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Neuronal networks are used in different fields of science and technology due to their capacity to approximate nonlinear functions through the synaptic weights optimization. This work shows a new form of optimization for neuronal networks based in fractional calculus. The fractional adaptation algorithm proposed was used to identify mechanical, electrical and biological systems. In each of the experiments a comparison between the proposed fractal-fractional model and the conventional model (with derivation order equal to one) was made.

In this work, a methodology based on a neural network to solve fractal-fractional differential equations with a nonsingular and nonlocal kernel is proposed, the neural network is optimized by the Levenberg–Marquardt algorithm. For evaluating the neural network, different chaotic oscillators of variable order are solved and compared with algorithms of numeric approximation.

This study aims to assess the interoperability of Diabeloop’s DBLG1 Closed Loop System on glycemic control for T1D patients, based on data from clinical trials NCT02987556[1] (SP7) and NCT04190277 (SP8). SP7 study was conducted on 63 out of 68 patients randomly assigned to two arms equipped with DBLG1 system using Cellnovo pump for arm 1 and Kaleido pump for arm 2. All patients attended a 12-week open loop (OL) phase (receiving their usual treatment) and a 12-week closed loop (CL) phase (using DBLG1 system). In SP8 study, 178 out of 184 patients, randomly assigned to two arms, started with a 2-week baseline phase in OL. The next 12 weeks, patients in arm 2 remained in OL while patients in arm 1 switched to CL equipped with DBLG1 System using DANA Diabecare-i insulin pump. The study was shortened due to COVID-19 epidemic. To assess the improvement reached by DBLG1 System, we compared the average time in range 70-180 mg/dL (TIR) between OL and active CL phases. For SP7 study, we analysed data from both arms. For SP8 study, to assess the TIRs for the same patients, we compared baseline and CL phases for arm 1. As shown in Figure 1, for SP7 the average TIRs increased by 8.6% for arm 1 (32 patients) and by 14.0% for arm 2 (31 patients in CL, 30 in OL). For SP8 arm 1 the average TIR increased by 12.8% (142 patients). DBLG1 System improved the TIR despite the use of 3 different pumps. Therefore, improvement linked with DBLG1 System on the average TIR is independent of the pump used. Disclosure A. Adenis: Employee; Self; Diabeloop SA. L. Pou: Employee; Self; Diabeloop SA. H. M. Romero-ugalde: Employee; Self; Diabeloop SA. Y. Tourki: Employee; Self; Diabeloop SA. S. Franc: Advisory Panel; Self; Diabeloop SA, Board Member; Self; Novo Nordisk, Other Relationship; Self; Abbott Diabetes, Research Support; Self; Merck Sharp & Dohme Corp., Stock/Shareholder; Self; Diabeloop SA. G. Charpentier: Other Relationship; Self; Diabeloop. P. Y. Benhamou: Consultant; Self; Diabeloop SA, Eli Lilly and Company, Insulet Corporation, Other Relationship; Self; Abbott Diabetes. M. Doron: Research Support; Self; Diabeloop SA. E. Huneker: Employee; Self; Diabeloop SA.

This paper presents the first autoregressive with exogenous input (ARX) model using energy expenditure, carbohydrates on board, and insulin on board as input to predict interstitial glucose (IG). The proposed model may be used for predicting IG even during physical activity (PA). A population-based model, obtained from a first database composed of 14 type 1 diabetes (T1D) patients, achieved a root-mean-square error (RMSE) of 16 . 7 ± 15 . 6 mg/dL, on IG prediction (30-min ahead) at the end of a PA, on a second database (15 T1D patients). Patient-specific ARX models, obtained on the second database, improved prediction accuracy (RMSE = 7 . 8 ± 4 . 5 mg/dL), outperforming the results found in the literature.

International audience; Objective Vagus nerve stimulation (VNS) is a potential therapeutic approach in a number of clinical applications. Although VNS is commonly delivered in an open-loop approach, it is now recognized that closed-loop stimulationmay be necessary to optimize the therapy. In this paper, we propose an original generic closed-loop control system that can be readily integrated into an implantable device and allows for the adaptive modulation of multiple VNS parameters. Methods The proposed control method consists of a state transition model (STM), in which each state represents a set of VNS parameters, and a state transition algorithm that optimally selects the best STM state, minimizing the error between an observed physiological variable and a given target value. The proposed method has been integrated into a real-time adaptive VNS prototype system and has been applied here to the regulation of the instantaneous heart rate, working synchronously with cardiac cycles. A quantitative performance evaluation is performed on seven sheep by computing classical control performance indicators. A comparison with a proportional-integral (PI) controller is also performed. Results The STM controller presents a median mean square error, overshoot, and settling time, respectively, equal to 622.21 ms(2), 72.8%, and 7.5 beats. Conclusion The proposed control method yields satisfactory accuracy and time response, while presenting a number of benefits over classical PI controllers. It represents a feasible approach for multiparametric VNS control on implantable devices. Significance Closed-loop multiparametric stimulation may improve response and minimize side effects on current pathologies treated by VNS.

International audience; Although the therapeutic effects of Vagus Nerve Stimulation (VNS) have been recognized in pre-clinical and pilot clinical studies, the effect of different stimulation configurations on the cardiovascular response is still an open question, especially in the case of VNS delivered synchronously with cardiac activity. In this paper, we propose a formal mathematical methodology to analyze the acute cardiac response to different VNS configurations, jointly considering the chronotropic, dromotropic and inotropic cardiac effects. A latin hypercube sampling method was chosen to design a uniform experimental plan, composed of 75 different VNS configurations, with different values for the main parameters (current amplitude, number of delivered pulses, pulse width, interpulse period and the delay between the detected cardiac event and VNS onset). These VNS configurations were applied to 6 healthy, anesthetized sheep, while acquiring the associated cardiovascular response. Unobserved VNS configurations were estimated using a Gaussian process regression (GPR) model. In order to quantitatively analyze the effect of each parameter and their combinations on the cardiac response, the Sobol sensitivity method was applied to the obtained GPR model and inter-individual sensitivity markers were estimated using a bootstrap approach. Results highlight the dominant effect of pulse current, pulse width and number of pulses, which explain respectively 49.4%, 19.7% and 6.0% of the mean global cardiovascular variability provoked by VNS. More interestingly, results also quantify the effect of the interactions between VNS parameters. In particular, the interactions between current and pulse width provoke higher cardiac effects than the changes on the number of pulses alone (between 6 and 25% of the variability). Although the sensitivity of individual VNS parameters seems similar for chronotropic, dromotropic and inotropic responses, the interacting effects of VNS parameters provoke significantly different cardiac responses, showing the feasibility of a parameter-based functional selectivity. These results are of primary importance for the optimal, subject-specific definition of VNS parameters for a given therapy and may lead to new closed-loop methods allowing for the optimal adaptation of VNS therapy through time.

International audience; Vagus nerve stimulation (VNS) is an established adjunctive therapy for pharmacologically refractory epilepsy and depression and is currently in active clinical research for other applications. In current clinical studies, VNS is delivered in an open-loop approach, where VNS parameters are defined during a manual titration phase. However, the physiological response to a given VNS configuration shows significant inter and intra-patient variability and may significantly evolve through time. VNS closed-loop approaches, allowing for the optimization of the therapy in an adaptive manner, may be necessary to improve efficacy while reducing side effects. This paper proposes a generic, closed-loop control VNS system that is able to optimize a number of VNS parameters in an adaptive fashion, in order to keep a control variable within a specified range. Although the proposed control method is completely generic, an example application using the cardiac beat to beat interval (RR) as control variable will be developed in this paper. The proposed controller is based on a state transition model (STM) that can be configured using a partially or a fully-connected architecture, different model orders and different state-transition algorithms. The controller is applied to the adaptive regulation of heart rate and evaluated on 6 sheep, for 13 different targets, using partially-connected STM with 10 states. Also, partially and fully-connected STM defined by 30 states were applied to 7 other sheep for the same 10 targets. Results illustrate the interest of the proposed fully-connected STM and the feasibility of integrating this control system into an implantable neuromodulator.