org/10.1186/1475-925X-8-13
