Research Interests:
The Left Ventricular Assist Device (LVAD) is a mechanical device that can assist an ailing natural heart in performing its functions. The latest generation of such devices is a rotary-type pump which is generally much smaller, lighter,... more
The Left Ventricular Assist Device (LVAD) is a mechanical device that can assist an ailing natural heart in performing its functions. The latest generation of such devices is a rotary-type pump which is generally much smaller, lighter, and quieter than the conventional pulsatile-type ...
Research Interests: Applied Mathematics, Health Care, Control Systems, Performance Analysis, Low Noise Measurement Systems, and 10 moreLeft Ventricular Assist Device, State Space, Feedback, Equivalent Circuit, Mathematical Model, Cardiovascular system, Vectors, Electrical And Electronic Engineering, Time varying, and Left Ventricle
Research Interests:
Research Interests:
Research Interests:
A new suction detection system for rotary blood pumps used in left ventricular assist devices is presented. The system can correctly classify pump flow patterns, based on a discriminant analysis (DA) model that combines several indices... more
A new suction detection system for rotary blood pumps used in left ventricular assist devices is presented. The system can correctly classify pump flow patterns, based on a discriminant analysis (DA) model that combines several indices derived from the pump flow signal to make a decision about the pump status. The indices considered in this approach are frequency-, time-, and time-frequency-domain indices. The frequency-domain indices detect changes in the harmonic and subharmonic energy content of the pump flow signal when a suction event is occurring. The time-domain indices detect changes in pump flow pulsatility based on a beat-to-beat analysis of the pump flow and first derivative of pump flow. The time-frequency index can track variations in the standard deviation of the instantaneous frequency of the pump flow signal. These indices are combined in a DA decision system to generate a suction alarm. The proposed system has been tested in simulations and in-vivo experimental tests and produced satisfactory results.
Research Interests: Biomedical Engineering, Medicine, Fourier Analysis, Discriminant Analysis, Computer Simulation, and 11 moreStandard Deviation, Hemodynamics, Animals, Cattle, Time Factors, Experimental Tests, Reproducibility of Results, Continuous Flow Heart Assist Devices, Equipment Design, Pulsatile Flow, and Instantaneous frequency
A control system for rotary ventricular assist devices was developed to automatically regulate the pumping speed of the device to avoid ventricular suction. The control system comprises a suction detector and a fuzzy logic controller... more
A control system for rotary ventricular assist devices was developed to automatically regulate the pumping speed of the
device to avoid ventricular suction. The control system comprises
a suction detector and a fuzzy logic controller (FLC). The suction
detector can correctly classify pump flow patterns, using a
discriminant analysis (DA) model that combines several indices
derived from the pump flow signal, to classify the pump status as
one of the following: no suction (NS), moderate suction (MS), and
severe suction (SS). The discriminant scores, which are the output of the suction detector, were used as inputs to the FLC. Based on this information, the controller updates pump speed, providing adequate flow and pressure perfusion to the patient. The performance of the control system was tested in simulations over a wide range of physiological conditions, including hypertension, exercise, and strenuous exercising for healthy, sick, and very sick hearts, using a lumped parameter model of the circulatory system coupled with a left ventricular assist device. The controller was able to maintain cardiac output andmean arterial pressure within acceptable physiologic ranges, while avoiding suction, demonstrating the feasibility of the proposed control system.
device to avoid ventricular suction. The control system comprises
a suction detector and a fuzzy logic controller (FLC). The suction
detector can correctly classify pump flow patterns, using a
discriminant analysis (DA) model that combines several indices
derived from the pump flow signal, to classify the pump status as
one of the following: no suction (NS), moderate suction (MS), and
severe suction (SS). The discriminant scores, which are the output of the suction detector, were used as inputs to the FLC. Based on this information, the controller updates pump speed, providing adequate flow and pressure perfusion to the patient. The performance of the control system was tested in simulations over a wide range of physiological conditions, including hypertension, exercise, and strenuous exercising for healthy, sick, and very sick hearts, using a lumped parameter model of the circulatory system coupled with a left ventricular assist device. The controller was able to maintain cardiac output andmean arterial pressure within acceptable physiologic ranges, while avoiding suction, demonstrating the feasibility of the proposed control system.