Physiologic Data-Driven Iterative Learning Control for Left Ventricular Assist Devices

Continuous flow ventricular assist devices (cfVADs) constitute a viable and increasingly used therapy for end-stage heart failure patients. However, they are still operating at a fixed-speed mode that precludes physiological cfVAD response and it is often related to adverse events of cfVAD therapy. To ameliorate this, various physiological controllers have been proposed, however, the majority of these controllers do not account for the lack of pulsatility in the cfVAD operation, which is supposed to be beneficial for the physiological function of the cardiovascular system. In this study, we present a physiological data-driven iterative learning controller (PDD-ILC) that accurately tracks predefined pump flow trajectories, aiming to achieve physiological, pulsatile, and treatment-driven response of cfVADs. The controller has been extensively tested in an in-silico environment under various physiological conditions, and compared with a physiologic pump flow proportional-integral-derivative controller (PF-PIDC) developed in this study as well as the constant speed (CS) control that is the current state of the art in clinical practice. Additionally, two treatment objectives were investigated to achieve pulsatility maximization and left ventricular stroke work (LVSW) minimization by implementing copulsation and counterpulsation pump modes, respectively. Under all experimental conditions, the PDD-ILC as well as the PF-PIDC demonstrated highly accurate tracking of the reference pump flow trajectories, outperforming existing model-based iterative learning control approaches. Additionally, the developed controllers achieved the predefined treatment objectives and resulted in improved hemodynamics and preload sensitivities compared to the CS support.

A Feasible Method to Control Left Ventricular Assist Devices for Heart Failure Patients: A Numerical Study

Installing and developing a sophisticated control system to optimize left ventricular assist device (LVAD) pump speed to meet changes in metabolic demand is essential for advancing LVAD technology. This paper aims to design and implement a physiological control method for LVAD pumps to provide optimal cardiac output. The method is designed to adjust the pump speed by regulating the pump flow based on a predefined set point (operating point). The Frank–Starling mechanism technique was adopted to control the set point within a safe operating zone (green square), and it mimics the physiological demand of the patient. This zone is predefined by preload control lines, which are known as preload lines. A proportional–integral (PI) controller was utilized to control the operating point within safe limits to prevent suction or overperfusion. In addition, a PI type 1 fuzzy logic controller was designed and implemented to drive the LVAD pump. To evaluate the design method, rest, moderate, and exercise scenarios of heart failure (HF) were simulated by varying the hemodynamic parameters in one cardiac cycle. This evaluation was conducted using a lumped parameter model of the cardiovascular system (CVS). The results demonstrated that the proposed control method efficiently drives an LVAD pump under accepted clinical conditions. In both scenarios, the left ventricle pressure recorded 112 mmHg for rest and 55 mmHg for exercise, and the systematic flow recorded 5.5 L/min for rest and 1.75 L/min for exercise.

An Intra-Cycle Optimal Control Framework for Ventricular Assist Devices Based on Atrioventricular Plane Displacement Modeling

A promising treatment for congestive heart failure is the implementation of a left ventricular assist device (LVAD) that works as a mechanical pump. Modern LVADs work with adjustable constant rotor speed and provide therefore continuous blood flow; however, recently undertaken efforts try to mimic pulsatile blood flow by oscillating the pump speed. This work proposes an algorithmic framework to construct and evaluate optimal pump speed policies with respect to generic objectives. We use a model that captures the atrioventricular plane displacement, which is a physiological indicator for heart failure. We employ mathematical optimization to adapt this model to patient specific data and to find optimal pump speed policies with respect to ventricular unloading and aortic valve opening. To this end, we reformulate the cardiovascular dynamics into a switched system and thereby reduce nonlinearities. We consider system switches that stem from varying the constant pump speed and that are state dependent such as valve opening or closing. As a proof of concept study, we personalize the model to a selected patient with respect to ventricular pressure. The model fitting results in a root-mean-square deviation of about 6 mmHg. The optimization that considers aortic valve opening and ventricular unloading results in speed modulation akin to counterpulsation. These in silico findings demonstrate the potential of personalized hemodynamical optimization for the LVAD therapy.

Control of ventricular unloading using an electrocardiogram-synchronized pulsatile ventricular assist device under high stroke ratios

Pulsatile ventricular assist devices (pVADs) yield a blood flow that imitates the pulsatile flow of the heart and, therefore, could diminish the adverse events related to the continuous flow provided by the ventricular assist devices that are commonly used. However, their intrinsic characteristics of larger size and higher weight set a burden to their implantation, that along with the frequent mechanical failures and thrombosis events, reduce the usage of pVADs in the clinical environment. In this study, we investigated the possibility to reduce the pump size by using high pump stroke ratios while maintaining the ability to control the hemodynamics of the cardiovascular system (CVS). In vitro and in vivo experiments were conducted with a custom pVAD implemented on a hybrid mock circulation system and in five sheep, respectively. The actuation of the pVAD was synchronized with the heartbeat. Variations of the pump stroke ratio, time delay between the pump stroke and the heart stroke, as well as duration of the pump systole in respect to the total cardiac cycle duration were used to evaluate the effects of various pump settings on the hemodynamics of the CVS. The results suggest that by varying the operating settings of the pVAD, a pulsatile flow that provides physiological hemodynamic parameters, as well as a control over the hemodynamic parameters, can be achieved. Additionally, by employing high pump stroke ratios, the size of the pVAD can be significantly reduced; however, at those high pump stroke ratios, the effect of the other pump parameters diminishes.

A Novel Multi-objective Physiological Control System for Rotary Left Ventricular Assist Devices

Various control and monitoring algorithms have been proposed to improve the left-ventricular assist device (LVAD) therapy by reducing the still-occurring adverse events. We developed a novel multi-objective physiological control system that relies on the pump inlet pressure (PIP). Signal-processing algorithms have been implemented to extract the required features from the PIP. These features then serve for meeting various objectives: pump flow adaptation to the perfusion requirements, aortic valve opening for a predefined time, augmentation of the aortic pulse pressure, and monitoring of the LV pre- and afterload conditions as well as the cardiac rhythm. Controllers were also implemented to ensure a safe operation and prevent LV suction, overload, and pump backflow. The performance of the control system was evaluated in vitro, under preload, afterload and contractility variations. The pump flow adapted in a physiological manner, following the preload changes, while the aortic pulse pressure yielded a threefold increase compared to a constant-speed operation. The status of the aortic valve was detected with an overall accuracy of 86% and was controlled as desired. The proposed system showed its potential for a safe physiological response to varying perfusion requirements that reduces the risk of myocardial atrophy and offers important hemodynamic indices for patient monitoring during LVAD therapy.

Benefits of object-oriented models and ModeliChart: modern tools and methods for the interdisciplinary research on smart biomedical technology

Computational models of biophysical systems generally constitute an essential component in the realization of smart biomedical technological applications. Typically, the development process of such models is characterized by a great extent of collaboration between different interdisciplinary parties. Furthermore, due to the fact that many underlying mechanisms and the necessary degree of abstraction of biophysical system models are unknown beforehand, the steps of the development process of the application are iteratively repeated when the model is refined. This paper presents some methods and tools to facilitate the development process. First, the principle of object-oriented (OO) modeling is presented and the advantages over classical signal-oriented modeling are emphasized. Second, our self-developed simulation tool ModeliChart is presented. ModeliChart was designed specifically for clinical users and allows independently performing

Sensorless cardiac phase detection for synchronized control of ventricular assist devices using nonlinear kernel regression model

Recently, driving methods for synchronizing ventricular assist devices (VADs) with heart rhythm of patients suffering from severe heart failure have been receiving attention. Most of the conventional methods require implanting a sensor for measurement of a signal, such as electrocardiogram, to achieve synchronization. In general, implanting sensors into the cardiovascular system of the patients is undesirable in clinical situations. The objective of this study was to extract the heartbeat component without any additional sensors, and to synchronize the rotational speed of the VAD with this component. Although signals from the VAD such as the consumption current and the rotational speed are affected by heartbeat, these raw signals cannot be utilized directly in the heartbeat synchronization control methods because they are changed by not only the effect of heartbeat but also the change in the rotational speed itself. In this study, a nonlinear kernel regression model was adopted to estimate the instantaneous rotational speed from the raw signals. The heartbeat component was extracted by computing the estimation error of the model with parameters determined by using the signals when there was no effect of heartbeat. Validations were conducted on a mock circulatory system, and the heartbeat component was extracted well by the proposed method. Also, heartbeat synchronization control was achieved without any additional sensors in the test environment.

A dynamic control algorithm based on physiological parameters and wearable interfaces for adaptive ventricular assist devices

In this work we present an innovative algorithm for the dynamic control of ventricular assist devices (VADs), based on the acquisition of continuous physiological and functional parameters such as heart rate, blood oxygenation, temperature, and patient movements. Such parameters are acquired by wearable devices (MagIC & Winpack) and sensors implanted close to the VAD. The aim of the proposed algorithm is to dynamically control the hydraulic power of the VAD as a function of the detected parameters, patient's activity and emotional status. In this way, the cardiac dynamics regulated by the proposed autoregulation control algorithm for sensorized VADs, thus providing new therapy approaches for heart failure.