Gokul Swamy

An adaptive transfer function for deriving the aortic pressure waveform from a peripheral artery pressure waveform

We developed a new technique to mathematically transform a peripheral artery pressure (PAP) waveform distorted by wave reflections into the physiologically more relevant aortic pressure (AP) waveform. First, a transfer function relating PAP to AP is defined in terms of the unknown parameters of a parallel tube model of pressure and flow in the arterial tree. The parameters are then estimated from the measured PAP waveform along with a one-time measurement of the wave propagation delay time between the aorta and peripheral artery measurement site (which may be accomplished noninvasively) by exploiting preknowledge of aortic flow. Finally, the transfer function with its estimated parameters is applied to the measured waveform so as to derive the AP waveform. Thus, in contrast to the conventional generalized transfer function, the transfer function is able to adapt to the intersubject and temporal variability of the arterial tree. To demonstrate the feasibility of this adaptive transfer function technique, we performed experiments in 6 healthy dogs in which PAP and reference AP waveforms were simultaneously recorded during 12 different hemodynamic interventions. The AP waveforms derived by the technique showed agreement with the measured AP waveforms (overall total waveform, systolic pressure, and pulse pressure root mean square errors of 3.7, 4.3, and 3.4 mmHg, respectively) statistically superior to the unprocessed PAP waveforms (corresponding errors of 8.6, 17.1, and 20.3 mmHg) and the AP waveforms derived by two previously proposed transfer functions developed with a subset of the same canine data (corresponding errors of, on average, 5.0, 6.3, and 6.7 mmHg).

Calculation of Forward and Backward Arterial Waves by Analysis of Two Pressure Waveforms

We developed a technique to calculate forward and backward arterial waves from proximal and distal pressure waveforms. First, the relationship between the waveforms is represented with an arterial tube model. Then, the model parameters are estimated via least-squares fitting. Finally, the forward and backward waves are calculated using the parameter estimates. Thus, unlike most techniques, the arterial waves are determined without a more difficult flow measurement or an experimental perturbation. We applied the technique to central aortic and femoral artery pressure waveforms from anesthetized dogs during drug infusions, volume changes, and cardiac pacing. The calculated waves predicted an abdominal aortic pressure waveform measurement more accurately (2.4 mmHg error) than the analyzed waveforms (5.3 mmHg average error); reliably predicted relative changes in a femoral artery flow measurement (14.7% error); and changed as expected with selective vasoactive drugs. The ratio of the backward- to forward-wave magnitudes was 0.37 ± 0.05 during baseline. This index increased by ~50% with phenylephrine and norepinephrine, decreased by ~60% with dobutamine and nitroglycerin, and changed little otherwise. The time delay between the waves in the central aorta was 175 ± 14 ms during baseline. This delay varied by ±~25% and was inversely related to mean pressure.

Estimation of the Aortic Pressure Waveform and Beat-to-Beat Relative Cardiac Output Changes From Multiple Peripheral Artery Pressure Waveforms

We introduce a patient- and time-specific technique to estimate the clinically more relevant aortic pressure (AP) waveform and beat-to-beat relative changes in cardiac output (CO) from multiple peripheral artery pressure (PAP) waveforms distorted by wave reflections. The basic idea of the technique is to first estimate the AP waveform by applying a new multichannel blind system identification method that we have developed (rather than the conventional generalized transfer function) to the PAP waveforms and then estimate the beat-to-beat proportional CO by fitting a Windkessel model to the estimated waveform in which wave distortion should be attenuated. We present an evaluation of the technique with respect to four swine datasets including simultaneous measurements of two peripheral AP waveforms, a reference AP waveform, and reference aortic flow probe CO during diverse hemodynamic interventions. Our results show an overall AP waveform error of 3.5 mmHg and an overall beat-to-beat CO error of 12.9% (after a single CO calibration in each animal). These estimation errors represent substantial improvements compared to those obtained with several alternative PAP waveform analysis techniques. With further successful testing, the new technique may ultimately be employed for automated and less invasive monitoring of central hemodynamics in various cardiovascular patients.

Blind identification of the central aortic pressure waveform from multiple peripheral arterial pressure waveforms.

We introduce a blind identification technique to reconstruct the clinically more relevant central aortic pressure waveform from multiple less invasively measured peripheral arterial pressure waveforms. We conducted initial testing of the technique in two swine in which peripheral arterial pressure waveforms from the femoral and radial arteries and reference central aortic pressure were simultaneously measured during diverse hemodynamic conditions. We report an overall error between the estimated and measured central aortic pressure waveforms of 4.8%. Potential clinical applications of the technique may include critical care monitoring with respect to invasive catheter systems and emergency and home monitoring with respect to non-invasive arterial pressure transducers

An early respiratory distress detection method with Markov models.

A method for early detection of respiratory distress in hospitalized patients which is based on a multi-parametric analysis of respiration rate (RR) and pulse oximetry (SpO2) data trends to ascertain patterns of patient instability pertaining to respiratory distress is described. Current practices of triggering caregiver alerts are based on simple numeric threshold breaches of SpO2. The pathophysiological patterns of respiratory distress leading to in-hospital deaths are much more complex to be detected by numeric thresholds. Our pattern detection algorithm is based on a Markov model framework based on multi-parameter pathophysiological patterns of respiratory distress, and triggers in a timely manner and prior to the violation of SpO2 85-90% threshold, providing additional lead time to attempt to reverse the deteriorating state of the patient. We present the performance of the algorithm on MIMIC II dataset resulting in true positive rate of 92% and false positive rate of 6%.

Estimation of the aortic pressure waveform from a peripheral artery pressure waveform via an adaptive transfer function

We developed a new technique to estimate the aortic pressure (AP) waveform from a single peripheral artery pressure (PAP) waveform. The technique 1) employs a parallel tube model of the arterial tree to establish a transfer function relating PAP to AP with unknown parameters; 2) estimates the parameters from the measured PAP waveform, along with a single non-invasive measurement of the wave transmission delay from the aorta to the peripheral artery measurement site, by exploiting the fact that aortic flow is zero during diastole; and 3) applies the transfer function to the PAP waveform to predict the unmeasured AP waveform. In this way, in contrast to the conventional generalized transfer function paradigm, the transfer function is able to adapt to the inter-subject and temporal variability of the arterial tree. We applied this adaptive transfer function technique to PAP waveforms measured from five dogs instrumented with reference AP catheters during various hemodynamic interventions. Our results showed that the technique was able to reliably estimate the AP waveform with an overall error of 4.2 mmHg. For comparison, the corresponding errors of two previously proposed generalized transfer functions trained on a subset of the same canine data were, on average, 19% larger.

Estimation of the aortic pressure waveform from a radial artery pressure waveform via an adaptive transfer function: Feasibility demonstration in swine

We previously proposed a new technique to estimate the physiologically and clinically more relevant central aortic pressure (AP) waveform from a conveniently and safely measured peripheral artery pressure (PAP) waveform distorted by wave reflections. In contrast to conventional generalized transfer function (GTF) techniques, the technique is able to adapt the transfer function relating PAP to AP to the inter-patient and temporal variability of the arterial tree by defining it through a tube model and invoking the fact that aortic flow is negligible during diastole to estimate the unknown model parameters. We conducted feasibility testing of this adaptive transfer function technique here with respect to radial artery pressure (RAP) waveforms, for the first time, as well as femoral artery pressure (FAP) waveforms from four swine instrumented with AP catheters during several hemodynamic conditions. Our results showed that the AP waveforms estimated by the technique from the RAP and FAP waveforms were in superior agreement to the measured AP waveforms (overall respective errors of 4.1 and 4.8 mmHg) than the two unprocessed PAP waveforms (9.1 and 8.1 mmHg) and a previous GTF technique trained on a subset of the same data (5.0 and 5.8 mmHg).

Quantification of forward and backward arterial waves by model-based analysis of aortic and femoral artery pressure waveforms

We developed a technique to quantify forward and backward arterial waves by model-based analysis of aortic and femoral artery pressure waveforms. Thus, in contrast to conventional techniques, our technique does not require a more difficult arterial flow measurement. We validated the forward and backward waves through a set of canine experiments by showing that the waves accurately predicted a third arterial pressure waveform measurement and changed in the expected manner to interventions of known effect. We also calculated the waves during nine different hemodynamic conditions. Our results showed that the relative magnitude of the backward wave was smallest during nitroglycerin and dobutamine and largest during phenylephrine and hemorrhage, while the time delay between the two waves was smallest during atrial pacing and about the same during the remaining eight conditions.

Continuous Ejection Fraction Estimation by Model-Based Analysis of an Aortic Pressure Waveform: Comparison to Echocardiography

Left ventricular ejection fraction (EF) is perhaps the most clinically significant index of global ventricular function. EF is measured in clinical practice using imaging methods such as non-invasive echocardiography. However, imaging methods generally require a skilled operator and expensive equipment. Thus, EF is not sufficiently monitored. To this end, we have recently developed a novel technique to continuously (i.e., automatically) estimate EF by model-based analysis of an aortic pressure waveform. Here, we review the technique and present its evaluation with respect to reference echocardiography measurements from three dogs during diverse interventions. We report an overall EF error of only 8.3%. With further successful testing, the technique may ultimately be utilized for continuous EF monitoring in research and clinical settings in which an aortic catheter is employed.

Identification of the Total Peripheral Resistance Baroreflex Impulse Response from Spontaneous Hemodynamic Variability: Validation by Chronic Arterial Baroreceptor Denervation

We have previously proposed a technique for estimating the static gain values and impulse response of the arterial and cardiopulmonary total peripheral resistance (TPR) baroreflex by mathematical analysis of beat-to-beat fluctuations in arterial blood pressure, cardiac output, and stroke volume. In this study, we evaluated the technique with respect to spontaneous hemodynamic variability measured from seven conscious dogs before and after chronic arterial baroreceptor denervation. Our results show that the technique correctly predicted the alterations in TPR baroreflex functioning that are known to occur following the baroreceptor denervation.