Artificial arterial blood pressure artifact models and an evaluation of a robust blood pressure and heart rate estimator

Qiao Li,Gari D Clifford,Roger G Mark

doi:10.1186/1475-925x-8-13

Abstract

BackgroundWithin 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.MethodsUsing 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.ResultsOur 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.ConclusionThe 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.

Highlights

Introduction to Random Signal Analysis andKalman Filtering New York: Wiley; 1983.21
SH: Sample-and-hold arterial blood pressure (ABP) estimate using ABP feature extraction routine of wSQI and clipping the reported ABP value when ψ
In order to evaluate the accuracy of an estimation method in the presence of noise, we chose the root mean squared error of the difference between each ABP estimation method and the true ABP

Summary

Introduction

Introduction to Random Signal Analysis andKalman Filtering New York: Wiley; 1983.21. Welch G, Bishop G: An introduction to the Kalman filter. 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. ABP waveforms are frequently corrupted by artifacts, such as transducer flushing, catheter clotting, movement artifacts, and non-invasive cuff inflations [1] These errors cause monitors to generate a high rate of false alarms. ICU false alarm rates can be as high as 86% [2,3] Various strategies, such as median filtering [4], multi-parametric analysis [5,6,7], machine learning [8,9,10,11] and signal quality assessment techniques [12], are used to reduce false alarms. A system that can integrate an estimate of the quality of each individual observation into each ABP estimate can improve the overall ABP estimate further [12]

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