The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Classification of EEG signals using the wavelet transform
Signal Processing
Fuzzy least squares support vector machines for multiclass problems
Neural Networks - 2003 Special issue: Advances in neural networks research IJCNN'03
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
AR Spectral Analysis Technique for Human PPG, ECG and EEG Signals
Journal of Medical Systems
Measuring saliency of features representing EEG signals using signal-to-noise ratios
Expert Systems with Applications: An International Journal
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
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
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This paper presents the application of least squares support vector machines (LS-SVMs) for automatic detection of alterations in the human electroencephalogram (EEG) activities during hypopnoea episodes. The obstructive sleep apnoea hypopnoea syndrome (OSAH) means ''cessation of breath'' during the sleep hours and the sufferers often experience related changes in the electrical activity of the brain and heart. Decision making was performed in two stages: feature extraction by computation of autoregressive (AR) coefficients and classification by the LS-SVMs. The EEG signals (pre and during hypopnoea) from three electrodes (C3, C4 and O2) were used as input patterns of the LS-SVMs. The performance of the LS-SVMs was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed LS-SVM has potential in detecting changes in the human EEG activity due to hypopnoea episodes.