• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.38 no.8
• /
• pp.715-720
• /
• 2014

In order to maximize the effect of left ventricular assist device (LVAD) on ventricular unloading, the therapy should be begun at appropriate level of heart failure severity. We predicted pumping efficacy of LVAD according to the severity of heart failure theoretically. We used 3 dimensional finite element model of ventricle coupled with 6 Wind-kessel compartmental model of vascular system. Using the computational model, we predicted cardiac responses such as contractile ATP consumption of ventricle, left ventricular pressure, cardiac output, ejection fraction, and stroke work according to the severity of ventricular systolic dysfunction under the treatments of continuous LVAD. Contractile ATP consumption, which indicates the ventricular energetic loading condition decreased maximally at the $5^{th}$ level heart-failure under LVAD therapy. We conclude that optimal timing for LVAD treatment is $5^{th}$ level heart-failure when considering LVAD treatment as "bridge to recovery".

• Journal of Biomedical Engineering Research
• /
• v.35 no.5
• /
• pp.119-124
• /
• 2014

A Ventricular assist device (VAD) is one of the most efficient treatments to raise the survivability of the end stage heart failure patient. However, some of LVAD patients have died for the failures and improper control of LVAD. To detect critical dangers in LVAD, the monitoring methods of LVAD outflow have been requested, because it can be affected by patient's hemodynamic states and abnormal conditions of LVAD. In the case of an external pulsatile LVAD, the air movement through the air line can be used to estimate LVAD outflow. In this study, the air movement in the air-line of the extracorporeal pulsatile LVAD was measured with a differential pressure sensor between different points. The precise estimation of air movement could be achieved by additional measurement of air pressure. In a series of in-vitro experiments, the LVAD outflow were changed according to the afterload of LVAD and the differential pressure of LVAD didn't have close correlation with the LVAD outflow that were measured with an ultrasonic flowmeter at the same time. However, new precise estimation with the data from differential pressure and one point pressure in the air-line showed higher correlations with LVAD outflow.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.12 no.4
• /
• pp.22-28
• /
• 2013

It is important to begin left ventricular assist device (LVAD) treatment at appropriate time for heart failure patients who expect cardiac recovery after the therapy. In order to predict the optimal timing of LVAD implantation, we predicted pumping efficacy of LVAD according to the severity of heart failure theoretically. We used LVAD-implanted cardiovascular system model which consist of 8 Windkessel compartments for the simulation study. The time-varying compliance theory was used to simulate ventricular pumping function in the model. The ventricular systolic dysfunction was implemented by increasing the end-systolic ventricular compliance. Using the mathematical model, we predicted cardiac responses such as left ventricular peak pressure, cardiac output, ejection fraction, and stroke work according to the severity of ventricular systolic dysfunction under the treatments of continuous and pulsatile LVAD. Left ventricular peak pressure, which indicates the ventricular loading condition, decreased maximally at the 1st level heart-failure under pulsatile LVAD therapy and 2nd level heart-failure under continuous LVAD therapy. We conclude that optimal timing for pulsatile LVAD treatment is 1st level heart-failure and for continuous LVAD treatment is 2nd level heart-failure when considering LVAD treatment as "bridge to recovery".

• Proceeding of EDISON Challenge
• /
• 2017.03a
• /
• pp.650-652
• /
• 2017

the aim of this study was to observe the combined effect of the CRT and LVAD on electromechanical cardiac behavior under LBBB and RBBB conditions computationally. We performed simulation by using advanced electromechanics model of failing ventricle combined with lumped model represents circulatory system, CRT and LVAD. We analyzed seven failing ventricle model including normal sinus rhythm, LBBB, LBBB coupled with CRT, LBBB coupled with CRT and LVAD, RBBB, RBBB coupled with CRT, and RBBB coupled with CRT and LVAD. We compared the effect from CRT and the effect from combined CRT and LVAD to both under LBBB and RBBB conditions. The results showed that the combined CRT and LVAD contributed a better hemodynamic compared to single CRT. This combined system synchronized the electrical activation greatly under LBBB and slightly under RBBB. It also shortened mechanical activation time which resulted short electromechanical delay. More importantly, the combined system produced better mechanical responses under both LBBB and RBBB conditions.

• Journal of the Korean Society for Precision Engineering
• /
• v.15 no.4
• /
• pp.83-90
• /
• 1998

In this paper, we present neural network for control of Left Ventricular Assist Device(LVAD) system with a pneumatically driven mock circulation system. Beat rate(BR), Systole-Diastole Rate(SDR) and flow rate are collected as the main variables of the LVAD system. System modeling is completed using the neural network with input variables(BR, SBR, their derivatives, actual flow) and output variable(actual flow). It is necessary to apply high perfomance control techniques, since the LVAD system represent nonlinear and time-varing characteristics. Fortunately. the neural network can be applied to control of a nonlinear dynamic system by learning capability In this study, we identify the LVAD system with neural network and control the LVAD system by PID controller and neural network feedforward controller. The ability and effectiveness of controlling the LVAD system using the proposed algorithm will be demonstrated by experiment.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1996.11a
• /
• pp.260-266
• /
• 1996

In this paper, we presents neural network identification and control of highly complicated nonlinear Left Ventricular Assist Device(LVAD) system with a pneumatically driven mock circulation system. Generally the LVAD system need to compensate nonlinearities. Hence, it is necessary to apply high performance control techniques. Fortunately, the neural network can be applied to control of a nonlinear dynamic system by learning capability. In this study, we identify the LVAD system with Neural Network Identification. Once the NNI has learned the dynamic model of LVAD system, the other network, called Neural Network Controller(NNC), is designed for control of a LVAD system. The ability and effectiveness of identifying and controlling a LVAD system using the proposed algorithm will be demonstrated by computer simulation.

• Journal of Biomedical Engineering Research
• /
• v.17 no.1
• /
• pp.43-48
• /
• 1996

We investigated the energistics of the physiological heart model by comparing predictive indexes of the myocardial oxygen consumption (MOC), such as tension-time index (R), tension-time or force-time inteual (FTI), rate-pressure product (RPP), pressure-work index, and systolic pressure-volume area (PVA) when using the electro-hydraulic left ventricular device (LVAD). We developed the model of LVAD incorporated the closed-loop cardiovascular system with a baroreceptor which can control heart rate and time-varying elastance of left and right ventricles. On considering the benefit of the LVAD, the effects of various operation modes, especially timing of assistance, were evaluated using this coupled computer model. Overall results of the computer simulation shows that our LVAD can unload the ischemic (less contractile) heart by decreasing the MU and increasing coronary flow. Because the pump ejection at the end diastolic phase of the natural heart may increase the afterload of the left ventricle, the control scheme of our LVAD must prohibit ejecting at this time. Since the increment of coronary flow is proportional to the peak aortic pressure after ventricle contraction, the LVAD must eject immediately following the closure of the aortic valve to increase oxygen availability.

• Proceedings of the KOSOMBE Conference
• /
• v.1993 no.11
• /
• pp.89-92
• /
• 1993

In this article, we present and analyze the results of the three consecutive in vivo experiments of the LVAD to evaluate the function of the LVAD and the adverse effects on living animals. We applied the LVAD consecutively to three mongrel dogs and the circulation of the blood was assisted under the anesthesia. We used in general both the asynchronous mode and the synchronous mode to drive the LVAD. During the experiments we monitored the dogs with a polygraph to evaluate the function of the LVAD and the additional effects on the natural hearts. We also examined several clinical pathologic tests in order to see the effects of the LVAD to the red blood cells and the other internal organs. The dogs survived for two to there days. The LVAD assisted the circulatory system at the maximum assist flow rate of 3.0 1/min. Although the red blood cells of the dogs had mechanical damages by the LVAD to result in the hemolysis, the degree of the hemolysis was not so high and the damages caused by the hemolysis on the dogs were not serious. The myocardium of the first dog was gradually worsened and eventually failed. The damage of the myocardium was due to the asynchronous driving mode of the LVAD. The other organs did not have serious damages due to the application of the LVAD. The main purpose of this paper is to evaluate the results of the in vivo experiments of the LVAD and to find better ways to the application of the LVAD to human beings.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1997.04a
• /
• pp.150-155
• /
• 1997

In this paper,we present neural network for control of Left Ventricular Assist Device(LVAD)system with a pneumatically driven mock cirulation system. It is necessary to apply high perfomance control techniques, since the LVAD system represent nonlinear and time-varing characteristics. Fortunately, the neural network can be applied to control of a nonliner dynamic system by learning capability. In this study,we identify the LVAD system with neural network and control the LVAD system by PID controller and neural network feedforward controller. The ability and effectiveness of controlling the LVAD system using the proposed algorithm will be demonstrated by computer simulation and experiment.

• 제어로봇시스템학회:학술대회논문집
• /
• 1995.10a
• /
• pp.514-517
• /
• 1995

A new stroke output control algorithm with a fuzzy logic for an electrohydraulic left ventricular assist device(EH-LVAD) was developed. The EH-LVAD pumps out blood from left atrium actively. Excessive suction of blood may cause fatal damage in left atrium. The LVAD has to provide a maximal stroke output without collapse of left atrium. In this study a new fuzzy algorithm for predicting and detecting suction and doing proper action on LVAD without using an extra pressure sensor but with bellows pressure signal and motor current signal is developed. The performance of the fuzzy control algorithm is demonstrated by the results from mock circulatory experiments.