A minimal realization technique for the dynamical structure function of a class of LTI systems

in IEEE Transactions on Control of Network Systems (in press)

Functional observability and subspace reconstruction in nonlinear systems

in Physical Review Research (2022), 4

Time-series analysis is fundamental for modeling and predicting dynamical behaviors from time- ordered data, with applications in many disciplines such as physics, biology, finance, and engineering ... [more ▼]

Time-series analysis is fundamental for modeling and predicting dynamical behaviors from time- ordered data, with applications in many disciplines such as physics, biology, finance, and engineering. Measured time-series data, however, are often low dimensional or even univariate, thus requiring embedding methods to reconstruct the original system’s state space. The observability of a system establishes fundamental conditions under which such reconstruction is possible. However, complete observability is too restrictive in applications where reconstructing the entire state space is not necessary and only a specific subspace is relevant. Here, we establish the theoretic condition to reconstruct a nonlinear functional of state variables from measurement processes, generalizing the concept of functional observability to nonlinear systems. When the functional observability condition holds, we show how to construct a map from the embedding space to the desired functional of state variables, characterizing the quality of such reconstruction. The theoretical results are then illustrated numerically using chaotic systems with contrasting observability properties. By exploring the presence of functionally unobservable regions in embedded attractors, we also apply our theory for the early warning of seizure-like events in simulated and empirical data. The studies demonstrate that the proposed functional observability condition can be assessed a priori to guide time-series analysis and experimental design for the dynamical characterization of complex systems. [less ▲]

Analysis and comparison of gait impairments in patients with Parkinson’s disease and normal pressure hydrocephalus using wearable sensors and machine learning algorithms

Scientific Conference (2022, September 05)

Objectives. Gait impairments in patients with Parkinson’s disease (PD) and normal pressure hydrocephalus (NPH) are visually assessed by movement disorders experts for diagnoses and to decide on ... [more ▼]

Objectives. Gait impairments in patients with Parkinson’s disease (PD) and normal pressure hydrocephalus (NPH) are visually assessed by movement disorders experts for diagnoses and to decide on pharmaceutical and surgical interventions. Despite standardised tests and clinicians’ expertise, such approaches entail a considerable level of subjectivity. The recent development of wearable sensors and machine learning offers complementary approaches providing more objective, quantitative assessments of gait impairments. We aim to employ the data gathered from an inertial measurement unit synchronized with a novel foot pressure sensor embedded in the patient’s shoes to characterize gait impairments. We focus on distinguishing PD from NPH and on assessing gait impairment before and after surgical intervention. Methods. A cohort of 10 PD and 10 NPH patients was assembled and patients performed standardised walking tests. Measurements were performed employing wearable sensors comprising a three-axes gyroscope, a three-axes accelerometer and eight pressure sensors embedded in each patient’s shoe. To analyse the generated data, existing algorithms were implemented and adapted. These allow to compute gait cycle parameters such as step time and metrics characterizing the swing and stance phases. Machine learning algorithms where employed to identify major changes in gait cycle parameters between the two groups of patients, and for individual patients before and after surgical intervention as DBS implantation in PD and Shunt implantation in NPH. Results. The gait impairments of both disease groups were measured and quantified. An algorithm to extract gait cycle parameters from sensors was implemented, tested and employed on such patients. Gait cycle parameters within and between the groups of PD and NPH patients were compared, assessing what gait cycle parameters allow to distinguish between these groups. Gait cycle impairments of patients before and after surgery were compared, assessing the effect of DBS or Shunt implantation and which gait cycle parameters allow to monitor symptoms improvement. Conclusions. Wearable sensors measuring pressure, combined with gait cycle parameters extraction and machine learning algorithms, have a great potential for objective evaluation of gait impairment. In particular, they allow to characterize what differentiate such impairments between PD and NPH patients, and what allow to assess motor symptoms improvement after surgery. [less ▲]

Linear system identifiability from single-cell data

in Systems and Control Letters (2022), 165

Buffering variability in cell regulation motifs close to criticality

in Physical Review. E. (2022)

Bistable biological regulatory systems need to cope with stochastic noise to fine tune their function close to bifurcation points. Here, we study stability properties of this regime in generic systems to ... [more ▼]

Bistable biological regulatory systems need to cope with stochastic noise to fine tune their function close to bifurcation points. Here, we study stability properties of this regime in generic systems to demonstrate that cooperative interactions buffer system variability, hampering noise-induced regime shifts. Our analysis also shows that, in the considered cooperativity range, impending regime shifts can be generically detected by statistical early warning signals from distributional data. Our generic framework, based on minimal models, can be used to extract robustness and variability properties of more complex models and empirical data close to criticality. Our generic framework, based on minimal models, can be used to extract robustness and variability properties of more complex models and empirical data close to criticality. [less ▲]

Model-based assessment of COVID-19 epidemic dynamics by wastewater analysis

in Science of the Total Environment (2022), 827

Continuous surveillance of COVID-19 diffusion remains crucial to control its diffusion and to anticipate infection waves. Detecting viral RNA load in wastewater samples has been suggested as an effective ... [more ▼]

Continuous surveillance of COVID-19 diffusion remains crucial to control its diffusion and to anticipate infection waves. Detecting viral RNA load in wastewater samples has been suggested as an effective approach for epidemic monitoring and the development of an effective warning system. However, its quantitative link to the epidemic status and the stages of outbreak is still elusive. Modelling is thus crucial to address these challenges. In this study, we present a novel mechanistic model-based approach to reconstruct the complete epidemic dynamics from SARS-CoV-2 viral load in wastewater. Our approach integrates noisy wastewater data and daily case numbers into a dynamical epidemiological model. As demonstrated for various regions and sampling protocols, it quantifies the case numbers, provides epidemic indicators and accurately infers future epidemic trends. Following its quantitative analysis, we also provide recommendations for wastewater data standards and for their use as warning indicators against new infection waves. In situations of reduced testing capacity, our modelling approach can enhance the surveillance of wastewater for early epidemic prediction and robust and cost-effective real-time monitoring of local COVID-19 dynamics. [less ▲]

Therapeutic maps for a sensor-based evaluation of deep brain stimulation programming

in Biomedizinische Technik. Biomedical Engineering (2021)

Modelling COVID-19 dynamics and potential for herd immunity by vaccination in Austria, Luxembourg and Sweden

in Journal of Theoretical Biology (2021)

Against the COVID-19 pandemic, non-pharmaceutical interventions have been widely applied and vaccinations have taken off. The upcoming question is how the interplay between vaccinations and social ... [more ▼]

Against the COVID-19 pandemic, non-pharmaceutical interventions have been widely applied and vaccinations have taken off. The upcoming question is how the interplay between vaccinations and social measures will shape infections and hospitalizations. Hence, we extend the Susceptible-Exposed-Infectious-Removed (SEIR) model including these elements. We calibrate it to data of Luxembourg, Austria and Sweden until 15 December 2020. Sweden results having the highest fraction of undetected, Luxembourg of infected and all three being far from herd immunity in December. We quantify the level of social interaction, showing that a level around 1/3 of before the pandemic was still required in December to keep the effective reproduction number Refft below 1, for all three countries. Aiming to vaccinate the whole population within 1 year at constant rate would require on average 1,700 fully vaccinated people/day in Luxembourg, 24,000 in Austria and 28,000 in Sweden, and could lead to herd immunity only by mid summer. Herd immunity might not be reached in 2021 if too slow vaccines rollout speeds are employed. The model thus estimates which vaccination rates are too low to allow reaching herd immunity in 2021, depending on social interactions. Vaccination will considerably, but not immediately, help to curb the infection; thus limiting social interactions remains crucial for the months to come. [less ▲]

SARS-CoV-2 transmission risk from asymptomatic carriers: Results from a mass screening programme in Luxembourg

in The Lancet Regional Health. Europe (2021), 4

Background To accompany the lifting of COVID-19 lockdown measures, Luxembourg implemented a mass screening (MS) programme. The first phase coincided with an early summer epidemic wave in 2020. Methods rRT ... [more ▼]

Background To accompany the lifting of COVID-19 lockdown measures, Luxembourg implemented a mass screening (MS) programme. The first phase coincided with an early summer epidemic wave in 2020. Methods rRT-PCR-based screening for SARS-CoV-2 was performed by pooling of samples. The infrastructure allowed the testing of the entire resident and cross-border worker populations. The strategy relied on social connectivity within different activity sectors. Invitation frequencies were tactically increased in sectors and regions with higher prevalence. The results were analysed alongside contact tracing data. Findings The voluntary programme covered 49 of the resident and 22 of the cross-border worker populations. It identified 850 index cases with an additional 249 cases from contact tracing. Over-representation was observed in the services, hospitality and construction sectors alongside regional differences. Asymptomatic cases had a significant but lower secondary attack rate when compared to symptomatic individuals. Based on simulations using an agent-based SEIR model, the total number of expected cases would have been 42·9 (90 CI [-0·3, 96·7]) higher without MS. Mandatory participation would have resulted in a further difference of 39·7 [19·6, 59·2]. Interpretation Strategic and tactical MS allows the suppression of epidemic dynamics. Asymptomatic carriers represent a significant risk for transmission. Containment of future outbreaks will depend on early testing in sectors and regions. Higher participation rates must be assured through targeted incentivisation and recurrent invitation. Funding This project was funded by the Luxembourg Ministries of Higher Education and Research, and Health. [less ▲]

Automated Deep Learning-based Segmentation of Brain, SEEG and DBS Electrodes on CT Images.

in Bildverarbeitung für die Medizin 2021 (2021)