In this study, we compared several classifiers for the supervised distinction between normal elderly and Alzheimer's disease individuals, based on resting state electroencephalographic markers, age, gender and education. Three main preliminary procedures served to perform features dimensionality reduction were used and discussed: a Support Vector Machines Recursive Features Elimination, a Principal Component Analysis and a novel method based on the correlation. In particular, five different classifiers were compared: two different configurations of SVM and three different optimal topologies of Error Back Propagation Multi Layer Perceptron Artificial Neural Networks (EBP MLP ANNs). Best result, in terms of classification (accuracy 86% and sensitivity 92%), was obtained by a Neural Network with 3 hidden layers that used as input: age, gender, education and 20 EEG features selected by the novel method based on the correlation.

Advanced classification of Alzheimer's disease and healthy subjects based on EEG markers

BEVILACQUA, Vitoantonio;Buongiorno, Domenico;
2015-01-01

Abstract

In this study, we compared several classifiers for the supervised distinction between normal elderly and Alzheimer's disease individuals, based on resting state electroencephalographic markers, age, gender and education. Three main preliminary procedures served to perform features dimensionality reduction were used and discussed: a Support Vector Machines Recursive Features Elimination, a Principal Component Analysis and a novel method based on the correlation. In particular, five different classifiers were compared: two different configurations of SVM and three different optimal topologies of Error Back Propagation Multi Layer Perceptron Artificial Neural Networks (EBP MLP ANNs). Best result, in terms of classification (accuracy 86% and sensitivity 92%), was obtained by a Neural Network with 3 hidden layers that used as input: age, gender, education and 20 EEG features selected by the novel method based on the correlation.
2015
International Joint Conference on Neural Networks, IJCNN 2015
978-1-4799-1959-8
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/83816
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