Classificação de sinais de eletroencefalograma usando máquinas de vetores suporte

AUTOR(ES)
DATA DE PUBLICAÇÃO

2009

RESUMO

Electroencephalogram (EEG) is a clinical method widely used to study brain function and neurological disorders. The EEG is a temporal data series which records the electrical activity of the brain. The EEG monitoring systems create a huge amount of data; with this fact a visual analysis of the EEG is not feasible. Because of this, there is a strong demand for computational methods able to analyze automatically the EEG records and extract useful information to support the diagnostics. Herewith, it is necessary to design a tool to extract the relevant features within the EEG record and to classify the EEG based on these features. Calculation of statistics over wavelet coefficients are being used successfully to extract features from many kinds of temporal data series, including EEG signals. Support Vector Machines (SVM) are machine learning techniques with high generalization ability, and they have been successfully used in classification problems by several researches. This dissertation makes an analysis of the influence of feature vectors based on wavelet coefficients in the classification of EEG signal using different implementations of SVMs.

ASSUNTO(S)

temporal data series wavelet engenharia eletrica support vector machine (svm) extração de características feature extraction eletroencefalogram (eeg) séries temporais wavelet máquina de vetor de suporte (svm) eletroencefalograma (eeg)

ACESSO AO ARTIGO

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