Keywords: Biological systems and controls, Decision support systems for the control of physiological and clinical variables, Healthcare management and delivery, disease control, critical care
Abstract: Recruitment maneuvers (RMs) following with positive-end-expiratory-pressure (PEEP) have proved effective in recruiting lung volume and preventing alveoli collapse. To date, standards for optimal patient-specific PEEP are unknown, resulting in variability in care and reduced outcomes, both indicating the need for personalized care. This research extends a well-validated virtual patient model by adding novel elements to model, which is able to utilize bedside available respiratory data, without increasing modelling complexity, to predict patient-specific lung distension and thus to minimise barotrauma risk. Prediction accuracy and robustness are validated against clinical data from 18 volume controlled ventilation (VCV) patients at 7 different baseline PEEP levels (0 to 12cmH2O), where predictions were made up to 12cmH2O of PEEP ahead. Using an exponential basis function set for prediction yields an absolute median peak inspiratory pressure prediction error of 1.50cmH2O for 623 prediction cases. Comparing predicted and clinically measured distension prediction in VCV demonstrated consistent, robust high accuracy with R2=0.90 (623 predictions), which is a measurable improvement in prediction error compared to predictions without using the proposed distension function (R2=0.82). Moreover, the R2 value increases to 0.93-0.95 if only clinically relevant ΔPEEP steps (2-6cmH2O) are considered with an overall median absolute error in peak pressure prediction of 1.04cmH2O. Overall, the results demonstrate the potential and significance for accurately capturing distension mechanics, allowing better risk assessment, as well as extending and more fully validating this virtual mechanical ventilation patient model.

Keywords: Biological systems and controls, Identification and validation, Decision support systems for the control of physiological and clinical variables
Abstract: Positive-end-expiratory-pressure (PEEP) have proved effective in recruiting lung volume and keeping alveoli open. However, there is no standard means to find an optimal patient-specific PEEP, creating variability in care and outcomes. There is thus a need for personalized approaches to find the best PEEP and optimise care. This research extends a well-validated virtual patient model with a newly proposed function to predict lung distension, while the impact on outcome of two different elastance identification strategies are discussed and compared. A prior studied and effective exponential basis function set is used as the general model, while elastance are identified using overlapped and separate methods, respectively. In this approach, model with overlapped elastance identification and proposed distension function yields an absolute median peak inspiratory pressure (PIP) prediction error of 1.50cmH2O for 623 prediction cases. Comparison between clinically measurement and model prediction for PIP yields R2=0.90 across 623 predictions in total, while R2=0.87 with separate elastance identification. Furthermore, both elastance identification methods are an improvement compared to predictions without proposed distension function (R2=0.82). Validation is fulfilled with 18 volume controlled ventilation patients respiratory data at 7 different baseline PEEP levels (0-12cmH2O) with a maximal PEEP prediction interval of 12cmH2O. Overall, the results demonstrate the impact of elastance identification methods, as well as the potential and significance for accurately capturing distension mechanics, which thus providing guidance for clinical care and insights for predictive lung mechanics modelling.

Keywords: Biomedical system modelling, Quantification of physiological parameters for diagnosis assessment, Model formulation
Abstract: Model-based approaches are often used to estimate mechanical properties of lungs, such as elastance (E) and airway resistance (R), during invasive and non-invasive mechanical ventilation (MV). Current models are less effective when spontaneous breathing is present. This analysis utilises b-spline functions within a single compartment model framework to identify patient-specific inspiratory driving pressure. A series of 2nd-order, constrained b-spline basis functions are used to identify inspiratory driving pressure breath to breath alongside single E and R using inspiration and expiration data from n=20 breaths for 10 patients ventilated using NAVA. Median [IQR] per patient RMS error for n = 20 breaths was 0.75 [0.6 – 0.9] cmH2O, with elastance ranging from 2.1 – 29.8 cmH20/L, and per-patient median peak driving pressure ranging from -1.9 to -7.9 cmH2O. Inspiratory driving pressure profiles matched esophageal pressures from literature and its value at peak nervous signal to the diaphragm (Eadi) was correlated with peak Eadi (R2=0.25-0.86). Average trans-pulmonary pressure remained consistent between breaths for each patient, despite differences in peak Eadi and peak airway pressure. Overall, the model-based approach resulted in physiologically reasonable inspiratory driving pressures, with trends with electrical activity and matched literature data showing neuro-muscular decoupling as a function of pressure and/or volume.

Keywords: Biomedical system modelling, Quantification of physiological parameters for diagnosis assessment, Model formulation
Abstract: Models of lung elastance, airway resistance, and patient work of breathing have been successfully applied to invasive mechanical ventilation data. Non-invasive mechanical ventilation data, including continuous positive airway pressure (CPAP), has presented challenges in predicting inspiratory driving pressure due to the combination of patient and device work. The model applied in this paper utilizes second order b-splines to describe inspiratory driving pressure. The model provided an accurate fit to the data, with an average root-mean-squared (RMS) error in model fit of 0.6 [0.425 – 0.675] cmH2O (median [lower quartile (LQ), upper quartile (UQ)]). Subject fit expiratory elastances were between 3.1 and 10.2 cmH2O/L and showed no correlation to set positive end-expiratory pressure (PEEP). Inspiratory driving pressure profiles approximated literature and work of breathing was shown to remain consistent between PEEP levels. Outlying data is hypothesized to be caused by subjects’ expiratory effort which was assumed negligible in the model. Further application of this model alongside validation data would provide more information on this and provide more evidence of model accuracy.

Keywords: Decision support systems and feedback control, Decision support systems for the control of physiological and clinical variables
Abstract: Mechanical ventilation (MV) is a complex support tool for respiratory failure patients. However, MV is easily mismanaged, and the common practice today relies on clinician’s experience and intuition. Due to this subjectivity, along with the complex task of managing multiple interdependent MV settings, setting patient-specific optimal MV is a difficult task. This research proposes a model-based method to manage the wide range of possible MV settings while taking patient-specific conditions into consideration. This method makes use of a “VENT” protocol to aid clinicians’ decision makings. The model-based method is integrated recommendations based on landmark studies and established guidelines to guide MV settings. Forward simulation results show acceptable results when recreating patient breath waveform from retrospective data. Protocol validation with retrospective patient data shows that actual clinically implemented settings are among the protocol recommendations.

Keywords: Decision support systems and feedback control, Simulation and visualization,, Biosignal analysis and interpretation,
Abstract: Surges of COVID-19 infections could lead to insufficient supply of mechanical ventilators, and rationing of needed care. Multiplexing mechanical ventilators (co-MV) to serve multiple patients is a potential temporary solution. However, if patients are ventilated in parallel ventilation, there is currently no means to match ventilation requirements or patients, with no guidelines to date for co-MV. This research uses patient-specific clinically validated respiratory mechanics models to propose a method for patient matching and mechanical ventilator settings for two-patient co-MV under pressure control mode. The proposed method can simulate and estimate the resultant tidal volume of different combinations of co-ventilated patients. With both patients fulfilling the specified constraint under similar ventilation settings, the actual mechanical ventilator settings for co-MV are determined. This method allows clinicians to analyze in silico co-MV before clinical implementation.

Keywords: Biosignal analysis and interpretation,, Biological systems and controls, Healthcare management and delivery, disease control, critical care
Abstract: Mechanical ventilation (MV) is used in the intensive care unit (ICU) to treat patients with respiratory failure. However, MV settings are not standardized due to significant inter- and intra- patient variability in response to care, leading to variability in care, outcome, and cost. There is thus a need to personalize MV. This research extends a single compartment lung mechanics model with physiologically relevant basis functions, to identify patient-specific lung mechanics and predict response to changes in MV care. The nonlinear evolution of pulmonary elastance as positive-end-expiratory pressure (PEEP) changes is captured by a physiologically relevant, simplified compensatory equation as a function of PEEP and pressure identification error at the baseline PEEP level. It allows both patient-specific and general prediction of lung elastance of higher PEEP. The prediction outcome is validated with data from two volume-controlled ventilation (VCV) trials and one pressure-controlled ventilation (PCV) trial, where the biggest PEEP prediction interval is a clinically unrealistic 20cmH2O, comprising 210 prediction cases over 36 patients (22 VCV; 14 PCV). Predicted absolute peak inspiratory pressure (PIP) errors are within 1.0cmH2O and 3.3cmH2O for 90% cases in the two VCV trials, while predicted peak inspiratory tidal volume (PIV) errors are within 0.073L for 85% cases in studied PCV trial. The model presented provides a highly accurate, predictive virtual patient model across multiple MV modes and delivery methods, and over clinically unrealistically large changes. Low computational cost, and fast, easy parameterization enable model-based, predictive decision support in real-time to safely personalize and optimize MV care.

Keywords: Model formulation, Identification and validation, Biological systems and controls
Abstract: This paper studies the trending behavior of the COVID-19 dynamics in Israeli cities. The model employed is used to describe, for each city, the accumulated number of cases, the number of cases per day, and the predicted final number of cases. The innovative analysis adopted here is based on the daily evolution of the predicted final number of infections, estimated with data available until a given date. The results discussed here are illustrative for six cities in Israel, including Jerusalem and Tel Aviv. They show that the model employed fits well with the observed data and is able to suitably describe the COVID-19 dynamics in a country strongly impacted by the disease that holds one of the most successful vaccination programs in the world.

Keywords: Biological systems and controls, Simulation and visualization,, Biomedical system modelling
Abstract: The COVID-19 pandemic is the defying crisis of our time. Since mass vaccination has not yet been established, countries still have been facing many issues due to the viral spread. Even in cities with high seroprevalence, intense resurgent waves of COVID-19 have been registered, possibly due to viral variants with greater transmission rates. Accordingly, we develop a new Model Predictive Control (MPC) framework that is able to determine social distancing guidelines and altogether provide estimates for the future epidemiological characteristic of the contagion. For such, the viral dynamics are represented through a Linear Parameter Varying (LPV) version of the Susceptible-Infected-Recovered-Deceased (SIRD) model. The solution of the LPV MPC problem is based on a Sequential Quadratic Program (SQP). This SQP provides convergent estimates of the future LPV scheduling parameters. We use real data to illustrate the efficiency of the proposed method to mitigate this contagion while vaccination is ongoing.

Keywords: Healthcare management and delivery, disease control, critical care, Biological systems and controls, Artificial intelligence for decision support systems
Abstract: In this work, the application of a model-free extremum seeking strategy is investigated to achieve the hypothetical control of the covid-19 pandemics by acting on social distancing. The advantage of this procedure is that it does not rely on the accurate knowledge of an epidemiological model and takes realistic constraints into account, such as hospital capacities. The simulation study reveals that the convergence has two time scales, with a fast catch of the transient optimum of the measurable cost function, followed by a slow tracking of this optimum following the original SIR dynamics. Several issues are discussed such as quantization of the sanitary measures.

Keywords: Model formulation, Simulation and visualization,, Biological systems and controls
Abstract: In order to analyze the effect of vaccination in a population with the presence of viruses, a variation of the SIR (Susceptible-Infected-Removed) model is proposed taking into account social distancing and the effect of the vaccine. The equilibrium points of the proposed model are calculated and the stability analysis of the system is carried out. For the proposed model, disease-free equilibrium point and endemic equilibrium point are found and the conditions of existence are discussed. For the disease-free point the bifurcation conditions are derived and simulations show that reducing the vaccination effort can lead the disease-free equilibrium to the endemic equilibrium. From the theoretical analysis, a minimum value of effort is obtained to guarantee a disease-free equilibrium point. Simulations were carried out from the value obtained from Rv to validate the theoretical results.

Keywords: Biomedical system modelling, Simulation and visualization,
Abstract: In this paper we explore the effect of the number of daily tests on an epidemics control policy purely based on testing and selective quarantine, and the impact of these actions depending on the time their application starts. We introduce a general model incorporating a stochastic disease evolution, a particular weighted graph representing the population, and an optimal contact tracing strategy to allocate available tests. Simulations on a community of 50'000 individuals show that the evolution of the epidemic produces a clear non-linear response to the variation of the number of tests used and to the starting time of their application. These results suggest that not only a minimum number of tests is necessary to obtain a positive outcome from the tracing strategy but also that there exists a saturation on the contribution of additional tests. The results also show that the timing in the application of the measures is as important as the measures themselves and that an excessive delay can be hardly overcome.

Keywords: Decision support systems and feedback control, Biological systems and controls, Biomedical system modelling
Abstract: This paper considers the susceptible-infected-susceptible (SIS) epidemic model with an underlying network structure and focuses on the effect of social distancing to regulate the epidemic level. We demonstrate that if each subpopulation is informed of its infection rate and reduces interactions accordingly, the fraction of the subpopulation infected stays below half for all time instants. To this end, we first modify the basic SIS model by introducing a state dependent parameter representing the frequency of interactions between subpopulations. Thereafter, we show that for this modified SIS model, the spectral radius of a suitably-defined matrix being not greater than one causes all the agents, regardless of their initial sickness levels, to converge to the healthy state; assuming non-trivial disease spread, the spectral radius being greater than one leads to the existence of a unique endemic equilibrium, which is also asymptotically stable. Finally, by leveraging the aforementioned results, we show that the fraction of (sub)populations infected never exceeds half.

Keywords: Biomedical system modelling, Biological systems and controls, Model formulation
Abstract: In open-heart surgery, temperature changes may severely damage organ tissues, therefore a better knowledge of thermal transient effects is required to improve temperature control. Heat transfer in a biological context is usually treated by means of empirical relationships or simple resistance models. In some cases, fairly simplified models only involving thermal resistance R and a heat capacity C are used. More advanced models which may be accurate enough tend to rely on heavy finite element computations. An intermediate model is sought to more accurately describe transient effects. By combining the well-known Pennes' bio-heat equation combined to a thermal two-port network, a circuit model is proposed to take into account thermal transients in a perfused tissue. A larger frequency band can be taken into account for such an approach. The proposed models may also consider the effect of blood perfusion on the temperature transients, as well as blood temperature fluctuations (as in extracorporeal circulation, for example) and metabolic heat generation. For a simple 1D scenario, sensitivities to different heat or temperature inputs are compared. The obtained impedance expressions are also analyzed for different branch levels of the lung.

Keywords: Model formulation, Identification and validation, Healthcare management and delivery, disease control, critical care
Abstract: Spontaneous breathing effort of a mechanically ventilated patient can seriously deteriorate the treatment outcome if it is not taken into account when choosing the appropriate settings. This paper presents an improved spontaneous breathing effort model that can be used to develop improved mechanical ventilation algorithms. Through an indicative experimental study it is shown that this model accurately describes the spontaneous breathing effort of a healthy human test subject. In comparison to the commonly used sinusoidal halfwave effort model it is shown that the proposed model improved fitting quality by a factor two.

Keywords: Biomedical system modelling, Identification and validation, Intensive and chronic therapy
Abstract: This research aims to improve mechanical ventilation therapy in the neonatal intensive care unit (NICU). Mechanical ventilation (MV) settings in this vulnerable cohort are currently clinically determined based on experience, estimation and patient response. Modelling the lung mechanics of each specific patient may aid as a setting guide for clinicians, and provide a deeper indication of patient status. This study presents a novel method for estimating the maximum remaining recruitable lung volume, 𝑉𝑚, of a neonate. Current methods for determining patient lung volume are invasive, costly and disruptive to care, so are not often performed. The method proposed is non-invasive and uses data readily available through bedside monitoring. An optimal 𝑉𝑚 value was determined for each patient. When compared to patient mass, a strong linear relationship was determined. The variability of results reflects the inter-patient variability amongst this cohort and reinforces the need for patient-specific treatment solutions utilising novel, non-invasive metrics to provide better, more personalised care

Keywords: Identification and validation, Biomedical system modelling, Healthcare management and delivery, disease control, critical care
Abstract: Patient-specific lung-mechanics during mechanical ventilation (MV) can be modelled via using fully ventilated/controlled waveforms. However, patient asynchrony due to spontaneous breathing (SB) effort commonly exists in patients on full MV support, leading to variability in breathing waveforms and reducing the accuracy of identified, model-based, and patient-specific lung mechanics. This study aims to extract ventilated breathing waveforms from affected asynchronous breathing cycles using an automated virtual patient model-based approach. In particular, change of lung elastance over a pressure-volume (PV) loop is identified using hysteresis loop analysis (HLA) to detect the occurrence of asynchrony, as well as its type and pattern. The identified HLA parameters are then combined with a nonlinear mechanics hysteresis loop model (HLM) to extract and replicate the ventilated waveforms from the coupled asynchronous breaths. The magnitude of asynchrony can then be quantified using an energy dissipation metric, Easyn, comparing the area difference of PV loops between model-reconstructed and original breathing cycles. A proof-of-concept study is conducted using clinical data from 2700 breathing cycles of two patients exhibiting asynchrony during MV. The reconstruction root mean square errors are within 5-10% of the clinical data for 90% of the cycles , indicating good and robust reconstruction accuracy. Estimation of Easyn shows significant asynchrony magnitude for Patient 1 with Easyn greater than 10% for over 50% breaths, while asynchrony occurrence for Patient 2 is lower with 90% breaths at Easyn < 10%, which is a minimal asynchrony magnitude. These results match direct observation, thus validating the ability of the virtual patient model and methods presented to be used for a real-time monitoring of asynchrony with different types and magnitudes, which in turn would justify prospective clinical tests.

Keywords: Quantification of physiological parameters for diagnosis assessment, Artificial intelligence support in diagnosis and decision making systems
Abstract: Facemasks have been widely used in hospitals, especially since the emergence of the coronavirus 2019 (COVID-19) pandemic, often severely affecting respiratory functions. Masks protect patients from contagious airborne transmission, and are thus more specifically important for chronic respiratory disease (CRD) patients. However, masks also increase air resistance and thus work of breathing, which may impact pulmonary auscultation and diagnostic acuity, the primary respiratory examination. This study is the first to assess the impact of facemasks on clinical auscultation diagnostic. Lung sounds from 29 patients were digitally recorded using an electronic stethoscope. For each patient, one recording was taken wearing a surgical mask and one without. Recorded signal were segmented in breath cycles using an autocorrelation algorithm. In total, 87 breath cycle were identified from sounds with mask, and 82 without mask. Time-frequency analysis of the signals was used to extract comparison features such as peak frequency, median frequency, band power, or spectral integration. All the features extracted in frequency content, its evolution, or power did not significantly differ between respiratory cycles with or without mask. This early stage study thus suggests minor impact on clinical diagnostic outcomes in pulmonary auscultation. However, further analysis is necessary such as on adventitious sounds characteristics differences with or without mask, to determine if facemask could lead to no discernible diagnostic outcome in clinical practice.

Keywords: Identification and validation, Healthcare management and delivery, disease control, critical care, Control of voluntary movements, respiration, locomotion
Abstract: The estimation of lung mechanics' parameters and the patient's residual volitional breathing effort is a prerequisite to adjust the parameters of assisted ventilation in a patient-individual manner. A real-time capable approach is investigated that estimates the resistance and compliance of a first-order lung model in conjunction with the intrapleural pressure in real-time. Latter is a measure for the patient's breathing effort. A signal generator model in the form of a Radial Basis Function (RBF) network is assumed for the intrapleural pressure. The Gaussian basis functions are periodic with the breathing cycle duration. This approach does not restrict the signal form of the patient-driven pressure curve. Recursive Least Squares (RLS) with selective forgetting is employed to consider the different dynamics of the estimated model parameters. A time-discrete version of the lung model is used for RLS. Computer simulations reveal that the approach is feasible and that selective forgetting is necessary to obtain satisfactory estimation results.

Keywords: Biosignal analysis and interpretation,, Biomedical system modelling, Artificial intelligence for decision support systems
Abstract: Electrical impedance spectroscopy (EIS) has been used as adjunct to colposcopy for cervical cancer diagnosis for many years. The study presented in this paper was a longitudinal EIS data analysis where women with a negative colposcopy were followed up to three years and their initial EIS readings were analysed to see if it was possible to predict the women who subsequently developed cervical cancer. A data-driven modelling approach was proposed to extract features from EIS readings and cross validation techniques was then used to chosen the best classification model constructed from the selected features to separate the group of women who developed cervical cancer from those who did not within the follow-up years. The developed method was applied to analyse a real EIS data set and the results showed that EIS does offer prognostic information on the risk of cervical cancer development over three follow-up years. The method developed is of long-term benefit for EIS–based cervical cancer diagnosis and, in conjunction with standard colposcopy, there is potential with the developed method to provide more effective and efficient patient management strategy for clinic practice

Keywords: Pharmaceutical product on demand, design, manufacturing and quality assessment, Pharmacokinetics and drug delivery, Biomedical system modelling
Abstract: Worldwide, cancer is the second cause of death after heart diseases, being accountable for 10 million deaths per year. This study addresses adenocarcinoma, the main subject to multiple anticancer treatments, that are currently developing in medicine and pharmacy study trials. A new research for a therapeutic vaccine involves the study of gold nanoparticles impacting the immune response for annihilating cancer cells. The model is proposed to be implemented using Quantitative-Structure Activity Relationship (QSAR) techniques, specifically artificial neural networks in relation with fuzzy rules to combine the benefits of human perception with the automatic characteristic of neural nets. The inputs to the resulted ANFIS model are molecular features that must be selected for optimization, using antlion optimization algorithm, inspired recently from natural behaviors, the same way once ANNs were developed. A couple of molecular features are extracted and computed from hyperspectral images through image processing approaches like morphological transformations and watershed segmentation.

Keywords: Pharmacokinetics and drug delivery, Decision support systems and feedback control, Simulation and visualization,
Abstract: A Moving Horizon Estimator (MHE) based Nonlinear Model Predictive Controller (NMPC) was designed for an impulsive minimal tumor growth model. The estimator computes the time-varying model parameters using mean square error with parameter deviation penalization and provides state estimations for the controller. The controller computes optimal doses for non-equidistant, fixed time instants while constraining the administered drug dose. Tuning of the MHE was based on experimental time series measurements, while for the NMPC a virtual population was generated. The robustness of the combined approach was tested in silico on a virtual population, where the simulation was tailored to a real experimental scenario.

Keywords: Pharmacokinetics and drug delivery, Biological systems and controls, Healthcare management and delivery, disease control, critical care
Abstract: Therapeutic optimization is a promising direction of computer aided medicine. Optimization of chemotherapy based on mathematical models can result in lower doses, fewer side effects, a smaller chance of acquired drug resistance and more efficient personification. We explore model-based chemotherapy optimization for high frequency low dose therapies with impulsive inputs. We keep the drug level over a specified value using the minimal value of injection doses. We generate therapy for population mean parameters acquired from identification based on mice experiments. We carry out in silico trials based on the results of the individual fits from the identification process and test the therapy generated for the population mean parameters. The results show that therapy optimization based on population mean parameters can be used to generate therapy for the individuals and results in a solution close to the optimal one without using specific knowledge about the individual.

Keywords: Biological systems and controls, Biomedical system modelling, Pharmacokinetics and drug delivery
Abstract: In this paper, we investigate strategies for administering chemo- and immunotherapy to force a tumor-immune system to its healthy equilibrium. To solve this problem, we use Pontryagin’s Maximum Principle applied to a modified Stepanova model. This model directly accounts for the detrimental effects of chemotherapy on immune cell density. Because the parameter for this interaction is unknown, we run simulations while varying the parameter to observe the effect on the system. Our results show that combined dosages of chemo- and immunotherapy over the first days of the treatment period are sufficient to force the system to its healthy equilibrium.

Keywords: Biomedical system modelling, Model formulation, Simulation and visualization,
Abstract: This paper proposes two modeling approaches to predict lung tumor dynamics as an effect of radiotherapy. Real clinical information of non-small cell lung cancer (NSCLC) patients undergoing stereotactic body radiation therapy (SBRT) as the primary treatment method has been used for numerical simulations. The classical Gompertz model for tumor volume growth prediction was modified using a fractional parameter and combined with the linear-quadratic model to foresee the effect of SBRT on the targeted tumor. Another approach was implemented by following a pharmacokinetic-pharmacodynamic (PKPD) minimal compartmental model for single therapy with SBRT. Statistical analysis has been carried out to compare the two models. In terms of tumor growth prediction, obtained results indicated a decrease in the total tumor volume for both modeling approaches. A striking observation to emerge from the data comparison is the interesting perspective of fractional tools for further exploration in modeling tumor growth.

Keywords: Biological systems and controls, Biomedical system modelling, Decision support systems for the control of physiological and clinical variables
Abstract: One of the most difficult challenges in cancer therapy is the emergence of drug resistance within tumors. Sometimes drug resistance can emerge as the result of mutations and Darwinian selection. However, recently another phenomenon has been discovered, in which tumor cells switch back and forth between drug-sensitive and pre-resistant states. Upon exposure to the drug, sensitive cells die off, and pre-resistant cells become locked in to a state of permanent drug resistance. In this paper, we explore the implications of this transient state switching for therapy scheduling. We propose a model to describe the phenomenon and estimate parameters from experimental melanoma data. We then compare the performance of continuous and alternating drug schedules, and use sensitivity analysis to explore how different conditions affect the efficacy of each schedule. We find that for our estimated parameters, a continuous therapy schedule is optimal. However we also find that an alternating schedule can be optimal for other, hypothetical parameter sets, depending on the difference in growth rate between pre- drug and post-drug cells, the delay between exposure to the drug and emergence of resistance, and the rates of switching between states.

Keywords: Biological systems and controls, Decision support systems and feedback control, Biomedical system modelling
Abstract: We present some "in silico" experiments to design combined chemo- and immunotherapy treatment schedules. We introduce a new framework by combining flatness-based control, which is a model-based setting, along with model-free control. The flatness property of the used mathematical model yields straightforward reference trajectories. They provide us with the nominal open-loop control inputs. Closing the loop via model-free control allows to deal with the uncertainties on the injected drug doses. Several numerical simulations illustrating different case studies are displayed. We show in particular that the considered health indicators are driven to the safe region, even for critical initial conditions. Furthermore, in some specific cases there is no need to inject chemotherapeutic agents.

Keywords: Model formulation, Decision support systems and feedback control, Kinetic modelling and control of biological systems
Abstract: Social distancing strategies have been adopted by governments to manage the COVID-19 pandemic, since the first outbreak began. However, further epidemic waves keep out the return of economic and social activities to their standard levels of intensity. Social distancing interventions based on control theory are needed to consider a formal dynamic characterization of the implemented SIR-type model to avoid unrealistic objectives and prevent further outbreaks. The objective of this work is twofold: to fully understand some dynamical aspects of SIR-type models under control actions (associated with second waves) and, based on it, to propose a switching non-linear model predictive control that optimize the non-pharmaceutical measures strategy. Opposite to other strategies, the objective here is not just to minimize the number of infected individuals at any time, but to minimize the final size of the epidemic while minimizing the time of social restrictions and avoiding the infected prevalence peak to overpass a maximum established by the healthcare system capacity. Simulations illustrate the benefits of the aforementioned proposal.

Keywords: Biosignal analysis and interpretation,, Advances in sensing and signal processing
Abstract: Rapid eye movements (REM) during sleep is a descriptor of the REM sleep stage. Parameters associated with REM sleep, such as REM sleep numbers, REM density, REM latency, and pre-REM negativity have been associated with the functional role of REM sleep. The temporal properties of these parameters appear to play an essential role in REM sleep, so precise knowledge of these temporal properties, particularly REM onset, can help elucidate the temporal dynamics of neural activity related to REM sleep. However, manual detection of this event is a time-consuming and subjective process that can be facilitated by an automatic detection tool. We developed an automatic REM onset detection algorithm based on features describing rapid eye movements and compared the results obtained with human detection by a sleep expert.

Keywords: Model formulation, Identification and validation, Simulation and visualization,
Abstract: The aim of this article is to give a better understanding of the dynamics of the SARS-CoV-2 pandemic in the Bergamo province (Italy), one of the most hit areas of the world, between February and April 2020. A new compartmental model, called SIAT3HE, was designed and fitted on accurate data about the pandemic provided by ATS Bergamo, the health protection agency of the Bergamo province. Our results show that SARS-CoV-2 reached Bergamo in January and infected 318,000 people, the 28.8% of the province population. The 43.1% of the infected individuals stayed asymptomatic. As 6,028 people died due to COVID-19 till April 30th, the infection fatality ratio of SARS-CoV-2 in the Bergamo province was 1.9%. These results are in very good agreement with available information: the number of infections is consistent with the results of recent serological surveys and the number of deaths due to COVID-19 is close to the excess mortality of the considered period.

Keywords: Biomedical imaging systems, Decision support systems for the control of physiological and clinical variables, Control of voluntary movements, respiration, locomotion
Abstract: COVID-19 induced acute respiratory distress syndrome (ARDS) could have two different phenotypes, which was reported to have different response and outcome to the typical ARDS positive end-expiration pressure (PEEP) treatment. The identification of the different phenotypes in terms of the recruitability can help improve the patient outcome. In this contribution we conducted alveolar overdistention and collapse analysis with the long term electrical impedance tomography monitoring data on two severe COVID-19 pneumonia patients. The result showed different patient reactions to the PEEP trial, revealed the progressive change in the patient status, and indicted a possible phenotype transition in one patient. It might suggest that EIT can be a practical tool to identify phenotypes and to provide progressive information of COVID-19 pneumonia.

Keywords: Biomedical imaging systems
Abstract: Dynamic SPECT imaging captures the dynamic process of the radioisotope washed in and out, to and from tissues and exchanged between biological compartments. One of the most common application field for dynamic SPECT imaging is investigating renal functions. The main goal of this study was to create realistic simulations of the kidney in order to support the development of dynamic SPECT reconstruction algorithms. We created and parametrized a compartment model of the kidney for simulating the dynamic behavior of the MAG3 radiopharmacon. Solving the model with a parameter set corresponding to the normal and to the pathological cases the SPECT data acquisition was simulated using the GATE Monte Carlo simulation toolkit.

Keywords: Advances in sensing and signal processing, Model formulation, Biosignal analysis and interpretation,
Abstract: Electrical Impedance Tomography (EIT) is mainly used to display information about the respiration of a patient. However, also cardiac-related signals are present, and, although they have small amplitude, they can be distinguished by their frequencies. In this contribution, we report a method based on harmonic analysis to separate respiration and perfusion. These are described by a summation of amplitude-modulated signals at different frequencies. We report the mathematical background of the method and its application on the global impedance and, finally, show how it is possible to create frequency-related images highlighting either the respiration or the perfusion inside an EIT video.