A Gaussian function model for simulation of complex environmental sensing

Chaudhry, Qasim Ali

doi:10.1186/s40294-015-0009-0

Cited by 6 publications

(6 citation statements)

References 4 publications

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“…The use of the double Hill function was motivated by myocyte recruitment during preload, which is fundamentally a cooperative process 28 and consequently, is modeled by a sigmoidal Hill function 29 . Both the Gaussian function and Boltzmann distribution not only gave sub-par results compared to the Hill model, but also did not model the myocyte recruitment mechanism: The Gaussian function is symmetric about a mean 23 , which is not correct for our model because contraction and relaxation are not symmetric processes [30][31][32][33][34][35][36][37][38][39] . The Boltzmann distribution is a probability distribution of physical states 25 , and hence does not capture the dynamic cooperativity of myocytes recruitment.…”

Section: Lumped Parameter Modelmentioning

confidence: 99%

A diagnostic, monitoring, and predictive tool for patients with complex valvular, vascular and ventricular diseases

Keshavarz‐Motamed

2020

Sci Rep

100

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Hemodynamics quantification is critically useful for accurate and early diagnosis, but we still lack proper diagnostic methods for many cardiovascular diseases. furthermore, as most interventions intend to recover the healthy condition, the ability to monitor and predict hemodynamics following interventions can have significant impacts on saving lives. Predictive methods are rare, enabling prediction of effects of interventions, allowing timely and personalized interventions and helping critical clinical decision making about life-threatening risks based on quantitative data. In this study, an innovative non-invasive imaged-based patient-specific diagnostic, monitoring and predictive tool (called C3VI-CMF) was developed, enabling quantifying (1) details of physiological flow and pressures through the heart and circulatory system; (2) heart function metrics. C3VI-CMF also predicts the breakdown of the effects of each disease constituents on the heart function. Presently, neither of these can be obtained noninvasively in patients and when invasive procedures are undertaken, the collected metrics cannot be by any means as complete as the ones C3VI-CMF provides. C3VI-CMF purposefully uses a limited number of noninvasive input parameters all of which can be measured using Doppler echocardiography and sphygmomanometer. Validation of C3VI-CMF, against cardiac catheterization in forty-nine patients with complex cardiovascular diseases, showed very good agreement with the measurements.prediction of effects of interventions, allowing timely and personalized interventions, and helping critical clinical decision making about life-threatening risks based on quantitative data.The heart resides in a sophisticated vascular network whose loads impose boundary conditions on the heart function 6,7,14-16 . Effective diagnosis and prediction hinge on quantifications of the global hemodynamics (heart workload) and of the local hemodynamics (detailed information of the dynamics of the circulatory system, e.g., flow and pressure) of the cardiovascular system as all are very important for long-term health of the heart 6,14,16 . However, there is no method to invasively or noninvasively quantify the heart workload (global hemodynamics) and to provide contribution breakdown of each component of the cardiovascular diseases. Moreover, current diagnostic methods are limited and cannot quantify detailed information of the flow dynamics of the circulatory system (local hemodynamics). Although all of these can provide valuable information about the patient's state of cardiac deterioration and heart recovery, currently, clinical decisions are chiefly made based on the anatomy alone. To augment anatomical information, cardiac catheterization is used as the clinical gold standard to evaluate pressure and flow through heart and circulatory system but it is invasive, expensive, high risk and therefore not practical for diagnosis in routine daily clinical practice or serial follow-up examinations 17 . Most importantly, cardiac catheterization only pro...

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Section: Lumped Parameter Modelmentioning

confidence: 99%

A diagnostic, monitoring, and predictive tool for patients with complex valvular, vascular and ventricular diseases

Keshavarz‐Motamed

2020

Sci Rep

100

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“…where, P LV (t), V(t), and V 0 are the LV time-varying pressure, time-varying volume, and unloaded volume, respectively (Keshavarz-Motamed et al, 2016). As explained by Keshavarz-Motamed (2020), to represent the normalized elastance function of the LV, we observed that among summation of Gaussian functions (Pironet et al, 2013;Chaudhry, 2015), Boltzmann Distribution (McDowell, 1999), double Hill function (Mynard et al, 2012;Broomé et al, 2013), and the latter provided the most physiologically accurate results (e.g., pressure, volume, and flow waveforms). The double Hill function which is a cooperative process (Moss et al, 2004), as physiologically expected from myocyte recruitment during preload and is modeled by a sigmoidal Hill function.…”

Section: Left Ventriclementioning

confidence: 93%

“…There have been attempts for quantifying hemodynamics and for fundamental understanding of cardiovascular mechanics using lumped parameter modeling (Segers et al, 2003;Geven et al, 2004;Tanné et al, 2008;Garcia et al, 2009Garcia et al, , 2005Keshavarz-Motamed et al, 2011, 2015Mynard et al, 2012;Broomé et al, 2013;de Canete et al, 2013;Revie et al, 2013;Benevento et al, 2015;Frolov et al, 2016;Mihalef et al, 2017;Duanmu et al, 2018;Li et al, 2018;Pant et al, 2018;Ben-Assa et al, 2019;Mao et al, 2019;Keshavarz-Motamed, 2020;Keshavarz-Motamed et al, 2020). All of these models [except (Keshavarz-Motamed et al, 2016;Keshavarz-Motamed, 2020)] cannot satisfy Requirements #1 and #2 above, although they were very important to provide fundamental understandings using idealized or hypothetical cases (Segers et al, 2003;Geven et al, 2004;Tanné et al, 2008;Garcia et al, 2009Garcia et al, , 2005Keshavarz-Motamed et al, 2011, 2015Mynard et al, 2012;Broomé et al, 2013;de Canete et al, 2013;Revie et al, 2013;Benevento et al, 2015;Frolov et al, 2016;Mihalef et al, 2017;Duanmu et al, 2018;…”

Section: Introductionmentioning

confidence: 99%

Effects of Choice of Medical Imaging Modalities on a Non-invasive Diagnostic and Monitoring Computational Framework for Patients With Complex Valvular, Vascular, and Ventricular Diseases Who Undergo Transcatheter Aortic Valve Replacement

Baiocchi

Barsoum

Khodaei

et al. 2021

Front. Bioeng. Biotechnol.

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Due to the high individual differences in the anatomy and pathophysiology of patients, planning individualized treatment requires patient-specific diagnosis. Indeed, hemodynamic quantification can be immensely valuable for accurate diagnosis, however, we still lack precise diagnostic methods for numerous cardiovascular diseases including complex (and mixed) valvular, vascular, and ventricular interactions (C3VI) which is a complicated situation made even more challenging in the face of other cardiovascular pathologies. Transcatheter aortic valve replacement (TAVR) is a new less invasive intervention and is a growing alternative for patients with aortic stenosis. In a recent paper, we developed a non-invasive and Doppler-based diagnostic and monitoring computational mechanics framework for C3VI, called C3VI-DE that uses input parameters measured reliably using Doppler echocardiography. In the present work, we have developed another computational-mechanics framework for C3VI (called C3VI-CT). C3VI-CT uses the same lumped-parameter model core as C3VI-DE but its input parameters are measured using computed tomography and a sphygmomanometer. Both frameworks can quantify: (1) global hemodynamics (metrics of cardiac function); (2) local hemodynamics (metrics of circulatory function). We compared accuracy of the results obtained using C3VI-DE and C3VI-CT against catheterization data (gold standard) using a C3VI dataset (N = 49) for patients with C3VI who undergo TAVR in both pre and post-TAVR with a high variability. Because of the dataset variability and the broad range of diseases that it covers, it enables determining which framework can yield the most accurate results. In contrast with C3VI-CT, C3VI-DE tracks both the cardiac and vascular status and is in great agreement with cardiac catheter data.

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“…A formal model of wireless sensor networks in combination with agent-based simulation model is presented in (Niazi and Hussain 2011a, b). As this work lacks mathematical modelling that is why it is extended in (Chaudhry 2015) using Gaussian function for sensing of emergent behaviour in CAS. Experiments have been carried out to deduce centrality metrics effects to validate the roles of nodes in complex networks ).…”

Section: Literature Review Of Cas Using Formal Methodsmentioning

confidence: 99%

Formal analysis of subnet-based failure recovery algorithm in wireless sensor and actor and network

Afzaal

Zafar

2016

Complex Adapt Syst Model

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Wireless sensor and actor networks (WSANs) have various applications in safety and mission critical systems. Sensors are used for sensing the information whereas actors for taking intelligent decisions. Developing and modeling algorithms for WSANs have raised several research issues which have captured attention of the research community. Maintaining inter-actor connectivity or failure recovery is a critical issue in WSANs because these are deployed in harsh and inhospitable environment which may result into physical damage to actors loosing inter-actor connectivity. In case of failure of inter-actor connectivity, the topology of the network may be affected that might be inefficient to recover. Therefore an efficient subnet-based failure recovery algorithm (SFRA) is proposed in this work. It is assumed the partitioning of WSAN into subnets which localizes the failure recovery procedure at subnet level achieving objective of efficiency. Moreover, algorithm is hybrid as it assumes pre-failure planning and post-failure recovery. The proposed model is presented as a graph-based model to represent static part of the network topology. The graph model is transformed into a formal model using Vienna development method-specification language (VDM-SL). The static model is described by defining formal specification of subnets, network topology, sensors, actors and gateways as composite objects. The state space of the WSANs is described in the form of functions and operations as dynamic part of the model. Invariants are defined over the data types in static model for ensuring safety criteria and pre/post conditions are defined in functions and operations for changing state space of the system. The proposed model is validated and verified using VDM-SL Toolbox.

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A Gaussian function model for simulation of complex environmental sensing

Cited by 6 publications

References 4 publications

A diagnostic, monitoring, and predictive tool for patients with complex valvular, vascular and ventricular diseases

A diagnostic, monitoring, and predictive tool for patients with complex valvular, vascular and ventricular diseases

Effects of Choice of Medical Imaging Modalities on a Non-invasive Diagnostic and Monitoring Computational Framework for Patients With Complex Valvular, Vascular, and Ventricular Diseases Who Undergo Transcatheter Aortic Valve Replacement

Formal analysis of subnet-based failure recovery algorithm in wireless sensor and actor and network

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