Feedback control of a rotary left ventricular assist device supporting a failing cardiovascular system

Wang, Yu; Faragallah, George; Divo, Eduardo; Simaan, Marwan A.

doi:10.1109/acc.2012.6315555

Cited by 9 publications

(6 citation statements)

References 12 publications

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“…The deterministic model of the cardiovascular-LVAD system in this work was experimentally validated by comparing the hemodynamic waveforms with data of patients [16,28,29]. It is assumed that the patient has a healthy and normal right ventricle and pulmonary system for simplicity.…”

Section: Cardiovascular-lvad Modelmentioning

confidence: 99%

Stochastic Modeling and Dynamic Analysis of the Cardiovascular System with Rotary Left Ventricular Assist Devices

Son

2019

Mathematical Problems in Engineering

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The left ventricular assist device (LVAD) has been used for end-stage heart failure patients as a therapeutic option. The aortic valve plays a critical role in heart failure and its treatment with LVAD. The cardiovascular-LVAD model is often used to investigate the physiological demands required by patients and predict the hemodynamic of the native heart supported with a LVAD. As a "bridge to recovery" treatment, it is important to maintain appropriate and active dynamics of the aortic valve and the cardiac output of the native heart, which requires that the LVAD pump must be adjusted so that a proper balance between the blood contributed through the aortic valve and the pump is maintained. In this paper, we investigate how the pump power of the LVAD pump can affect the dynamic behaviors of the aortic valve for different levels of activity and different severities of heart failure. Our objective is to identify a critical value of the pump power (i.e., breakpoint) to ensure that the LVAD pump does not take over the pumping function in the cardiovascular-pump system and share the ejected blood with left ventricle to help the heart to recover. In addition, hemodynamic often involves variability due to patients' heterogeneity and the stochastic nature of cardiovascular system. The variability poses significant challenges to understand dynamic behaviors of the aortic valve and cardiac output. A generalized polynomial chaos (gPC) expansion is used in this work to develop a stochastic cardiovascular-pump model for efficient uncertainty propagation, from which it is possible to rapidly calculate the variance in the aortic valve opening duration and the cardiac output in the presence of variability. The simulation results show that the gPC-based cardiovascular-pump model is a reliable platform that can provide useful information to understand the effect of LVAD pump on the hemodynamic of the heart.

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Section: Cardiovascular-lvad Modelmentioning

confidence: 99%

Stochastic Modeling and Dynamic Analysis of the Cardiovascular System with Rotary Left Ventricular Assist Devices

Son

2019

Mathematical Problems in Engineering

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“…An important challenge facing the increased use of the LVAD as a bridge-to-recovery [2]- [4] is the development of an appropriate feedback controller which ensures (1) that the circulatory demands of the patients in terms of blood flow are met, and (2) that the aortic valve is operating normally. The controller's task is to adjust the blood flow through the pump by automatically regulating the pump rotational speed which is controlled by the pump power delivered to the device.…”

Section: Introductionmentioning

confidence: 99%

“…We will derive the features from the patient's systemic vascular flow waveform in the circulatory system; a hemodynamic variable that is easily accessible using a flow sensor cuff placed on the patients arm and is readily available on the non-linear time-varying cardiovascular-LVAD model [4] that will be used to perform the necessary simulations. In this paper, we propose and test a new algorithm for detection and classification of aortic valve dynamics based on the Lagrangian Support Vector Machine (LSVM) approach in pattern recognition.…”

Section: Introductionmentioning

confidence: 99%

Detection of aortic valve dynamics in bridge-to-recovery feedback control of the Left Ventricular Assist Device

Wang

Faragallah

Simaan

2014

2014 European Control Conference (ECC)

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Aortic valve dynamics -which implies continuous opening and closing of the aortic valve in each cardiac cycle during the feedback control of the rotary Left Ventricular Assist Devices (LVAD) support -has important clinical implications for patients with mild congestive heart failure. When the LVAD is implanted in such patients as a bridge-torecovery device, permanent closure of the aortic valve must be avoided by maintaining proper control on the power delivered to the device. In this paper, a new aortic valve dynamics detection algorithm based on a Lagrangian Support Vector Machine (LSVM) model is presented. A detection indicator is derived from the systemic vascular flow signal in the circulatory system using a nonlinear mathematical model of the combined cardiovascular-LVAD system and forms the input to the LSVM classifier. The LSVM classifier is trained and tested to classify the aortic valve dynamics into two states: aortic valve opening and closing (i.e. operating normally) and aortic valve permanently closed. Our results show that the proposed algorithm can detect the aortic valve dynamics effectively in terms of classification accuracy and stability. This classifier will be an integral part in the development of a feedback controller for the LVAD when used on patients as a bridge-to-recovery device. The output of the classifier will be used to adjust the power delivered to the LVAD to ensure that the aortic valve opens and closes normally within each cardiac cycle while at the same time making sure that the physiological demands of the patient are met.

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“…The rotary LVAD is a mechanical pump surgically implanted in the patient as a bridge from the left ventricle to the aorta to help maintain the flow of blood from the patient's heart, which cannot effectively work on its own. In order to meet the circulatory demand of the patient, developing an appropriate pump control mechanism to adjust the blood flow through the pump by controlling the pump speed (PS) is an important challenge facing the increased use of such devices [1]- [3]. An important constraint that should be taken into consideration is to ensure that the pump is rotated at a speed below a value beyond which the pump will attempt to draw more blood from the left ventricle than available causing an event called ventricular suction.…”

Section: Introductionmentioning

confidence: 99%

A Suction Detection System for Rotary Blood Pumps Based on the Lagrangian Support Vector Machine Algorithm

Wang

Simaan

2013

IEEE J. Biomed. Health Inform.

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The Left Ventricular Assist Device (LVAD) is a rotary mechanical pump that is implanted in patients with congestive heart failure to help the left ventricle in pumping blood in the circulatory system. However, using such a device may result in a very dangerous event, called ventricular suction that can cause ventricular collapse due to overpumping of blood from the left ventricle when the rotational speed of the pump is high. Therefore, a reliable technique for detecting ventricular suction is crucial. This paper presents a new suction detection system that can precisely classify pump flow patterns, based on a Lagrangian Support Vector Machine (LSVM) model that combines six suction indices extracted from the pump flow signal to make a decision about whether the pump is in suction, approaching suction, or not in suction. The proposed method has been tested using in vivo experimental data based on two different pumps. The simulation results show that the system can produce superior performance in terms of classification accuracy, stability, learning speed, and good robustness compared to three other existing suction detection methods and the original SVM-based algorithm. The ability of the proposed algorithm to detect suction provides a reliable platform for the development of a feedback control system to control the speed of the pump while at the same time ensuring that suction is avoided.

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Feedback control of a rotary left ventricular assist device supporting a failing cardiovascular system

Cited by 9 publications

References 12 publications

Stochastic Modeling and Dynamic Analysis of the Cardiovascular System with Rotary Left Ventricular Assist Devices

Stochastic Modeling and Dynamic Analysis of the Cardiovascular System with Rotary Left Ventricular Assist Devices

Detection of aortic valve dynamics in bridge-to-recovery feedback control of the Left Ventricular Assist Device

A Suction Detection System for Rotary Blood Pumps Based on the Lagrangian Support Vector Machine Algorithm

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