Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Christine Fredel Boos"'
Autor:
Ricardo Nava de Sousa, Julia Volkmann, Cristian Ricardo Schwatz, Christine Fredel Boos, Rodrigo Koerich Decker, Jonathan Utzig, Henry França Meier
Publikováno v:
Lecture Notes in Mechanical Engineering ISBN: 9783031259890
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a32beb5d88ca327c27de72afe9b07e0f
https://doi.org/10.1007/978-3-031-25990-6_12
https://doi.org/10.1007/978-3-031-25990-6_12
Publikováno v:
AFRICON
Chronic Obstructive Pulmonary Disease (COPD) is characterized by progressive and partially reversible airway obstruction. Gait is often used to analyze and monitor the progression of diseases that interfere with the posture and locomotion such as COP
Publikováno v:
2015 Twelve International Conference on Electronics Computer and Computation (ICECCO).
Among the all of the different Artificial Intelligence tools, Artificial Neural Networks (ANN) are widely used for automated pattern recognition and classification process. One of its many applications in Biomedical Engineering is its use in the deve
Publikováno v:
2015 International Conference on Electrical Engineering and Information Communication Technology (ICEEICT).
Analysis of long-term electroencephalogram signals (EEG) is an important tool to clinically confirm the diagnosis of epilepsy. The characteristic electrographic events that represent epilepsy in the analysis of EEG are called epileptiform events (spi
Publikováno v:
2015 International Conference on BioSignal Analysis, Processing and Systems (ICBAPS).
This paper conducts a study on the use of Wavelet Scalograms as input of Artificial Neural Networks (ANN) for the automatic detection of epileptiform events in EEG signals. For this purpose, two Wavelet families, Daubechies and Coiflet, were tested t
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
https://doi.org/10.1007/978-3-319-00846-2_191
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
https://doi.org/10.5772/53585
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
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
https://doi.org/10.1007/978-3-642-29305-4_146
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
https://doi.org/10.1007/978-3-642-29305-4_91