Research institute for
signals, systems and
computational intelligence

Biomedical Signal Processing

Human beings are a source of many signals that encode information related to the health status of the person, its intentions, the status of diverse biological process in his body, the information he is trying to communicate, etc. In general these signals are of nonlinear and nonstationary nature, and the information is hidden and coded in a complex way which is not clearly exposed in the raw signal. In many cases, the information conveyed in those signals can be interpreted rather easily by other human beings. For example, a listener can understand the meaning of a speech sentence and the emotional state of the speaker. As another example, an expert can determine the presence or absence of apnea events by watching a polysomnogram of a sleeping patient. Nevertheless, when one tries to replicate this characteristic in a computer, the nonlinear and nostationary characteristics pose a serious problem for an automatic algorithm.
Moreover, there are signals that carry interesting information that at the present day cannot be interpreted by other humans. For example, the electroencephalographic record obtaining at the same time a person is imagining to pronounce a word, carry information regarding the word that was imagined, but it is not possible for a human observer to extract this information.
In the last years several advanced signal processing methods have been developed which try to improve the capabilities of information extraction, and can be used to improve the behaviour of systems dedicated to detect its presence or absence in a specific signal. Some of these techniques are sparse representation, nonnegative matrix factorization, blind source separation, independent component analysis, regularization and inverse problems

Objetive

In this line, these (and other) advanced signal processing method are developed and extended to address the problems found in specific signals applied to problems of speech recognition, speech denoising and dereverberation, detection of apneas and hypopneas, prediction of the presence of the sleep apnea-hypopneas syndrome and other related applications.

Director

Leonardo Rufiner

Publications

Publications in 2020
An online method for estimating grazing and rumination bouts using acoustic signals in grazing cattle   sinc_CVRGPUMGR20.doi sinc_CVRGPUMGR20.pdf
J. O. Chelotti, S. R. Vanrell, L. Rau, J. Galli, A. M. Planisich, S.A Utsumi, D. H. Milone, L. Giovanini, H. L. Rufiner
Computers and Electronics in Agriculture, Volume 173 – 2020
Publications in 2019
Sleep-wake stages classification using heart rate signals from pulse oximetry   sinc_CDS19.doi sinc_CDS19.pdf
R. Casal, L. Di Persia, G. Schlotthauer
Heliyon, Volume 5, Number 10, page e02529 – 2019
Publications in 2018
A regularity-based algorithm for identifying grazing and rumination bouts from acoustic signals in grazing cattle   sinc_VCGUGRM18.doi sinc_VCGUGRM18.pdf
S. R. Vanrell, J. O. Chelotti, J. Galli, S.A Utsumi, L. Giovanini, H. L. Rufiner, D. H. Milone
Computers and Electronics in Agriculture, Volume 151, page 392–402 – 2018
Assessment of Homomorphic Analysis for Human Activity Recognition from Acceleration Signals   sinc_VMR17.doi sinc_VMR17.pdf
S. R. Vanrell, D. H. Milone, H. L. Rufiner
IEEE Journal of Biomedical and Health Informatics, page 1001–1010 – 2018
Measuring complexity of biomedical signals (Editorial)   sinc_SHER18.doi sinc_SHER18.pdf
G. Schlotthauer, A. Humeau-Heurtier, J. Escudero, H. L. Rufiner
Complexity, Volume 2018 – 2018
Publications in 2017
Discriminative methods based on sparse representations of pulse oximetry signals for sleep apnea–hypopnea detection   sinc_RLDSR17.doi sinc_RLDSR17.pdf
R.E. Rolon, L. Larrateguy, L. Di Persia, R. Spies, H. L. Rufiner
Biomedical Signal Processing and Control, Volume 33, page 358–367 – 2017
Embedded system for real-time monitoring of foraging behavior of grazing cattle using acoustic signals   sinc_PDRGCLG17.doi sinc_PDRGCLG17.pdf
Nestor N. Deniz, J. O. Chelotti, J. Galli, A. M. Planisich, M. Larripa, H. L. Rufiner, L. Giovanini
Computers and Electronics in Agriculture, Volume 138, page 167–174 – 2017
Publications in 2016
Multi-objective optimisation of wavelet features for phoneme recognition   sinc_VRM16.doi VRM16.doi sinc_VRM16.pdf
L. D. Vignolo, H. L. Rufiner, D. H. Milone
IET Signal Processing, Volume 10, Number 6, page 685–691 – 2016
Open Access database of EEG signals recorded during imagined speech   sinc_PGR16.pdf
G. Pressel-Coreto, I. E. Gareis, H. L. Rufiner
12th International Symposium on Medical Information Processing and Analysis (SIPAIM) – 2016
Publications in 2015
Denoising sound signals in a bioinspired non-negative spectro-temporal domain   MGDMR14.doi sinc_MGDMR15.pdf
C. E. Martínez, J. Goddard, L. Di Persia, D. H. Milone, H. L. Rufiner
Digital Signal Processing, Volume 38, page 22–31 – jan 2015
Publications in 2014
Local Discriminant Wavelet Packet Basis for Signal Classification in Brain Computer Interface   sinc_PARS14.pdf
V. Peterson, R. C. Acevedo, H. L. Rufiner, R. Spies
Anales del VI Congreso Latinoamericano de Ingeniería Biomédica (CLAIB 2014), page 279 – oct 2014
Publications in 2011
Denoising audio signals in the non-negative auditory cortical domain   sinc_MGDMR11.pdf
C. E. Martínez, J. Goddard, L. Di Persia, D. H. Milone, H. L. Rufiner
12th Argentine Symposium on Technology (40th JAIIO) – aug 2011
Short-time multifractal analysis: application to biological signals   sinc_LST11.doi sinc_LST11.pdf
R. F. Leonarduzzi, G. Schlotthauer, M. E. Torres
Journal of Physics, Volume 313, Number 012012 – 2011
Publications in 2009
EMD of Gaussian White Noise: Effects of Signal Length and Sifting Number on the Statistical Properties of Intrinsic Mode Function   sinc_STRF09.doi sinc_STRF09.pdf
G. Schlotthauer, M. E. Torres, H. L. Rufiner, P. Flandrin
Advances in Adaptive Data Analysis, Volume 1, Number 4, page 517–527 – 2009
Publications in 2008
Signal denoising with hidden Markov models using hidden Markov trees as observation densities   po.http sinc_MDT08.pdf
D. H. Milone, L. Di Persia, D. R. Tomassi
18th IEEE International Workshop on Machine Learning for Signal Processing – october 16-18 2008
Publications in 2007
Auditory cortical representations of speech signals for phoneme classification   sinc_RMMG07.pdf
H. L. Rufiner, C. E. Martínez, D. H. Milone, J. Goddard
Springer-Verlag, Berlin Heidelberg, Volume 4827, page 1004-–1014 – 2007
Publications in 2006
Separation of mixed speech signals of short duration using Wiener filter as postprocessing for Frequency-Domain ICA   sinc_HDOY06.pdf
Akihiro Handa, L. Di Persia, Kenko Ohta, Masuzo Yanagida
IPSJ SIG Notes, Volume 2006, Number 12, page 1-6 – 2006
Publications in 2005
Analysis of spanish synthesized speech signals using spectral and basis pursuit representations   sinc_MGMR05.pdf
F. M. Martínez, J. Goddard, A. E. Martínez, H. L. Rufiner
Proc. of the 4th International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications – october 2005
Publications in 2004
Analysis of Spanish Speech Signals using Higher Order Statistics   sinc_MGMR04.pdf
F. M. Martínez, J. Goddard, A. E. Martínez, H. L. Rufiner
Memorias del III Congreso Latinoamericano de Ingeniería Biomédica (IIICLAEB 2004) – september 2004
Introducing complexity measures in nonlinear physiological signals: application to robust speech recognition   sinc_RTGM04.pdf
H. L. Rufiner, M. E. Torres, L. G. Gamero, D. H. Milone
Physica A: Statistical Mechanics and its Applications, Volume 332, Number 1, page 496–508 – 2004