• 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.

• International Journal of Control, Automation, and Systems
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• 제1권3호
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• pp.368-375
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• 2003

Data fusion approach is investigated to avoid suction in the left ventricular assist system (LVAS) using a nonpulsatile pump. LVAS requires careful control of pump speed to support the heart while preventing suction in the left ventricle and providing proper cardiac output at adequate perfusion pressure to the body. Since the implanted sensors are usually unreliable for long-term use, a sensorless approach is adopted to detect suction. The pump model is developed to provide the load coefficient as a necessary signal to the data fusion system without the implanted sensors. The load coefficient of the pump mimics the pulsatility property of the actual pump flow and provides more comparable information than the pump flow after suction occurs. Four signals are generated from the load coefficient as inputs to the data fusion system for suction detection and a neural fuzzy method is implemented to construct the data fusion system. The data fusion approach has a good ability to classify suction status and it can also be used to design a controller for LVAS.

• 대한의용생체공학회:의공학회지
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• 제22권4호
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• pp.349-358
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• 2001

A novel electro-mechanical implantable ventricular assist system is developed as a bridge to transplantation or recovery for patients with end-stage heart failure. The developed system is composed of an implanted blood pump, an external monitoring system which stores data, and a wearable system including a portable external driver and a portable power supply system. The blood pump is designed to be implanted into the left upper abdominal space and provides blood flow from the left ventricular apex to the aorta. The pulsatile blood flow is generated by a double cylindrical cam. There was mo excessive heat emission from the blood pump into the temperature-controlled chamber in the heat test and no stagnated flow within the blood sac by the observation in the flow visualization test. Animal experiments were performed using sheep and calves. The maximum assist flow rate reached 7.85L/min in the animal experiment. The evaluation results showed that the developed system was feasible for the implantable ventricular assist system. The long-term in vitro durability test and mid-term in vivo experiments are in progress and mow the modified next model is under development.

• 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 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.

• 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.

• 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.

• 대한의용생체공학회:의공학회지
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• 제19권1호
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• pp.39-46
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• 1998

본 연구에서 복잡한 비선형적 특성을 갖는 공압식 좌심실보조장치의 모델링과 제어에 인공신경망을 제안하였다. 일반적으로 좌심실보조장치는 비선형이 보상되어야 하는데 인공신경망은 학습능력에 의해 비선형 동적 시스템의 제어에 적용될 수 있다. 인공신경망 모델링을 통해 좌심실 보조장치의 동적 모델을 모델링하고 이를 기반으로 하여 인공신경망 제어기가 설계되었다. 제안된 알고리즘을 이용한 좌심실보조장치의 모델링과 제어성능 및 유효성은 컴퓨터 시뮬레이션에 의해 증명되었다.