Developments in control systems for rotary left ventricular assist devices for heart failure patients: a review

AlOmari, Abdul-Hakeem H.; Savkin, Andrey V.; Stevens, Michael C.; Mason, D. G.; Timms, Daniel; Salamonsen, Robert F.; Lovell, Nigel H.

doi:10.1088/0967-3334/34/1/r1

Cited by 104 publications

(63 citation statements)

References 144 publications

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“…As the current focus of development of the HumanLib is on the diseased cardiovascular system, a common VAD controller trial includes testing the behavior of a controller for different pathological conditions of the cardiovascular system. Another aspect of VAD research focuses on the question of suitable control objectives to allow safe and efficient treatment (for a comprehensive overview see [4], the objectives that have been considered in the preceding sections are hardly clinically applicable). Similarly, the setup allows to quickly compare different VAD controller strategies.…”

Section: Discussionmentioning

confidence: 99%

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

Gesenhues

Hein

Ketelhut

et al. 2017

Biomedical Engineering / Biomedizinische Technik

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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 in silico studies in real time including intuitive interaction with the model. Furthermore, ModeliChart is capable of interacting with hardware such as sensors and actuators. Finally, it is presented how optimal control methods in combination with OO models can be used to realize clinically motivated control applications. All methods presented are illustrated on an exemplary clinically oriented use case of the artificial perfusion of the systemic circulation.

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Section: Discussionmentioning

confidence: 99%

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

Gesenhues

Hein

Ketelhut

et al. 2017

Biomedical Engineering / Biomedizinische Technik

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“…However, in the majority of the above described approaches, a large number of features are employed. Also, suction detection algorithms are used only in LVAD controllers [62]. …”

Section: Related Workmentioning

confidence: 99%

A Decision Support System for the Treatment of Patients with Ventricular Assist Device Support

Karvounis¹,

Tsipouras²,

Tzallas³

et al. 2014

Methods Inf Med

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SummaryBackground: Heart failure (HF) is affecting millions of people every year and it is characterized by impaired ventricular performance, exercise intolerance and shortened life expectancy. Despite significant advancements in drug therapy, mortality of the disease remains excessively high, as heart transplant remains the gold standard treatment for end-stage HF when no contraindications subsist. Traditionally, implanted Ventricular Assist Devices (VADs) have been employed in order to provide circulatory support to patients who cannot survive the waiting time to transplantation, reducing the workload imposed on the heart. In many cases that process could recover its contractility performance.Objectives: The SensorART platform focuses on the management and remote treatment of patients suffering from HF. It provides an inter-operable, extendable and VAD-independent solution, which incorporates various hardware and software components in a holistic approach, in order to improve the quality of the patients’ treatment and the workflow of the specialists. This paper focuses on the description and analysis of Specialist’s Decision Support System (SDSS), an innovative component of the SensorART platform.Methods: The SDSS is a Web-based tool that assists specialists on designing the therapy plan for their patients before and after VAD implantation, analyzing patients’ data, extracting new knowledge, and making informative decisions.Results: SDSS offers support to medical and VAD experts through the different phases of VAD therapy, incorporating several tools covering all related fields; Statistics, Association Rules, Monitoring, Treatment, Weaning, Speed and Suction Detection.Conclusions: SDSS and its modules have been tested in a number of patients and the results are encouraging.

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“…For patients suffering from its end-stage manifestation, left ventricular assist devices (LVADs) can significantly improve survival rate and quality of life [1]. For the control of LVADs, several strategies exist [2], one promising approach being the control of the end diastolic pressure (EDP). EDP is defined as the end-diastolic (ED) value of the left ventricular pressure (LVP).…”

Section: Introductionmentioning

confidence: 99%

Estimation of End:Diastolic Pressure via Deconvolution

Antink

Rüschen

Leonhardt

et al. 2016

2016 Computing in Cardiology Conference (CinC)

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Left ventricular assist devices (LVADs) can significantly improve survival rate and quality of life for patients suffering from end-stage heart failure. Several promising strategies to control LVADs are being developed, some being focused on the end-diastolic pressure (EDP). For those, the problem of EDP estimation in real-time has to be solved. In this work, a deconvolution-based method to identify features in cardiac signals is presented. This method is applied to the estimation of the EDP from the left-ventricular pressure (LVP) signal and evaluated on animal trial data. In 11 trials with adult sheep, a myocardial infarction was induced and an LVAD was implanted. A total of 37.6 hours of LVP data was annotated by a medical expert. Compared to the annotations, a root mean square error of 11.6 ms / 4.1 mmHg was achieved using the proposed deconvolution method. IntroductionAccording to the American Heart Association, about 5.7 million Americans live with heart failure. For patients suffering from its end-stage manifestation, left ventricular assist devices (LVADs) can significantly improve survival rate and quality of life [1]. For the control of LVADs, several strategies exist [2], one promising approach being the control of the end diastolic pressure (EDP). EDP is defined as the end-diastolic (ED) value of the left ventricular pressure (LVP). Thus, to implement an EDP control strategy, its value has to be determined in real time.In [3], the estimation of the ED time point was approach by an analysis of the peak curvature of the LVP signal and evaluated in terms of temporal accuracy. Here, a deconvolution-based approach is presented and its accuracy is evaluated both in temporal as well as amplitudinal accuracy. In previous work we have demonstrated that deconvolution methods have a great potential in the processing of (multimodal) cardiac signals. It was shown in [4] that blind deconvolution can be used to estimate a virtual source signal and linear filter coefficients to analyze and represent measured multimodal signals (e.g. photoplethysmography and ballistocardiography). In [5] it was further shown that a desired, measured signal (e.g. reference ECG) can be approximated by filtering measured multichannel signals (e.g. capacitively coupled ECG) with estimated linear filters. In this work, we expand the concept of blind deconvolution towards the estimation of the EDP. In particular, a measured signal and a desired, virtual signal are used to estimated linear filter coefficients in a training phase. These filter coefficients can subsequently be used to efficiently locate ED time points via convolution and peak detection. Deconvolution AlgorithmThe aim of the proposed algorithm is to find a feature, namely the ED time point, in the LVP signal. Thus, the observed signal x = x(t), with t ∈ 0 . . . T − 1, is the LVP signal, see Figure 1, top graph, solid line. The desired signal y = y(t) is derived from the temporal location of the ED time points. In particular, a medical expert has identified all points i...

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Developments in control systems for rotary left ventricular assist devices for heart failure patients: a review

Cited by 104 publications

References 144 publications

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

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

A Decision Support System for the Treatment of Patients with Ventricular Assist Device Support

Estimation of End:Diastolic Pressure via Deconvolution

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