Publicação
Classification of alzheimer’s electroencephalograms using artificial neural networks and logistic regression
| Resumo: | The Artificial Neural Networks have been used over the years to solve complex problems and their development has strongly grown in recent years. In particular, this work, focused on the development and a comparison between Artificial Neural Networks (ANN) and a traditional statistical technic known as Logistic Regression (LR) in Electroencephalogram (EEG) classification. The Wavelet Transform was seen as the main technique of signal processing, in order to analyze the EEG signals of this study. Some features were extracted by the EEG signals like relative power (RP) in conventional frequency bands and two spectral ratios. The best feature combination was selected by Principal Components Analysis method to increase the accuracy of the ANN and LR to discriminate their entries between Alzheimer Disease and Controls. |
|---|---|
| Autores principais: | Rodrigues, Pedro Miguel |
| Outros Autores: | Teixeira, João Paulo; Hornero, Roberto; Poza, Jesús; Carreres, Alicia |
| Assunto: | EEG Logistic regression Classification Wavelet transform Artificial neural networks |
| Ano: | 2011 |
| País: | Portugal |
| Tipo de documento: | comunicação em conferência |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Instituto Politécnico de Bragança |
| Idioma: | inglês |
| Origem: | Biblioteca Digital do IPB |
| Resumo: | The Artificial Neural Networks have been used over the years to solve complex problems and their development has strongly grown in recent years. In particular, this work, focused on the development and a comparison between Artificial Neural Networks (ANN) and a traditional statistical technic known as Logistic Regression (LR) in Electroencephalogram (EEG) classification. The Wavelet Transform was seen as the main technique of signal processing, in order to analyze the EEG signals of this study. Some features were extracted by the EEG signals like relative power (RP) in conventional frequency bands and two spectral ratios. The best feature combination was selected by Principal Components Analysis method to increase the accuracy of the ANN and LR to discriminate their entries between Alzheimer Disease and Controls. |
|---|