Výsledky vyhledávání - "Geovani R. Scolaro"

Comparison between Artificial Neural Networks and Discriminant Functions for Automatic Detection of Epileptiform Discharges

Autor: Geovani R. Scolaro, Christine Fredel Boos, F. M. Azevedo

Publikováno v: IFMBE Proceedings ISBN: 9783319008455

This study presents the performance analysis between two classifiers when they are used together with mimetic analysis based morphological features to develop a method for automatic detection of epileptiform discharges in EEG signals. We applied mime

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::18e6b94f0938ba7b5e945a1640a35868
https://doi.org/10.1007/978-3-319-00846-2_191

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Wavelet Filter to Attenuate the Background Activity and High Frequencies in EEG Signals Applied in the Automatic Identification of Epileptiform Events

Autor: Fernando Mendes de Azevedo, Roger Walz, Geovani R. Scolaro, Christine Fredel Boos

Publikováno v: Practical Applications in Biomedical Engineering

© 2012 Scolaro et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproducti

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0a16f831e3542767d048c05e83886598
https://doi.org/10.5772/53585

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Analysis of Pre-Processing Methods for Artificial Neural Network Pattern Recognition of EEG Signals

Autor: F. M. Azevedo, Geovani R. Scolaro, Christine Fredel Boos, M. C. V. Pereira

Publikováno v: IFMBE Proceedings ISBN: 9783642293047

Artificial Neural Networks (ANN) are a common tool for pattern recognition in applications using bioelectrical signals. The different ways that these signals are presented as input stimuli of the neural networks have a direct influence on their perfo

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::3f34bf8cda866cc67d33ba5577f1e54e
https://doi.org/10.1007/978-3-642-29305-4_146

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Wavelet Filter Proposal to Attenuate the Background Activity and High Frequencies in EEG Signals

Autor: Fernando M. Azevedo, Geovani R. Scolaro, Christine Fredel Boos

Publikováno v: Biomedical Engineering.

This paper proposes a digital filter based on wavelet transform and performs an investigation to find an appropriate wavelet function for filtering epileptiform events in EEG signals using the wavelet multiresolution analysis. We investigated five kn

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::81596018be75dd2b96a0a5279c518729
https://doi.org/10.2316/p.2013.791-174

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Evaluation of Different Wavelet Functions Applied in the Development of Digital Filters to Attenuate the Background Activity in EEG Signals

Autor: F. M. Azevedo, Geovani R. Scolaro, Christine Fredel Boos

Publikováno v: IFMBE Proceedings ISBN: 9783642293047

In this paper is presented a study where were evaluated five families of Wavelet function (Coiflets, Daube-chies, Symlets, Biorthogonal and Reverse Biorthogonal) totalizing 65 evaluated functions and were used 500 epileptiform events for all the expe

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::f4ef83114b7a160fefefe72b55ed7302
https://doi.org/10.1007/978-3-642-29305-4_91

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Automatic Detection of Paroxysms in EEG Signals Using Morphological Descriptors and Artificial Neural Networks

Autor: Maria do Carmo Vitarelli Pereira, Fernando M. de Azevedo Geovani R. Scolaro, Christine Fredel Boos

Publikováno v: Biomedical Engineering, Trends in Electronics, Communications and Software

The first recordings of brain activity in the form of electrical signals, through the use of a galvanometer and insertion of two electrodes on the scalp of an individual, were conducted in 1875 by British scientist Richard Caton. Since then, the elec

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::990c0dafd779e4a04843ec5a721b1d33
https://doi.org/10.5772/13444

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Classification of epileptiform events in raw EEG signals using neural classifier

Autor: Fernando Mendes de Azevedo, Geovani R. Scolaro

Publikováno v: 2010 3rd International Conference on Computer Science and Information Technology.

This study evaluated the capability of neural classifier to perform the separation between epileptiform and non-epileptiform events. To processing the EEG signals was used the Wavelet Transform through the use of the Coiflet1 function. The main eleme

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::ce621c59c3359f1496500752517c8416
https://doi.org/10.1109/iccsit.2010.5563949

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Neonatal Respiratory Monitoring System

Autor: J. Oliveira, Vander Vigolo, M. A. B. Rodrigues, C. J. E. Montero, Geovani R. Scolaro, Dayane Luiza Fioreze Scolaro

Publikováno v: World Congress on Medical Physics and Biomedical Engineering 2006 ISBN: 9783540368397
Scopus-Elsevier

This article describes the adopted procedures for the development of a system for neonatal respiratory monitoring, that consists of one or more devices linked to an IMBPC computer, where in this work they were just used four. It also consists of a pr

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dee1e76ae65dd65435a48e1ac10d4a05
https://doi.org/10.1007/978-3-540-36841-0_183

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