Biomedical subjects

Dean M Karantonis

Publications and source records attributed to Dean M Karantonis.

3 recordsLinked to original sources

PubMed2007

Noninvasive average flow estimation for an implantable rotary blood pump: a new algorithm incorporating the role of blood viscosity.

The effect of blood hematocrit (HCT) on a noninvasive flow estimation algorithm was examined in a centrifugal implantable rotary blood pump (iRBP) used for ventricular assistance. An average flow estimator, based on three parameters, input electrical power, pump speed, and HCT, was developed. Data were collected in a mock loop under steady flow conditions for a variety of pump operating points and for various HCT levels. Analysis was performed using three-dimensional polynomial surfaces to fit the collected data for each different HCT level. The polynomial coefficients of the surfaces were then analyzed as a function of HCT. Linear correlations between estimated and measured pump flow over a flow range from 1.0 to 7.5 L/min resulted in a slope of 1.024 L/min (R2=0.9805). Early patient data tested against the estimator have shown promising consistency, suggesting that consideration of HCT can improve the accuracy of existing flow estimation algorithms.

Algorithms↗

PubMed2006

Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring.

The real-time monitoring of human movement can provide valuable information regarding an individual's degree of functional ability and general level of activity. This paper presents the implementation of a real-time classification system for the types of human movement associated with the data acquired from a single, waist-mounted triaxial accelerometer unit. The major advance proposed by the system is to perform the vast majority of signal processing onboard the wearable unit using embedded intelligence. In this way, the system distinguishes between periods of activity and rest, recognizes the postural orientation of the wearer, detects events such as walking and falls, and provides an estimation of metabolic energy expenditure. A laboratory-based trial involving six subjects was undertaken, with results indicating an overall accuracy of 90.8% across a series of 12 tasks (283 tests) involving a variety of movements related to normal daily activities. Distinction between activity and rest was performed without error; recognition of postural orientation was carried out with 94.1% accuracy, classification of walking was achieved with less certainty (83.3% accuracy), and detection of possible falls was made with 95.6% accuracy. Results demonstrate the feasibility of implementing an accelerometry-based, real-time movement classifier using embedded intelligence.

Acceleration↗

PubMed2006

Identification and classification of physiologically significant pumping states in an implantable rotary blood pump.

In a clinical setting it is necessary to control the speed of rotary blood pumps used as left ventricular assist devices to prevent possible severe complications associated with over- or underpumping. The hypothesis is that by using only the noninvasive measure of instantaneous pump impeller speed to assess flow dynamics, it is possible to detect physiologically significant pumping states (without the need for additional implantable sensors). By varying pump speed in an animal model, five such states were identified: regurgitant pump flow, ventricular ejection (VE), nonopening of the aortic valve over the cardiac cycle (ANO), and partial collapse (intermittent and continuous) of the ventricle wall (PVC-I and PVC-C). These states are described in detail and a strategy for their noninvasive detection has been developed and validated using (n = 6) ex vivo porcine experiments. Employing a classification and regression tree, the strategy was able to detect pumping states with a high degree of sensitivity and specificity: state VE-99.2/100.0% (sensitivity/specificity); state ANO-100.0/100.0%; state PVC-I- 95.7/91.2%; state PVC-C-69.7/98.7%. With a simplified binary scheme differentiating suction (PVC-I, PVC-C) and nonsuction (VE, ANO) states, both such states were detected with 100% sensitivity.

Algorithms↗