Výsledky vyhledávání - "Carlos D'Giano"

Akademický článek

Statistical Model-Based Classification to Detect Patient-Specific Spike-and-Wave in EEG Signals

Autor: Antonio Quintero-Rincón, Valeria Muro, Carlos D’Giano, Jorge Prendes, Hadj Batatia

Publikováno v: Computers, Vol 9, Iss 4, p 85 (2020)

Spike-and-wave discharge (SWD) pattern detection in electroencephalography (EEG) is a crucial signal processing problem in epilepsy applications. It is particularly important for overcoming time-consuming, difficult, and error-prone manual analysis o

Externí odkaz: https://doaj.org/article/88719ef8d0b24a24960c3b108fb37871

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A quadratic linear-parabolic model-based EEG classification to detect epileptic seizures

Autor: Hadj Batatia, Carlos D'Giano, Antonio Quintero-Rincón

Publikováno v: The Journal of Biomedical Research
The Journal of Biomedical Research, 2020, 34 (3), pp.203-210. ⟨10.7555/JBR.33.20190012⟩
Journal of Biomedical Research

The two-point central difference is a common algorithm in biological signal processing and is particularly useful in analyzing physiological signals. In this paper, we develop a model-based classification method to detect epileptic seizures that reli

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::74151d437b797f3183fdca339a22aadc
https://oatao.univ-toulouse.fr/26050/

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Predicción de crisis epilépticas utilizando el coeficiente de correlación producto-momento de Pearson a partir de un clasificador lineal de la distribución Gaussiana generalizada

Autor: Carlos D'Giano, Marcelo Risk, Antonio Quintero-Rincón

Publikováno v: Neurología Argentina. 10:210-217

Predecir una crisis epiléptica significa la capacidad de determinar de antemano el momento de una crisis con la mayor precisión posible. Un pronóstico correcto de un evento epiléptico en aplicaciones clínicas es un problema típico en procesamie

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::95b2d0c3e3653bef2af4d52b40c5a3e7
https://doi.org/10.1016/j.neuarg.2018.06.004

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Bayesian EEG source localization using a structured sparsity prior

Autor: Thomas Oberlin, Jean-Yves Tourneret, Carlos D'Giano, Facundo Costa, Hadj Batatia

Publikováno v: NeuroImage
NeuroImage, Elsevier, 2017, Vol. 144 (Part. A), pp. 142-152. ⟨10.1016/j.neuroimage.2016.08.064⟩

International audience; This paper deals with EEG source localization. The aim is to perform spatially coherent focal localization and recover temporal EEG waveforms, which can be useful in certain clinical applications. A new hier- archical Bayesian

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a9a081bc2f8929454d77def43163b313
https://doi.org/10.1016/j.neuroimage.2016.08.064

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Seizure Onset Detection in EEG Signals Based on Entropy from Generalized Gaussian PDF Modeling and Ensemble Bagging Classifier

Autor: Hadj Batatia, Antonio Quintero Rincón, Carlos D'Giano

Publikováno v: Advances in Predictive, Preventive and Personalised Medicine ISBN: 9783030117993

This paper proposes a new algorithm for epileptic seizure onset detection in EEG signals. The algorithm relies on the measure of the entropy of observed data sequences. Precisely, the data is decomposed into different brain rhythms using wavelet mult

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e8614bd1f1fe8038118390dbf9dd55b1
https://oatao.univ-toulouse.fr/24878/

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Statistical Model-Based Classification to Detect Patient-Specific Spike-and-Wave in EEG Signals

Autor: Valeria Muro, Hadj Batatia, Antonio Quintero Rincón, Carlos D'Giano, Jorge Prende

Publikováno v: Computers Vol.9, No.4, 2020
Repositorio Institucional (UCA)
Pontificia Universidad Católica Argentina
instacron:UCA
Computers
Computers, MDPI, 2020, 9(4) (85), pp.1-14. ⟨10.3390/computers9040085⟩
Volume 9
Issue 4
Computers, Vol 9, Iss 85, p 85 (2020)

Fil: Quintero-Rincón, Antonio. Pontificia Universidad Católica Argentina; Argentina Fil: Quintero-Rincón, Antonio. Fleni. Fundación para la Lucha contra la Enfermedad Neurológica Pediátrica; Argentina Fil: Muro, Valeria. Fleni. Fundación para

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::516099c5000b75f8d768aaf774a9a2f0
https://doi.org/10.3390/computers9040085

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Fast statistical model-based classification of epileptic EEG signals

Autor: Marcelo Pereyra, Hadj Batatia, Marcelo Risk, Antonio Quintero-Rincón, Carlos D'Giano

Publikováno v: Biocybernetics and Biomedical Engineering
Biocybernetics and Biomedical Engineering, 2018, 38 (4), pp.877-889

This paper presents a supervised classification method to accurately detect epileptic brain activity in real-time from electroencephalography (EEG) data. The proposed method has three main strengths: it has low computational cost, making it suitable

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6082259d8f8cb4250c96daf8bfb74752
http://www.sciencedirect.com/science/article/pii/S0208521618301219

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Study on spike-and-wave detection in epileptic signals using t-location-scale distribution and the k-nearest neighbors classifier

Autor: Jorge Prendes, Antonio Quintero-Rincón, Valeria Muro, Carlos D'Giano

Publikováno v: Proceedings of 2017 IEEE URUCON
URUCON, 2017 IEEE
URUCON, 2017 IEEE, Oct 2017, Montevideo, Uruguay. pp.125-128

International audience; Pattern classification in electroencephalography (EEG) signals is an important problem in biomedical engineering since it enables the detection of brain activity, in particular the early detection of epileptic seizures. In thi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dd64b1f2b2e277dc58ed7cc44335bc1d
https://hal.archives-ouvertes.fr/hal-02134635

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A visual EEG epilepsy detection method based on a wavelet statistical representation and the Kullback-Leibler divergence

Autor: Carlos D'Giano, Antonio Quintero-Rincón, Marcelo Pereyra, Marcelo Risk, Hadj Batatia

Publikováno v: VII Latin American Congress on Biomedical Engineering CLAIB 2016, Bucaramanga, Santander, Colombia, October 26th-28th, 2016 ISBN: 9789811040856
VII Latin American Congress on Biomedical Engineering CLAIB 2016
Latin American Congress on Biomedical Engineering
Latin American Congress on Biomedical Engineering, Oct 2016, Santander, Colombia. pp.13--16, ⟨10.1007/978-981-10-4086-3_4⟩

This paper presents a statistical signal processing method for the characterization of EEG of patients suffering from epilepsy. A statistical model is proposed for the signals and the Kullback-Leibler divergence is used to study the differences betwe

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ce6b45c00c9b5f86d4a7463936b69fa7
https://doi.org/10.1007/978-981-10-4086-3_4

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A new algorithm for epilepsy seizure onset detection and spread estimation from EEG signals

Autor: Antonio Quintero-Rincón, Carlos D'Giano, Marcelo Pereyra, Hadj Batatia, Marcelo Risk

Publikováno v: Journal of Physics: Conference Series
Journal of Physics: Conference Series, IOP Publishing, 2016, 20th Argentinean Bioengineering Society Congress (SABI 2015), 705 (1), pp.12--32. ⟨10.1088/1742-6596/705/1/012032⟩
CONICET Digital (CONICET)
Consejo Nacional de Investigaciones Científicas y Técnicas
instacron:CONICET
Quintero-Rincón, A, Pereyra, M A, D'Giano, C, Batatia, H & Risk, M 2016, ' A new algorithm for epilepsy seizure onset detection and spread estimation from EEG signals ', Journal of Physics: Conference Series, vol. 705, no. 1, 012032 . https://doi.org/10.1088/1742-6596/705/1/012032

Appropriate diagnosis and treatment of epilepsy is a main public health issue. Patients suffering from this disease often exhibit different physical characterizations, which result from the synchronous and excessive discharge of a group of neurons in

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::83d32e5c38018e38b0f7f56cab3328c1
https://hal.archives-ouvertes.fr/hal-03172254

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