Biomedical Signal Processing

Submitted by zennaro2 on

EEG Analysis

Submitted by zennaro2 on

  • Frequency and time-frequency analysis of EEG, also under trans-magnetic stimulation
  • EEG in hepatic encephalopathy and epilepsy
  • Relationships between infra-slow components of blood flow velocity and EEG variability
  • Assessment of functional connectivity from EEG data
  • Bayesian methods for (single-trial) ERP estimation

Data analysis, learning and control for biology and medicine

Submitted by zennaro2 on

We develop methodological tools, algorithms and software to analyze data and vital signals reflecting individual health and to exploit them with learning techniques to improve patient therapy, possibly creating real-time feedback mechanisms. Tools are personalized, adaptive, proactive and equipped with intelligent self-diagnostic functions. Models to predict and prevent the incidence of new diseases or medical complications are also investigated.

Application to high-throughput data

Submitted by zennaro2 on

  • Preprocessing and analysis of static and dynamic RNA-seq and microarray expression data.
  • Preprocessing of SNP arrays and exome sequencing data and multivariate analysis of genetic variations
  • Optimal primer design, pre-processing and analysis of 16S sequencing data and reverse engineering of microbial networks.
  • Methods for the preprocessing and quantification of mass spectrometry data and for the modeling of protein turnover from SILAC data.

    Development and application of advanced modeling, data mining and machine learning methods for high-throughput biological data analysis

    Submitted by zennaro2 on

    • Deterministic and stochastic modelling of transcriptional networks and signaling pathways, reverse engineering of transcriptional networks and miRNA-mRNA regulatory motifs, and integration of genetic phenotypic and environmental risk factors via Systems Genetics approaches
    • Advanced data mining and machine learning methods for robust biomarker discovery, predictive modeling and clustering from microarrays and next generation sequencing  data

    Modeling, Identification and Control of Physiological Systems

    Submitted by zennaro2 on

    This research area deals with the use of mathematical modeling techniques to develop and validate mathematical models able to either simulate the behavior of complex, dynamical biological systems, or to estimate key parameters usable to quantify physiological processes. Depending on the aim and the available experimental data, the models can be whole-body, organ/tissues, cellular or multiscale, formulated as ordinary or partial differential equations, deterministic or stochastics, minimal or large scale.

    Control of Physiological Systems

    Submitted by zennaro2 on

    • Development of control algorithms for automatic insulin delivery in type 1 diabetes (Artificial Pancreas): personalization, run2run adaptation, fault detection and fault compensation for safe unsupervised AP use.
    • In silico validation and clinical testing of AP algorithms.

    Identification & Parameter Estimation Techniques

    Submitted by zennaro2 on

    • Parameter estimation techniques at both the individual and population level (nonlinear-mixed effects)
    • Deconvolution techniques for biomedical signals analysis

    Modeling of Physiological Systems

    Submitted by zennaro2 on

    • Development and validation of “Minimal" models of the glucose system to measure signals and parameters (such insulin sensitivity, beta-cell function, hepatic insulin extraction, hepatic glucose production, glucose utilization and rate of appearance of ingested glucose) from intravenous/oral tolerance tests.
    • Development and validation of "Maximal" models of the glucose-insulin system for in silico simulation.
    • Development and validation of Models of the glucose-insulin system for use in control applications (artificial pancreas).

      Functional and Anatomical Imaging Research for Neuroscience

      Submitted by zennaro2 on

      Neuroimaging is a crucial method of investigation for studying the human brain in healthy and impaired populations. Cutting edge improvements in imaging are, for instance, integral to intervention and prognostication in the neuro-oncology field. Our research activities include development and application of statistical methods for modeling and integrate the complexity of the physiological information provided from MRI, PET and electrocortical data.

      Pages

      Subscribe to DEI RSS