Tom Trigano
@sce.ac.il
Department of Electrical Engineering
From 2006 to 2008 I worked as a postdoctoral fellow at the Hebrew University of Jersualem in the department of statistics. Since 2008 I am a senior lecturer in the Shamoon College of Engineering, Israel. My main research interests include applied statistics, statistical signal processing, pattern recognition and machine learning with applications to spectroscopy and biomedical applications.
EDUCATION
Tom (Thomas) Trigano was born in Paris, France in 1978, and received an M.Sc. in engineering from the Telecom Paris Tech (France) and an M.Sc in Applied Probability from Paris VI University (France) in 2001. He recieved the Ph.D. degree in signal processing from the Telecom Paris Tech in 2005
RESEARCH INTERESTS
Signal Processing, Machine Learning, Compressed Sensing, Applied Statistics, Biomedical applications, Nuclear Spectroscopy
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Yael Tzror, Mark Bezner, Shani Deri, Tom Trigano, and Kfir Ben-Harush
Elsevier BV
Tom Trigano and Dima Bykhovsky
Institute of Electrical and Electronics Engineers (IEEE)
Tom Trigano, Shlomi Talala, and David Luengo
Institute of Electrical and Electronics Engineers (IEEE)
Standard recordings of electrocardiograhic signals are contaminated by a large variety of noises and interferences, which impair their analysis and the further related diagnosis. In this paper, we propose a method, based on compressive sensing techniques, to remove the main noise artifacts and to locate the main features of the pulses in the electrocardiogram (ECG). The motivation is to use Trend Filtering with a varying proximal parameter, in order to sequentially capture the peaks of the ECG, which have different functional regularities. The practical implementation is based on an adaptive version of the ADMM (alternating direction method of multiplier) algorithm. We present results obtained on simulated signals and on real data illustrating the validity of this approach, showing that results in peak localization are very good in both cases and comparable to state of the art approaches.
T Trigano and Z Fradkin
IOP Publishing
Abstract The ability of peptide trapping in an electrostatic ion beam trap (EIBT) is used for the measurement of renin substrate lifetime dependence from the pressure. The time decay estimation is traditionally obtained by optimization of nonlinear curve-fitting in the least-squares sense. This paper presents a novel algorithm to address this problem, using a numerical differentiation method as the basis for lifetime estimation. Simulations results show that the proposed method provides results similar to those obtained with the classical approach, but is faster by about two orders of magnitude. An experimental result is detailed, which shows the adequacy of this algorithm for the real-life monitoring of decay measurements, not only for EIBT, but also for other processes such as luminescence where exponential decay is involved.
Zikang Chen, Xiangcong Kong, Xiaoying Zheng, Yongxin Zhu, and Tom Trigano
Springer Nature Switzerland
Tom Trigano and David Luengo
Elsevier BV
David Luengo, Albert Treytl, Stephanie Nestawal, Peter Arras, Kinga Korniejenko, Galyna Tabunshchyk, and Tom Trigano
IEEE
By definition, a smart, sustainable city is technologically enabled, connected and agile to address economic, environmental and social challenges. The success of a smart city, however, also relies on the competencies of its citizens, suggesting a re-modelling of engineering education towards a learner-centred, competency-based education. This paper gives an overview of the challenges encountered throughout implementing the BIOART multinational curriculum development project. A project that tackled the challenges for an inclusive, resilient smart city concept by developing novel teaching and learning content and adopting innovative educational methodologies in biomedical engineering, a.o. in the context of an internationally organised summer school and online hackathon.
David Luengo, Javier Via, and Tom Trigano
IEEE
In this paper, we describe an efficient iterative algorithm for finding sparse solutions to a linear system. Apart from the well-known L1 norm regularization, we introduce an additional cost term promoting solutions without too-close activations. This additional term, which is expressed as a sum of cross-products of absolute values, makes the problem non-convex and difficult to solve. However, the application of the successive convex approximations approach allows us to obtain an efficient algorithm consisting in the solution of a sequence of iteratively reweighted LASSO problems. Numerical simulations on randomly generated waveforms and ECG signals show the good performance of the proposed method.
Maayan Khayat, Shani Deri, David Wolf, Tom Trigano, Ohad Medalia, and Kfir Ben-Harush
Elsevier BV
T. Trigano and Y. Bechor
Springer Science and Business Media LLC
Tom Trigano, Shira Vaknin, and David Luengo
Elsevier BV
David Meltzer, David Luengo, and Tom Trigano
Springer International Publishing
David Luengo, David Meltzer, and Tom Trigano
MDPI AG
The electrocardiogram (ECG) was the first biomedical signal for which digital signal processing techniques were extensively applied. By its own nature, the ECG is typically a sparse signal, composed of regular activations (QRS complexes and other waveforms, such as the P and T waves) and periods of inactivity (corresponding to isoelectric intervals, such as the PQ or ST segments), plus noise and interferences. In this work, we describe an efficient method to construct an overcomplete and multi-scale dictionary for sparse ECG representation using waveforms recorded from real-world patients. Unlike most existing methods (which require multiple alternative iterations of the dictionary learning and sparse representation stages), the proposed approach learns the dictionary first, and then applies a fast sparse inference algorithm to model the signal using the constructed dictionary. As a result, our method is much more efficient from a computational point of view than other existing algorithms, thus becoming amenable to dealing with long recordings from multiple patients. Regarding the dictionary construction, we located first all the QRS complexes in the training database, then we computed a single average waveform per patient, and finally we selected the most representative waveforms (using a correlation-based approach) as the basic atoms that were resampled to construct the multi-scale dictionary. Simulations on real-world records from Physionet’s PTB database show the good performance of the proposed approach.
Tom Trigano and Yann Sepulcre
Elsevier BV
David Luengo, David Meltzer, and Tom Trigano
IEEE
The electrocardiogram (ECG) was the first biomedical signal where digital signal processing techniques were extensively applied. By its own nature, the ECG is typically a sparse signal, composed of regular activations (the QRS complexes and other waveforms like the P and T waves) and periods of inactivity (corresponding to isoelectric intervals like the PQ or ST segments), plus noise and interferences. In this work, we show how to construct a realistic multi-scale dictionary using waveforms recorded from realworld patients and how to apply this dictionary to obtain a sparse representation of ECG signals. Simulations on realworld records from Physionet show the good performance of the proposed approach.
Dima Bykhovsky and Tom Trigano
World Scientific Pub Co Pte Lt
The generation of non-Gaussian random processes with a given autocorrelation function (ACF) is addressed. The generation is based on a compound process with two components. Both components are solutions of appropriate stochastic differential equations (SDEs). One of the components is a Gaussian process and the other one is non-Gaussian with an exponential ACF. The analytical study shows that a compound combination of these processes may be used for the generation of a non-Gaussian random process with a required ACF. The results are verified by two numerical examples.
Tom Trigano, Igor Shevtsov, and David Luengo
Elsevier BV
Tom Trigano and Jonas Cohen
Institute of Electrical and Electronics Engineers (IEEE)
One of the main measurements performed in a nuclear spectroscopy experiment is the activity of an unknown radioactive source. The use of digital apparatus and physical perturbations, known as pile-up effect, make this measurement difficult when the activity of the source is high. In recent contributions, the use of compressive sensing methods yielded good estimates of this activity. This letter presents an improvement of a previously described method. It takes into account the fact that the signal used for the activity estimation is sampled, and introduces another plug-in estimation to counterbalance the bias introduced by the sampling. Results on simulations and real data validate the proposed approach, but illustrate that a good fit between the dictionary used and the signal at hand is required.
Thomas Trigano, Yann Sepulcre, and Yaracov Ritov
Institute of Electrical and Electronics Engineers (IEEE)
One of the main objectives of nuclear spectroscopy is the estimation of the counting rate of unknown radioactive sources. Recently, we proposed an algorithm based on a sparse reconstruction of the time signal in order to estimate precisely this counting rate, under the assumption that it remained constant over time. Computable bounds were obtained to quantify the performances. This approach, based on a postprocessed approach of a non-negative sparse regression of the time signal, performed well even when the activity of the source was high. The purpose of this paper is to present an extension of the previous method for an activity varying over time. It relies on the same preliminary sparse reconstruction. However, the postprocessed and plug-in steps are made differently to fit the nonhomogeneous framework. The adapted bounds are presented, and results on simulations illustrate the advantages and limitations of this method.
Tom Trigano, Ilia Gildin, and Yann Sepulcre
Institute of Electrical and Electronics Engineers (IEEE)
Every radioactive source can be characterized by an histogram obtained after collecting the energies of photons emitted from the source, also called energy spectrum. However, when the activity of this source is high, a physical phenomenon known as the pile-up effect distorts direct measurements, resulting in a significant distortion of the energy spectrum. We suggest in this letter an iterative algorithm to attenuate the pile-up effect and enhance the resulting energy spectra. It is based on iterations of a post-processed, non-negative, version of the Least Absolute Shrinkage and Selection Operator (LASSO). Results on simulations and real data illustrate the improvement obtained by the proposed method.
T. Trigano, E. Barat, T. Dautremer, and T. Montagu
Institute of Electrical and Electronics Engineers (IEEE)
This letter considers a problem stemming from the analysis of spectrometric data. When performing experiments on highly radioactive matter, electrical pulses recorded by the spectrometer tend to overlap, thus yielding severe distortions when computing the histogram of the pulses' energies. In this letter, we propose a fast recursive algorithm which estimates efficiently this histogram from measurements of the duration and energies of overlapping pulses. Its good performances are shown both on simulations and real data. Furthermore, its lower algorithmic complexity makes it more fitting for real-time implementation.
David Luengo, Sandra Monzón, Tom Trigano, Javier Vía, and Antonio Artés-Rodríguez
IOS Press
The problem of blind sparse analysis of electrogram (EGM) signals under atrial fibrillation (AF) conditions is considered in this paper. A mathematical model for the observed signals that takes into account the multiple foci typically appearing inside the heart during AF is firstly introduced. Then, a reconstruction model based on a fixed dictionary is developed and several alternatives for choosing the dictionary are discussed. In order to obtain a sparse solution, which takes into account the biological restrictions of the problem at the same time, the paper proposes using a Least Absolute Shrinkage and Selection Operator (LASSO) regularization followed by a post-processing stage that removes low amplitude coefficients violating the refractory period characteristic of cardiac cells. Finally, spectral analysis is performed on the clean activation sequence obtained from the sparse learning stage in order to estimate the number of latent foci and their frequencies. Simulations on synthetic signals and applications on real data are provided to validate the proposed approach.
David Luengo, Javier Via, Sandra Monzon, Tom Trigano, and Antonio Artes-Rodriguez
IEEE
Negative co-occurrence is a common phenomenon in many signal processing applications. In some cases the signals involved are sparse, and this information can be exploited to recover them. In this paper, we present a sparse learning approach that explicitly takes into account negative co-occurrence. This is achieved by adding a novel penalty term to the LASSO cost function based on the cross-products between the reconstruction coefficients. Although the resulting optimization problem is non-convex, we develop a new and efficient method for solving it based on successive convex approximations. Results on synthetic data, for both complete and overcomplete dictionaries, are provided to validate the proposed approach.
Yann Sepulcre, Thomas Trigano, and Ya'acov Ritov
Institute of Electrical and Electronics Engineers (IEEE)
We consider the counting rate estimation of an unknown radioactive source, which emits photons at times modeled by an homogeneous Poisson process. A spectrometer converts the energy of incoming photons into electrical pulses, whose number provides a rough estimate of the intensity of the Poisson process. When the activity of the source is high, a physical phenomenon known as pileup effect distorts direct measurements, resulting in a significant bias to the standard estimators of the source activities used so far in the field. We show in this paper that the problem of counting rate estimation can be interpreted as a sparse regression problem. We suggest a post-processed, non-negative, version of the Least Absolute Shrinkage and Selection Operator (LASSO) to estimate the photon arrival times. The main difficulty in this problem is that no theoretical conditions can guarantee consistency in sparsity of LASSO, because the dictionary is not ideal and the signal is sampled. We therefore derive theoretical conditions and bounds which illustrate that the proposed method can none the less provide a good, close to the best attainable, estimate of the counting rate activity. The good performances of the proposed approach are studied on simulations and real datasets.