Reduction of false arterial blood pressure alarms using signal quality assessement and relationships between the electrocardiogram and arterial blood pressure

Abstract

The paper presents an algorithm for reducing false alarms related to changes in arterial blood pressure (ABP) in intensive care unit (ICU) monitoring. The algorithm assesses the ABP signal quality, analyses the relationship between the electrocardiogram and ABP using a fuzzy logic approach and post-processes (accepts or rejects) ABP alarms produced by a commercial monitor. The algorithm was developed and evaluated using unrelated sets of data from the MIMIC database. By rejecting 98.2% (159 of 162) of the false ABP alarms produced by the monitor using the test set of data, the algorithm was able to reduce the false ABP alarm rate from 26.8% to 0.5% of ABP alarms, while accepting 99.8% (441 of 442) of true ABP alarms. The results show that the algorithm is effective and practical, and its use in future patient monitoring systems is feasible.

Robust heart rate estimation from multiple asynchronous noisy sources using signal quality indices and a Kalman filter

“…Signal quality assessment of the ABP was based on a combination of two previously described algorithms: a beat-by-beat fuzzy logic-based assessment of features in the ABP waveform (Zong et al 2004) and heuristic thresholding of each ABP pulse to determine normality (Sun et al 2006). (These latter algorithms are known as wsqi and jsqi respectively.)…”

Estimation of Heart Rate Using Signal Fusion of Ecg and Bp Signals.

“…Methods for recoding a time series of respiratory effort include impedance pneumography, impedance plethysmograpy and flow thermography which measures the changes in temperature of air flow as it moves in and out of the mouth and/or nose over a thermistor. Literature on deriving a respiratory signal from other related signals is dense in the case of the electrocardiogram, but relatively less for other signals such as the photo plethysmogram and blood pressure waveform [2].The field of respiration rate estimation from respiratory signals (whether derived or not) is scantily covered in the public literature, particularly with respect to large data sets. Key works by O'Brien and Heneghan, de Chazal et al, Ishijima, Park et al, and Tarassenko [15] et al highlights the importance of modeling noise and combining information from multiple ECG leads and sensor modalities to compensate for noisy measurements.…”

Estimation of blood pressure parameters using ex-Gaussian model

“…First, the signal quality should be estimated in order to skip the fragments of the data which are too noisy. This can be done by using the methods proposed in [1], [2]. Next, the characteristic points of a full beat of blood pressure signal should be estimated.…”