Abstract: In medical practice, the effectiveness of fighting cancer is not only determined by the composition of the used drug, but determined by the administration method as well. As a result, having drugs with a suitable action profile is just a promising beginning, but without appropriate delivery methods, the therapy still can be ineffective. Finding the optimal biologic dose is an empirical process in medical practice; however, using controllers, an automated optimal administration can be determined. In this paper, we evaluate the effectiveness of different drug delivery protocols; using in silico simulations (like bolus doses, low-dose metronomic regimen and continuous infusion therapy). In addition, we compare these results with discrete-time controller-based treatments containing state feedback, setpoint control, actual state observer and load estimation.

Keywords: antiangiogenic therapy; maximum tolerated dose; bolus dose; low-dose metronomic regimen; continuous infusion therapy; optimal biologic dose; discrete-time control; state feedback; setpoint control; actual state observer; load estimation

Abstract: A novel automated framework is proposed in this paper to address the significant but challenging task of multi-label brain tumor segmentation. Kernel sparse representation, which produces discriminative sparse codes to represent features in a high-dimensional feature space, is the key component of the proposed framework. The graph-cut method is integrated into the framework to make a segmentation decision based on both the kernel sparse representation and the topological information of brain structures. A splitting technique based on principal component analysis (PCA) is adopted as an initialization component for the dictionary learning procedure, which significantly reduces the processing time without sacrificing performance. The proposed framework is evaluated on the multi-label Brain Tumor Segmentation (BRATS) Benchmark. The evaluation results demonstrate that the proposed framework is able to achieve compatible performance and better generalization ability compared to the state-of-the-art approaches.

Keywords: Brain tumor segmentation, kernel methods, superpixels, PCA, sparse coding, dictionary learning, graph-cuts

Abstract: The current paper introduces a novel controller design approach dealing with the control of affine Linear Parameter Varying (LPV) systems using the abstract mathematical properties of the LPV parameter space and classical state-feedback design. By the designed controller structure the parameter dependent LPV system mimics a given selected reference LTI system reaching given performance specifications originally prescribed for the reference LTI system. Further, the actual feedback gains are calculated by comparison to the reference control gains - thus, realizing a ”relative control”. The method is demonstrated on given nonlinear biomedical problems with simulation results under MATLAB.

Keywords: LPV model; Affine LPV; qLPV; Physiological control

Abstract: Heart failure (HF) is a complex syndrome without an objective definition. It has become a serious problem in public health policies because of the increased prevalence, high cost of treatment, frequent re-hospitalization and high mortality. Neither strict standards for HF classification nor single-type treatments are currently available. The non-specific clinical symptoms make diagnosis at early stages difficult, leading to deterioration and hospitalization. The use of advanced medical techniques and newly developed medicines may decrease mortality, but many HF patients still have a low quality of life because of insufficient muscular endurance and limited activities. Recent reports have shown that exercise programs contribute to the recovery of cardiac functions and improve clinical results for most HF patients. However, excessive, intense exercise may increase the risk of death, particularly for cardiac-related patients. In this study, different HF types are categorized and a safe, customized mechanism for self-exercise training integrating Internet-of-Medical-Thing devices and cloud computing technologies is proposed. The detected biometric features of the HF patients are linked to the personal communication devices of the patients and doctors, a cloud server system and the hospital medical information system. The proposed system mainly collects heart rate and metabolic equivalent features in a real-time manner from the Internet-of-Medical-Thing devices worn by patients. Measured data are dynamically compared to customized maximum limitations that are defined by rehabilitation physicians according to the patient’s cardio-pulmonary exercise testing record in the hospital. A prototype system was successfully developed and validated with several test cases and showed excellent performance at an affordable cost. The proposed mechanism provides a customized platform for HF patients to pursue a better quality of life, based on prognostic exercise prescription using a safe self-exercise training mechanism.

Keywords: chronic heart failure; ejection fraction; heart rate; metabolic equivalent; Internet-of-Medical-Thing

Abstract: Artificial Pancreas (AP) is an expression referred to a set of techniques for the closed-loop control of the plasma glucose concentration by means of exogenous insulin administration in diabetic patients. Diabetes comprises a group of metabolic disorders characterized by high blood sugar levels over a prolonged period, due to pancreas failure to produce enough insulin and/or insulin resistance, so that higher amounts of insulin are usually required in order to keep glycemia in a safe range. In this work, we face the problem of glucose control for a class of Type-2 diabetic patients, in the presence of sampled glucose measurements and without any information about the time course of insulinemia. A compact physiological model of the glucose-insulin system is reviewed, then an observer (based on this model) is designed to estimate the insulin trajectory from the glucose samples. Finally, a feedback control law (based on the reconstructed state) is designed to deliver exogenous intra-venous insulin to each individual. Simulations have been performed in-silico on models of virtual patients, whose parameters are tuned according to real data, and aim at validating the method in the presence of parameter variations and quantization errors.

Keywords: Diabetes, Artificial Pancreas, Glucose Control, Observers, Feedback Systems

Abstract: Infectious Hospital Agents (IHA) is an individual-based simulation framework that is able to model wide range of infection spreading scenarios in the hospital environment. The simulations are agent-based simulations driven by stochastic events, the evolution of the model is tracked in discrete time. Our aim was to build a general, customisable and extensible simulation environment for the domain of Hospital-Associated Infections (HAIs). The system is designed in Object Oriented fashion, and the implementation is in C++. In this paper, the authors describe the motivations and the background of the framework, sketch the conceptual framework, and present a demonstration example.

Keywords: Healthcare-Associated Infections; Hospital simulation; Agent-based simulation

Abstract: A data model is presented in the form of ontology which includes the indoor location description of hospitals, the indoor navigation features and the accessibility attributes for people with motion disabilities. The possible use of the ontology is demonstrated by outlining some RDF data excerpt, OWL definitions and SPARQL queries for the navigation features of future applications.

Keywords: Linked Open Data; ontology; indoor navigation; medical facility; accessibility

Abstract: In order to develop an efficient and user-friendly supervisory system for robotassisted radio-frequency ablation of liver tumors, we proposed and developed a new cognitive engine. This novel framework, based on a hybrid architecture. This novel system can generate and supervise entire surgical procedures, which are readable for both operators and computers, by applying semantic methods. The entire prototype is constructed by ontology and operated by SPARQL query language in JAVA. According to ex-vivo phantom experiments, the cognitive engine provides surgical execution procedures correctly for the radio-frequency ablation surgical system. The proposed cognitive engine can be modified for many other robot-assisted applications.

Keywords: cognitive engine; radio-frequency ablation; needle insertion; surgical robots

Abstract: The closed-loop inverse kinematics algorithm is a numerical approximation of the solution of the inverse kinematics problem, which is a central problem of robotics. The accuracy of this approximation, i.e. the convergence of the numerical solution to the real solution can be increased by increasing the value of a feedback gain parameter. However, this can lead to unstable operation if the stability margin is reached. The accuracy of the closedloop inverse kinematics algorithm is increased here by replacing the numerical integration with second-order and implicit numerical integration techniques. The application of implicit Euler, explicit trapezoid, implicit trapezoid and the weighted average method is considered, and an iteration is presented to calculate the implicit solutions. Simulation results show that implicit second-order methods give the best results. However, they decrease the stability margin due to the iteration required to calculate the implicit solution. The stability margin of the algorithms with different numerical integration techniques is analyzed, and it turns out that the implicit trapezoid method has the most desirable properties.

Keywords: differential inverse kinematics; numerical integration; explicit Euler; implicit Euler; explicit trapezoid; implicit trapezoid; theta method; weighted average method

Abstract: An important step in any control system design is to account for the fault tolerance desired for the system at an early stage of development. It is not enough to test the fault tolerance after the implementation, as the tuning possibilities may be insufficient to ensure tolerance for an unexpected fault, it is better to monitor at the design phase. The control research community is interested in fault tolerant control system design, but only specific applications are addressed. The present paper deals with such a fault tolerant control system for a complex chemical process, the (13C) isotope separation columns cascade. To ensure the robustness to uncertainties of the designed system, the controller is a fractional order type, tuned using the particle swarm optimization method. The simulation results were obtained using the TrueTime Matlab toolbox.

Keywords: fault tolerant systems; distributed control system; fractional order controllers; robust control system

Abstract: We consider a fractional-order model of two asymmetrically coupled spiking neurons. The dynamical behavior of the two neurons is modeled by the fractional-order Hodgkin-Huxley equations. Simulations of the model for distinct values of the order of the fractional derivative, α, and of the coupling constants, κ1, κ2, show interesting features, such as relaxation oscillations, mixed-mode oscillations, small oscillations, and localized solutions. Moreover, α adds extra complexity to the dynamics of the model. These differences may explain certain differences in processing similar tasks in the human brain.

Keywords: asymmetric; coupled; neurons; fractional Hodgkin-Huxley equations

Abstract: Vibrations in airplane wings have a negative impact on the quality and safety of a flight. For this reason, active vibration suppression techniques are of extreme importance. In this paper, a smart beam is used as a simulator for the airplane wings and a fractional order PD controller is designed for active vibration mitigation. To implement the ideal fractional order controller on the smart beam unit, its digital approximation is required. In this paper, a new continuous-to-discrete-time operator is used to obtain the discrete-time approximation of the ideal fractional order PD controller. The efficiency and flexibility, as well as some guidelines for using this new operator, are given. The numerical examples show that high accuracy of approximation is obtained and that the proposed method can be considered as a suitable solution for obtaining the digital approximation of fractional order controllers. The experimental results demonstrate that the designed controller can significantly improve the vibration suppression in smart beams.

Keywords: fractional order controller; novel indirect discretization method; smart beam; vibration attenuation; experimental results

Abstract: This paper uses tools from fractional calculus such as Cole-Cole and fractional order impedance models for estimation of glucose concentration. The measured impedance is compared with two fractional order models and the simulation results show that Cole-Cole model has limitation and cannot capture the dynamics of the simulated environment. On the other hand, the fractional order model can follow the changes in impedance for several study cases. Model parameters are correlated with various conditions of the test environment. The results of these study cases show that the fractional order model is a suitable candidate for this particular application. The hypothesis tested in this paper provides new tools for glucose concentration monitoring and measurement.

Keywords: Fractional Order Impedance Models; Diabetes; Modeling; Cole-Cole Model

Abstract: Angiogenesis inhibitors offer a promising new treatment modality in oncology. However, the optimal administration regimen is often not well-established, despite the fact that it might have substantial impact on the outcome. The aim of the present study was to investigate this issue. Eight weeks old male C57Bl/6 mice were implanted with C38 colon adenocarcinoma, and were given either daily (n = 9) or single (n = 5) dose of bevacizumab. Outcome was measured by tracking tumor volume; both caliper and magnetic resonance imaging was employed. Longitudinal growth curves were modelled with mixed-effects models (with correction for autocorrelation and heteroscedasticity, where necessary) to infer on population-level. Several different growth models (exponential, logistic, Gompertz) were applied and compared. Results show that the estimation of the exponential model is very reliable, but it prevents extrapolation in time. Nevertheless, it clearly established the advantage of the continuous regime.

Keywords: mixed effects models; tumor growth; angiogenesis inhibition; dosing regimen