Comput Methods Programs Biomed - A hybrid system based on information gain and principal component analysis for the classification of transcranial Doppler signals.

Tópicos

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Resumo

A transcranial Doppler (TCD) is a non-invasive, easy to apply and reliable technique which is used in the diagnosis of various brain diseases by measuring the blood flow velocities in brain arteries. This study aimed to classify the TCD signals, and feature ranking (information gain - IG) and dimension reduction methods (principal component analysis - PCA) were used as a hybrid to improve the classification efficiency and accuracy. In this context, each feature within the feature space was ranked depending on its importance for the classification using the IG method. Thus, the less important features were ignored and the highly important features were selected. Then, the PCA method was applied to the highly important features for dimension reduction. As a result, a hybrid feature reduction between the selection of the highly important features and the application of the PCA method on the reduced features were achieved. To evaluate the effectiveness of the proposed method, experiments were conducted using a support vector machine (SVM) classifier on the TCD signals recorded from the temporal region of the brain of 82 patients, as well as 24 healthy people. The experimental results showed that using the IG and PCA methods as a hybrid improves the classification efficiency and accuracy compared with individual usage.

Resumo Limpo

transcrani doppler tcd noninvas easi appli reliabl techniqu use diagnosi various brain diseas measur blood flow veloc brain arteri studi aim classifi tcd signal featur rank inform gain ig dimens reduct method princip compon analysi pca use hybrid improv classif effici accuraci context featur within featur space rank depend import classif use ig method thus less import featur ignor high import featur select pca method appli high import featur dimens reduct result hybrid featur reduct select high import featur applic pca method reduc featur achiev evalu effect propos method experi conduct use support vector machin svm classifi tcd signal record tempor region brain patient well healthi peopl experiment result show use ig pca method hybrid improv classif effici accuraci compar individu usag

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