Showing papers by "Ramakrishna Mukkamala published in 2006"

Continuous cardiac output monitoring in humans by invasive and noninvasive peripheral blood pressure waveform analysis.

Abstract: We present an evaluation of a novel technique for continuous (i.e., automatic) monitoring of relative cardiac output (CO) changes by long time interval analysis of a peripheral arterial blood press...

Estimation of arterial and cardiopulmonary total peripheral resistance baroreflex gain values: validation by chronic arterial baroreceptor denervation.

Abstract: Feedback control of total peripheral resistance (TPR) by the arterial and cardiopulmonary baroreflex systems is an important mechanism for short-term blood pressure regulation. Existing methods for...

A weighted-principal component regression method for the identification of physiologic systems

Abstract: We introduce a system identification method based on weighted-principal component regression (WPCR). This approach aims to identify the dynamics in a linear time-invariant (LTI) model which may represent a resting physiologic system. It tackles the time-domain system identification problem by considering, asymptotically, frequency information inherent in the given data. By including in the model only dominant frequency components of the input signal(s), this method enables construction of candidate models that are specific to the data and facilitates a reduction in parameter estimation error when the signals are colored (as are most physiologic signals). Additionally, this method allows incorporation of preknowledge about the system through a weighting scheme. We present the method in the context of single-input and multi-input single-output systems operating in open-loop and closed-loop. In each scenario, we compare the WPCR method with conventional approaches and approaches that also build data-specific candidate models. Through both simulated and experimental data, we show that the WPCR method enables more accurate identification of the system impulse response function than the other methods when the input signal(s) is colored

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

Abstract: 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.

Continuous cardiac output and left atrial pressure monitoring by pulmonary artery pressure waveform analysis.

Abstract: We introduce a novel technique for continuous (i.e., automatic) monitoring of cardiac output (CO) and left atrial pressure (LAP) by mathematical analysis of a pulmonary artery pressure (PAP) waveform. To obtain an initial evaluation of the technique, we applied it to PAP waveforms obtained from nine critically ill patients and compared the resulting CO and LAP estimates with standard operator- dependent thermodilution and pulmonary capillary wedge pressure measurements, respectively. We report that the technique achieved an overall CO error of 17.2% and an overall LAP error of 15.8%. With further testing, the technique may ultimately be employed so as to permit, for the first time, continuous CO and LAP monitoring in critically ill patients. I. INTRODUCTION ARDIAC output (CO) represents the total blood flow rate in the circulation, while left atrial pressure (LAP) generally indicates the blood pressure attained in the left ventricle during the cardiac filling phase. CO and LAP are two of the most important quantities to be able to monitor in critically ill patients, as they facilitate the diagnosis, monitoring, and treatment of various disease processes such as left ventricular failure, mitral valve disease, and shock of any cause. For example, a decrease in CO while LAP is rising would indicate that the patient is in left ventricular failure, whereas a decrease in CO while LAP is falling may indicate that the patient is going into hypovolemic shock. The standard methods for monitoring CO and LAP in critically ill patients both involve the use of the balloon- tipped, flow-directed pulmonary artery catheter (1)(2). CO is specifically estimated according to the thermodilution method. This method involves injecting a bolus of cold saline in the right atrium, measuring temperature downstream in the pulmonary artery, and computing the average CO based on conservation laws. LAP is estimated through the pulmonary capillary wedge pressure (PCWP) method. This method involves advancing the catheter into a branch of the pulmonary artery, inflating the balloon, and measuring the resulting steady-state PCWP. In theory, PCWP should nearly equal LAP, since flow has ceased

Continuous left ventricular ejection fraction monitoring by central aortic pressure waveform analysis.

Abstract: Left ventricular ejection fraction (EF) is perhaps the most clinically significant index of global ventricular function. EF is measured in clinical practice via imaging methods such as echocardiography. However, these methods generally require a well-trained operator and expensive capital equipment. Thus, EF measurements are only obtained in the clinical setting and are usually made few and far between. To expand the measurement of this critical hemodynamic variable, our overarching hypothesis is that EF may be continuously (i.e., automatically) monitored by mathematical analysis of routinely measured blood pressure waveforms. Here, we introduce a novel technique for estimating the absolute EF by model-based analysis of only a central aortic pressure (CAP) waveform. We then demonstrate the validity of the technique with respect to five conscious dogs in which reference EF was independently measured before and after chronic pacing induced heart failure. With further successful testing, the technique may potentially be utilized for continuous EF monitoring in research and clinical settings in which an aortic catheter is employed as well as for ambulatory EF monitoring in conjunction with recently developed implantable devices for measuring CAP.

Selective quantification of cardiac sympathetic and parasympathetic nervous function based on the heart rate baroreflex impulse response

Abstract: We introduce an improved technique for selectively quantifying cardiac sympathetic and parasympathetic nervous function based on the identification of the heart rate (HR) baroreflex impulse response from non-invasive cardio-respiratory measurements. We have tested the technique with respect to 24 humans breathing randomly or spontaneously under selective pharmacological autonomic blockade. Our results show that the technique performs much better than traditional HR power spectral indices in terms of predicting the known drug effects and is effective during spontaneous breathing.

Estimation of the central arterial pressure waveform and beat-to-beat stroke volume from multiple peripheral arterial pressure waveforms

Abstract: We introduce a new technique to estimate the clinically more relevant central arterial pressure (AP) waveform and relative beat-to-beat changes in stroke volume (SV) from multiple, less invasively measured peripheral AP waveforms distorted by wave reflections. The basic idea is to first reconstruct the central AP waveform by applying multi-channel blind system identification and to then estimate beat-to-beat proportional SV by fitting a Windkessel model to the reconstructed waveform in which wave distortion should be attenuated. We evaluated the technique in four swine in which two peripheral AP waveforms and gold standard central AP and SV were simultaneously measured during diverse hemodynamic interventions. We report an overall central AP error of 5.6% and an overall beat-to-beat SV error of 14.7%.