Cardiovascular engineering
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Comparative Study
Afterload assessment with or without central venous pressure: a preliminary clinical comparison.
A clinical comparison, of two methods of afterload assessment, has been made. The first method, systemic vascular resistance index (SVR(i)), is based upon the traditional formula for afterload which utilizes central venous pressure (CVP), as well as cardiac index (C(i)), and mean arterial blood pressure (MAP). The second method, total systemic vascular resistance index (TSVR(i)), also uses MAP and C(i). ⋯ Furthermore, there was also a high degree of correlation (ranging from 94 to 100%) noted between the hour-to-hour change in SVR(i) with the hour-to-hour change in TSVR(i) (P < 0.0001). The results, of this pilot study, support the premise that the use of CVP may not always be necessary for afterload evaluation in the clinical setting. Minimally-invasive means of measuring both C(i) and MAP, without CVP, may be adequate for use in assessing afterload.
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Our institution is in development of a low frequency, non-invasive Diastolic Timed Vibrator (DTV) for use in emergency treatment of ST Elevation Myocardial Infarction (STEMI). It is preferable to avoid vibration emissions during the IsoVolumetric Contraction Period (IVCP) and at least the majority of mechanical systole thereafter, as systolic vibration may cause a negative inotropic effect in the ischemic heart. Furthermore diastolic vibration should preferably include the IsoVolumetric Relaxation Period (IVRP) which has been shown in clinical studies to improve cardiac performance and enhance coronary flow. ⋯ A DTV should ideally be able to stop vibrating on or before the peak of the first dominant deflection of a QRS complex, and begin vibrating near the peak of the T wave. Given early detection of ventricular depolarization can occur 10-20 ms prior to R wave peak, it is proposed that a DTV should preferably be able to stop vibrating within 10 ms of a triggered stop command. Onset of vibration during peak of T wave could be approximated by a rate adapted Q-T interval regression equation, and then fine tuned by manual adjustment during therapy.
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Analysis of digital volume pulse (DVP) signal measured by photoplethysmograph (PPG) technique is a low cost non-invasive method of obtaining vital information related to arterial conditions. In this paper, we present a new two-pulse synthesis (TPS) model for deriving arterial parameters, useful for noninvasive assessment of human vascular health. The model is based on the use of Rayleigh function. ⋯ The TPS model compares well with the conventional methods in determining parameters such as pulse transit time or foot-to-foot delay (D), reflection index (RI), stiffness index (SI) and pulse wave velocity (PWV). A new parameter, viz. differential pulse spread (DPS) has also been introduced for DVP signals using the model. The differential pulse spread provides a new dimension to estimate the process of arterial degeneration.
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Quantitative evaluation of cardiac function from cardiac magnetic resonance (CMR) images requires the identification of the myocardial walls. This generally requires the clinician to view the image and interactively trace the contours. Especially, detection of myocardial walls of left ventricle is a difficult task in CMR images that are obtained from subjects having serious diseases. ⋯ Difference of Gaussian weighting function (DoG) is newly introduced in random walk approach for blood pool (inner contour) extraction. The myocardial wall (outer contour) is segmented out by a modified active contour method that takes blood pool boundary as the initial contour. Promising experimental results in CMR images demonstrate the potentials of our approach.
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Evaluation of the time-varying parameters (Compliance, Resistance, and Inertance) that describe the right and left ventricles has been of interest for some years. Analyses usually involve a particular assertion regarding energy contributions or of the nature of the parameters themselves. ⋯ Coefficients of the polynomials are estimated from the observed data with use of the maximum likelihood method and stochastic calculus. The pump equation was finally evaluated in full from un-processed pressure and flow data and the method is provided herein.