• Journal of Chest Surgery
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• 제52권2호
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• pp.105-108
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• 2019

Right heart failure is a relatively common complication after left ventricular assist device (LVAD) implantation. Severe right heart failure can be managed by temporary right ventricular assist device (RVAD) implantation. However, trans-sternal RVAD insertion requires a subsequent third sternotomy for cannula removal. Herein, we present a case of RVAD insertion via a left anterior mini-thoracotomy after LVAD implantation in a patient with alcohol-induced cardiomyopathy.

• 대한의용생체공학회:의공학회지
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• 제17권3호
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• pp.387-394
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• 1996

This paper presents a Neural Network Identification(NNI) method for modeling of highly complicated nonlinear and time varing human system with a pneumatically driven mock circulatory system of Left Ventricular Assist Device(LVAD). This system consists of electronic circuits and pneumatic driving circuits. The initiation of systole and the pumping duration can be determined by the computer program. The line pressure from a pressure transducer inserted in the pneumatic line was recorded System modeling is completed using the adaptively trained backpropagation learning algorithms with input variables, heart rate(HR), systole-diastole rate(SDR), which can vary state of system. Output parameters are preload, afterload which indicate the systemic dynamic characteristics. Consequently, the neural network shows good approximation of nonlinearity, and characteristics of left Ventricular Assist Device. Our results show that the neural network leads to a significant improvement in the modeling of highly nonlinear Left Ventricular Assist Device.

• 한국기계가공학회지
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• 제12권4호
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• pp.22-28
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• 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".

• Journal of Chest Surgery
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• 제53권2호
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• pp.79-81
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• 2020

Treatment options for children with end-stage heart failure are limited. We report the first case of a successful pediatric heart transplantation bridged with a durable left ventricular assist device in Korea. A 10-month-old female infant with dilated cardiomyopathy and left ventricular non-compaction was listed for heart transplantation. During the waiting period, the patient's status deteriorated. Therefore, we decided to provide support with a durable left ventricular assist device as a bridge to transplantation. The patient was successfully bridged to heart transplantation with effective support and without any major adverse events.

• Journal of Chest Surgery
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• 제48권6호
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• pp.407-410
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• 2015

A two-month-old infant presented with coarctation of the aorta, severe left ventricular dysfunction, and moderate to severe mitral regurgitation. Through median sternotomy, the aortic arch was repaired under cardiopulmonary bypass and regional cerebral perfusion. The patient was postoperatively supported with a left ventricular assist device for five days. Left ventricular function gradually improved, eventually recovering with the concomitant regression of mitral regurgitation. Prompt surgical repair of coarctation of the aorta is indicated for patients with severe left ventricular dysfunction. A central approach for surgical repair with a back-up left ventricular assist device is a safe and effective treatment strategy for these patients.

• Journal of Chest Surgery
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• 제47권4호
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• pp.409-412
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• 2014

Percutaneous extracorporeal life support (P-ECLS) is a useful modality for the management of refractory cardiac or pulmonary failure. However, venoarterial P-ECLS may result in a complication of left ventricular distension. In this case report, we discuss a patient with drug-induced dilated cardiomyopathy managed with venoarterial P-ECLS and a left atrial vent catheter. The venoarterial P-ECLS was modified to a paracorporeal left ventricular assist device (LVAD) by removing the femoral venous cannula. After 28 days of hospitalization, the patient was successfully weaned from the paracorporeal LVAD and discharged home from the hospital.

• 한국정밀공학회지
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• 제15권4호
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• pp.83-90
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• 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.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1997년도 춘계학술대회 논문집
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• pp.150-155
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• 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.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1996년도 추계학술대회 논문집
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• pp.260-266
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• 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.

• 제어로봇시스템학회논문지
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• 제4권3호
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• pp.315-320
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• 1998

In this paper, we present the PID control method for the controlling flow rate of highly complicated nonlinear Left Ventricular Assist Device(LVAD) with pneumatically driven mock circulatory system. Beat Rate (BR), Systole-Diastole Rate (SDR) and flow rate are used as the main variables of the LVAD system. System modeling is completed using the neural network with input variables (BR, SDR, their derivatives, actual flow) and an output valiable(actual flow). Then, as the basis of this model, we perform the simulation of PID control to predict the performance and tendency of the system and control the flow rate of LVAD system using the PID controller. The ability and effectiveness of identifying and controlling a LVAD system using the proposed algorithm will be demonstrated through computer simulation and experiments.