Wavelet applications in medicine
IEEE Spectrum
Classification of EEG signals using the wavelet transform
Signal Processing
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Features extracted by eigenvector methods for detecting variability of EEG signals
Pattern Recognition Letters
Analysis of EEG signals by combining eigenvector methods and multiclass support vector machines
Computers in Biology and Medicine
Wavelet/mixture of experts network structure for EEG signals classification
Expert Systems with Applications: An International Journal
Statistics over features for internal carotid arterial disorders detection
Computers in Biology and Medicine
Decision support systems for time-varying biomedical signals: EEG signals classification
Expert Systems with Applications: An International Journal
Analysis of EEG signals by implementing eigenvector methods/recurrent neural networks
Digital Signal Processing
Combined neural network model employing wavelet coefficients for EEG signals classification
Digital Signal Processing
Recurrent neural networks employing Lyapunov exponents for EEG signals classification
Expert Systems with Applications: An International Journal
A novel large-memory neural network as an aid in medical diagnosis applications
IEEE Transactions on Information Technology in Biomedicine
Multiclass Support Vector Machines for EEG-Signals Classification
IEEE Transactions on Information Technology in Biomedicine
The wavelet transform, time-frequency localization and signal analysis
IEEE Transactions on Information Theory
Training feedforward networks with the Marquardt algorithm
IEEE Transactions on Neural Networks
Computers in Biology and Medicine
A tunable support vector machine assembly classifier for epileptic seizure detection
Expert Systems with Applications: An International Journal
Feature-based Type Identification of File Fragments
Security and Communication Networks
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This paper presented the usage of statistics over the set of the features representing the electroencephalogram (EEG) signals. Since classification is more accurate when the pattern is simplified through representation by important features, feature extraction and selection play an important role in classifying systems such as neural networks. Multilayer perceptron neural network (MLPNN) architectures were formulated and used as basis for detection of electroencephalographic changes. Three types of EEG signals (EEG signals recorded from healthy volunteers with eyes open, epilepsy patients in the epileptogenic zone during a seizure-free interval, and epilepsy patients during epileptic seizures) were classified. The selected Lyapunov exponents, wavelet coefficients and the power levels of power spectral density (PSD) values obtained by eigenvector methods of the EEG signals were used as inputs of the MLPNN trained with Levenberg-Marquardt algorithm. The classification results confirmed that the proposed MLPNN has potential in detecting the electroencephalographic changes.