Full Curriculum vitae
Faes received the MS degree in Electronic Engineering from the University
of Padova, Italy, in 1998, and the PhD degree in Electronic Devices
from the University of Trento, Italy, in 2003. He was a Research Fellow
on system identification and modeling at the Medical Biophysics Division
of ITC-irst (Institute for Scientific and Technologic Research), Trento,
until 2001, and at the Biophysics and Biosignals Laboratory of the Department
of Physics, University of Trento, until 2008. He is currently with the
Interdepartmental Center for Biotechnologies (BIOtech) of the University
of Trento. He has been visiting scientist at the Dept. of Biomedical
Engineering, State University of New York, Stony Brook (May-July 2007),
and at the Dept. of Biomedical Engineering, Worcester Polytechnic Institute,
Worcester, MA, USA (Sept-Dec 2010).
Luca Faes is a member of the IEEE Enginering in Medicine and Biology
Society (IEEE-EMBS), and of the Italian Society of Chaos and Complexity.
He is Associate Editor for the Signal Processing Theme (since 2008)
and co-chair of the Track on Connectivity and Causality (since 2011)
of the IEEE-EMBS Annual International Conference. He is member of the
board of the journal ISRN Biomedical Engineering (launched in 2012).
He has been Guest Editor of the special issues “Methodological
Advances in Brain Connectivity” (Comp. Math. Methods Med. 2012)
and “Assessing Causality in Brain Dynamics and Cardiovascular
Control” (Phil. Trans. Royal Soc. A 2013).
The research interests of Luca Faes regard digital signal processing
and system modeling aimed to the characterization of physiological systems.
His research activity is focused on the development of advanced methods
for time series analysis and model identification, with application
to the study of the physiological mechanisms underlying cardiac atrial
fibrillation, cardiovascular control and brain activity and connectivity.
Within this field, he has authored three book chapters and about 100
papers in peer-reviewed international journals (see full
list) and conference proceedings, receiving more than 550 citations
Papers (see full
Faes, G Nollo, A Porta, 'Compensated transfer entropy as
a tool for reliably estimating information transfer in physiological
time series', Entropy; special issue on “Transfer Entropy”,
Faes, S Erla, G Nollo: 'Measuring connectivity in linear
multivariate processes: definitions, interpretation and practical
analysis', Comp Math Methods Med, special issue on “Methodological
Advances in Brain Connectivity”, 2012; 140513:18 pages.
L Faes, G Nollo, A Porta: 'Information-based detection
of nonlinear Granger causality in multivariate processes via a nonuniform
embedding technique', Phys Rev E; 2011; 83(5 Pt 1):051112.
L Faes, A Porta, G Nollo: 'Testing frequency domain
causality in multivariate time series', IEEE Trans Biomed Eng 2010;
Faes, Ki H Chon, G Nollo: 'A method for the time-varying
nonlinear prediction of complex nonstationary biomedical signals',
IEEE Trans Biomed Eng 2009;56(2):205-209.
G Nollo, K H Chon: 'Assessment of Granger causality by nonlinear
model identification: application to short-term cardiovascular variability',
Ann Biomed Eng 2008;36(3):381-395.
Faes, L Widesott, M Del Greco, R Antolini, G Nollo: 'Causal
cross-spectral analysis of heart rate and blood pressure variability
for describing the impairment of the cardiovascular control in
neurally mediated syncope', IEEE Trans Biomed Eng 2006;53:65-73.
Nollo, L Faes, A Porta, R Antolini, F Ravelli:
'Exploring directionality in spontaneous heart period and systolic
pressure variability interactions in humans. Implications in the
evaluation of the baroreflex gain', Am J Physiol Heart Circ Physiol
L Faes, GD Pinna, A Porta, R Maestri, G Nollo:
'Surrogate data analysis for assessing the significance of the coherence
function', IEEE Trans Biomed Eng 2004;51(7):1156-1166.
Faes, G Nollo, R Antolini, F Gaita, F Ravelli: 'A method
for quantifying atrial fibrillation organization based on wave morphology
similarity', IEEE Trans Biomed Eng 2002;49:1504-1513.
Matlab toolbox for extended MVAR
Matlab toolbox for block MVAR modelling: