Journal of neuroscience methods
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J. Neurosci. Methods · Oct 2005
Automatic seizure detection in EEG using logistic regression and artificial neural network.
The detection of epileptiform discharges in the EEG is an important component in the diagnosis of epilepsy. In this study, multiple signal classification (MUSIC), autoregressive (AR) and periodogram methods were used to get power spectra in patients with absence seizure. The EEG power spectra were used as an input to a classifier. ⋯ The comparisons between the developed classifiers were primarily based on analysis of the receiver operating characteristic (ROC) curves as well as a number of scalar performance measures pertaining to the classification. The MLPNN based classifier outperformed the LR based counterpart. Within the same group, the MLPNN-based classifier was more accurate than the LR-based classifier.
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J. Neurosci. Methods · Oct 2005
Characterization of a model of cutaneous inflammatory pain produced by an ultraviolet irradiation-evoked sterile injury in the rat.
Neuroimmune interactions are of known importance in the genesis and maintenance of inflammatory pain states. However, the immune response to tissue damage is likely to differ depending on whether or not the injury is accompanied by infection. Many clinically important inflammatory pain states involve a sterile tissue injury. ⋯ The animals develop heat-hyperalgesia, mechano-hyperalgesia, mechano-allodynia, and cold-allodynia that last for several days. Cold-allodynia appears within 6 h or less, but the other symptoms are not clearly evident until 12-36 h after exposure. This model offers several advantages for the experimental analysis of the causes of inflammatory allodynia and hyperalgesia.
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J. Neurosci. Methods · Oct 2005
Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients.
This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for classification of electroencephalogram (EEG) signals. Decision making was performed in two stages: feature extraction using the wavelet transform (WT) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Five types of EEG signals were used as input patterns of the five ANFIS classifiers. ⋯ The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the saliency of features on classification of the EEG signals were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in classifying the EEG signals.