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Advisor(s)
Abstract(s)
Este trabalho tem como objetivo a detecção da doença de Alzheimer (DA) através de parâmetros não-lineares de sinais de fala. Os parâmetros são extraídos de sub-bandas dos sinais, as quais são obtidas por meio da transformada Wavelet, e algumas das suas estatísticas descritivas são utilizadas como entrada para vários classificadores. Acurácias de 100, 77,8 e 85,2% são obtidas na detecção da DA entre mulheres, homens e todos, respectivamente, utilizando classificadores de regressão logística.
This work aims to detect Alzheimer’s disease (AD) through non-linear features of speech signals. The features are extracted from signal subbands, which are obtained through the wavelet transform, and some of its descriptive statistics are used as input for several classifiers. Accuracies of 100, 77.8 and 85.2% are obtained in detecting AD among women, men and all, respectively, using logistic regression classifiers.
This work aims to detect Alzheimer’s disease (AD) through non-linear features of speech signals. The features are extracted from signal subbands, which are obtained through the wavelet transform, and some of its descriptive statistics are used as input for several classifiers. Accuracies of 100, 77.8 and 85.2% are obtained in detecting AD among women, men and all, respectively, using logistic regression classifiers.
Description
Keywords
Doença de Alzheimer Sinal de fala Análise não-linear Wavelet Classificadores Alzheimer’s disease Speech signal Non-linear analysis Classifiers
Citation
Publisher
Sociedade Brasileira de Telecomunicações