Research Interests

  • Fields: Biomedical Signal Processing; Computational Physiology and Neuroscience, Statistical Physics
  • Research Activity: Development of advanced biomedical signal processing methods for the analysis of complex physiological systems, aimed at mechanism understanding and disease assessment
  • Methodological approach: Measurement of physiological time series from biomedical signals; development of methods for multivariate time series analysis in the time domain (prediction methods), frequency domain (spectral analysis) and information domain (entropy-based measures) for the quantitative description of the complexity of individual systems, the coupling between systems and their causal interaction.
  • Applicative contexts: neurophysiology; brain connectivity; cognitive neuroscience; cardiovascular neuroscience; cardiac, cardiorespiratory and cerebrovascular regulation; heart rate variability; cardiac atrial fibrillation; brain-heart interactions; network physiology.
  • Aims: characterization of brain, cardiac and multi-organ physiological mechanisms in physiological states (e.g.: aging, sleep, cognition, resting states, physiological stressors) and diseased conditions (e.g.:  sleep disorders, syncope, epilepsy, cardiac fibrillation).

Brain connectivity

Neural Models; Cognitive Experiments

Cardiovascular Interactions
Aging, Myocardial Infarction

Brain-Heart Interactions
Neurally-Mediated syncope

Physiological Network Analysis
Sleep disorders

Research Lines

Multi-System Analysis of the Human Physiological Network
Based on the view of the human body as an integrated network composed by several organ systems, each with its own internal dynamics but also functionally connected to the other organs, we apply novel methods for multivariate time series analysis to the nonlinear, multi-scale and time-variant output signals of brain, heart and peripheral physiological systems. This integrated unconventional approach aims at providing new insight on the functional structure of the human physiological networks and on its evolution across different physiological states and pathological conditions. A prime example of application of this multi-system approach is the study of sleep state transitions and sleep disorders.

  • A Porta, L Faes, 'Wiener-Granger Causality in Network Physiology with Applications to Cardiovascular Control and Neuroscience', Proceedings of the IEEE 2016; 104(2): 282-309.
  • L Faes, D Marinazzo, S Stramaglia, F Jurysta, A Porta, G Nollo, 'Predictability decomposition detects the impairment of brain-heart dynamical networks during sleep disorders and their recovery with treatment', Phil. Trans. R. Soc. A, special issue on "Uncovering brain-heart information through advanced signal and image processing", 2016; 374:20150177.
  • L Faes, D Marinazzo, F Jurysta, G Nollo, 'Linear and nonlinear analysis of brain-heart and brain-brain interactions during sleep', Phys. Meas. 2015; 36:683-698.


Information Dynamics of Brain Networks
In order to improve the understanding of how the brain function emerges from the coordinated behavior of spatially separated cerebral regions, we study the temporal dynamics of brain networks reconstructed from EEG or fMRI measurements. We define entropy-based measures quantifying the amounts of information generated at each network node, actively stored in the node, transferred to it from the other connected nodes, and modified during the transfer according to synergetic and/or redundant interactions. Information-theoretic measures are complemented with frequency-domain measures of brain connectivity to provide a complete analysis framework that is exploited to assess resting-state and brain networks and their modifications induced by stimulation or cognitive elicitation.

  • L Faes, D Marinazzo, G Nollo, A Porta 'An information-theoretic framework to map the spatio-temporal dynamics of the scalp electroencephalogram', IEEE Trans. Biomed. Eng., special issue on “Brain Connectivity”, in press, 2016; DOI: 10.1109/TBME.2016.2569823.
  • S Stramaglia, L Angelini, G Wu,  JM Cortes, L Faes, D Marinazzo, 'Synergetic and redundant information flow detected by unnormalized Granger causality: application to resting state fMRI', IEEE Trans. Biomed. Eng., special issue on "Brain Connectivity", in press, 2016; DOI: 10.1109/TBME.2016.2559578.
  • A Montalto, L Faes, D. Marinazzo, 'MuTE: a MATLAB toolbox to compare established and novel estimators of the multivariate transfer entropy', PLOS ONE 2014; 9(10):e109462 (13 pages).


Advanced Tools for Assessing the Complexity of Cardiovascular Regulation
The short-term analysis of heart rate, arterial pressure and respiration beat-to-beat variability  provides a non-invasive quantitative way to assess how the autonomic nervous system affects the cardiovascular control. We develop novel time series analysis tools, mainly rooted in information theory, to quantify the complexity of cardiac dynamics and how this complexity is explained by the vascular and respiratory dynamics. These tools are then exploited to investigate the mechanisms underlying cardiovascular and cardiorespiratory regulation under different physiological stressors and pathological states.

  • M Javorka, B Czippelova, Z Turianikova, Z Lazarova, I Tonhajzerova, L Faes, 'Causal analysis of short-term cardiovascular variability: state-dependent contribution of feedback and feedforward mechanisms', Med. Biol. Eng. Comput., in press, 2016; DOI: 10.1007/s11517-016-1492-y.
  • D Widjaja, A Montalto, E Vlemincx, D Marinazzo, S Van Huffel, L Faes, 'Cardiorespiratory information dynamics during mental arithmetic and sustained attention', PLOS ONE 2015; 10(6): e0129112 (14 pages).
  • L Faes, D Marinazzo, A Montalto, G Nollo, 'Lag-specific transfer entropy as a tool to assess cardiovascular and cardiorespiratory information transfer', IEEE Trans Biomed Eng 2014; 61(10):2556-2568. DOI: 10.1109/TBME.2014.2323131


Filling the Gap between Advanced Biosignal Methods, Clinical Practice and Personalized Medicine
The exploitation of tools for biomedical signal processing and time series analysis in the clinical practice is currently limited, among other factors, by the lack of a standardized assessment of descriptive measures, the computational burden of their calculation, and the poor consideration of inter-individual differences. Focusing on the evaluation of measures which have shown their diagnostic power (e.g., complexity indexes of heart rate variability), we face these issues by comparing a wide range of analysis techniques, implementing data-efficient algorithms for their computation, and designing statistical procedures to establish the confidence limits of their estimates. Our goals are to achieve a computational breakthrough in the assessment of complex physiological dynamics, as well as to characterize individual differences in cardiovascular control in a number of experimental conditions. This will allow to provide a better knowledge on the specific physiology of each individual, leading to a routinely feasible personalized assessment of the underlying physiology, and ultimately to an improved diagnosis of altered conditions.

  • L Faes, DMS Simpson, A Beda, 'Estimation of Confidence Limits for Descriptive Indexes Derived from Autoregressive analysis of  Time Series: Methods and Application to Heart Rate Variability', submitted, 2016.
  • A Porta, B De Maria, V Bari, A Marchi, L Faes, 'Are Nonlinear Model-Free Approaches for the Assessment of the Entropy-Based Complexity of the Cardiac Control Superior to the Linear Model-Based one?', submitted, 2017.
  • A Porta, G Nollo, L Faes, 'Editorial: Bridging the gap between the development of advanced biomedical signal processing tools and clinical practice', Phys. Meas. 2015; 36:627-631.

Directed brain-heart interactions during sleep

Network Physiology: how different organ systems dynamically interact

    Last updated Jun 23, 2016