Biomedical subjects

Andrew T Reisner

Publications and source records attributed to Andrew T Reisner.

3 recordsLinked to original sources

PubMed2006

A method for automatic identification of reliable heart rates calculated from ECG and PPG waveforms.

OBJECTIVE: The development and application of data-driven decision-support systems for medical triage, diagnostics, and prognostics pose special requirements on physiologic data. In particular, that data are reliable in order to produce meaningful results. The authors describe a method that automatically estimates the reliability of reference heart rates (HRr) derived from electrocardiogram (ECG) waveforms and photoplethysmogram (PPG) waveforms recorded by vital-signs monitors. The reliability is quantitatively expressed through a quality index (QI) for each HRr. DESIGN: The proposed method estimates the reliability of heart rates from vital-signs monitors by (1) assessing the quality of the ECG and PPG waveforms, (2) separately computing heart rates from these waveforms, and (3) concisely combining this information into a QI that considers the physical redundancy of the signal sources and independence of heart rate calculations. The assessment of the waveforms is performed by a Support Vector Machine classifier and the independent computation of heart rate from the waveforms is performed by an adaptive peak identification technique, termed ADAPIT, which is designed to filter out motion-induced noise. RESULTS: The authors evaluated the method against 158 randomly selected data samples of trauma patients collected during helicopter transport, each sample consisting of 7-second ECG and PPG waveform segments and their associated HRr. They compared the results of the algorithm against manual analysis performed by human experts and found that in 92% of the cases, the algorithm either matches or is more conservative than the human's QI qualification. In the remaining 8% of the cases, the algorithm infers a less conservative QI, though in most cases this was because of algorithm/human disagreement over ambiguous waveform quality. If these ambiguous waveforms were relabeled, the misclassification rate would drop from 8% to 3%. CONCLUSION: This method provides a robust approach for automatically assessing the reliability of large quantities of heart rate data and the waveforms from which they are derived.

Algorithms↗

PubMed2006

Continuous cardiac output monitoring by peripheral blood pressure waveform analysis.

A clinical method for monitoring cardiac output (CO) should be continuous, minimally invasive, and accurate. However, none of the conventional CO measurement methods possess all of these characteristics. On the other hand, peripheral arterial blood pressure (ABP) may be measured reliably and continuously with little or no invasiveness. We have developed a novel technique for continuously monitoring changes in CO by mathematical analysis of a peripheral ABP waveform. In contrast to the previous techniques, our technique analyzes the ABP waveform over time scales greater than a cardiac cycle in which the confounding effects of complex wave reflections are attenuated. The technique specifically analyzes 6-min intervals of ABP to estimate the pure exponential pressure decay that would eventually result if pulsatile activity abruptly ceased (i.e., after the high frequency wave reflections vanish). The technique then determines the time constant of this exponential decay, which equals the product of the total peripheral resistance and the nearly constant arterial compliance, and computes proportional CO via Ohm's law. To validate the technique, we performed six acute swine experiments in which peripheral ABP waveforms and aortic flow probe CO were simultaneously measured over a wide physiologic range. We report an overall CO error of 14.6%.

Algorithms↗

PubMed2005

Laguerre-model blind system identification: cardiovascular dynamics estimated from multiple peripheral circulatory signals.

This paper presents a method for comparing multiple circulatory waveforms measured at different locations to improve cardiovascular parameter estimation from these signals. The method identifies the distinct vascular dynamics that shape each waveform signal, and estimates the common cardiac flow input shared by them. This signal-processing algorithm uses the Laguerre function series expansion for modeling the hemodynamics of each arterial branch, and identifies unknown parameters in these models from peripheral waveforms using multichannel blind system identification. An effective technique for determining the Laguerre base pole is developed, so that the Laguerre expansion captures and quickly converges to the intrinsic arterial dynamics observed in the two circulatory signals. Furthermore, a novel deconvolution method is developed in order to stably invert the identified dynamic models for estimating the cardiac output (CO) waveform from peripheral pressure waveforms. The method is applied to experimental swine data. A mean error of less than 5% with the measured peripheral pressure waveforms has been achieved using the models and excellent agreement between the estimated CO waveforms and the gold standard measurements have been obtained.

Algorithms↗