Authors:
Fernando Mendes de Azevedo
;
Geovani Rodrigo Scolaro
and
Christine Fredel Boos
Affiliation:
Biomedical Engineering Institute and University Federal of Santa Catarina, Brazil
Keyword(s):
Spikes, Sharp waves, Wavelet, Artificial neural networks, Neural classifier.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Computer Vision, Visualization and Computer Graphics
;
Data Manipulation
;
Detection and Identification
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
;
Wavelet Transform
Abstract:
In this article is discussed the application of a hybrid approach that uses the Wavelet Transform and Artificial Neural Networks in detection and recognition of epileptiform events in EEG signals. It is presented the methodology used to develop a Neural Classifier as well as the experiments and its results through the Neural Networks and Wavelet Transform. The developed Neural Classifier showed good results in the classification of Epileptiform events with and without pre-processing achieving sensitive of 97.14%, specificity of 94.55% and accuracy of 96.14%, suggesting the high sample rate of the EEG signals contributed to achieve these values.