A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Autoassociative MLP in Sleep Spindle Detection
Journal of Medical Systems
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Combined neural network model employing wavelet coefficients for EEG signals classification
Digital Signal Processing
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Analysis and interpretation of electroencephalogram signals have found a wide spectrum of applications in clinical diagnosis. In spite of the outstanding experience of specialists, the analysis of biomedical data encounters many difficulties. Problems are associated with both technical aspects and nonstationary character of EEG sequences. Hardware and software solutions in this area are subjected to the continuous improvement due to the technological development. A very promising tool in analysis and interpretation of EEG signals are artificial neural networks. The paper presents the application of artificial neural networks along with the discrete wavelet transform to the analysis and classification of neurological disorders based on recorded EEG signals.