Comput Methods Programs Biomed - Clustering technique-based least square support vector machine for EEG signal classification.

Tópicos

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Resumo

This paper presents a new approach called clustering technique-based least square support vector machine (CT-LS-SVM) for the classification of EEG signals. Decision making is performed in two stages. In the first stage, clustering technique (CT) has been used to extract representative features of EEG data. In the second stage, least square support vector machine (LS-SVM) is applied to the extracted features to classify two-class EEG signals. To demonstrate the effectiveness of the proposed method, several experiments have been conducted on three publicly available benchmark databases, one for epileptic EEG data, one for mental imagery tasks EEG data and another one for motor imagery EEG data. Our proposed approach achieves an average sensitivity, specificity and classification accuracy of 94.92%, 93.44% and 94.18%, respectively, for the epileptic EEG data; 83.98%, 84.37% and 84.17% respectively, for the motor imagery EEG data; and 64.61%, 58.77% and 61.69%, respectively, for the mental imagery tasks EEG data. The performance of the CT-LS-SVM algorithm is compared in terms of classification accuracy and execution (running) time with our previous study where simple random sampling with a least square support vector machine (SRS-LS-SVM) was employed for EEG signal classification. We also compare the proposed method with other existing methods in the literature for the three databases. The experimental results show that the proposed algorithm can produce a better classification rate than the previous reported methods and takes much less execution time compared to the SRS-LS-SVM technique. The research findings in this paper indicate that the proposed approach is very efficient for classification of two-class EEG signals.

Resumo Limpo

paper present new approach call cluster techniquebas least squar support vector machin ctlssvm classif eeg signal decis make perform two stage first stage cluster techniqu ct use extract repres featur eeg data second stage least squar support vector machin lssvm appli extract featur classifi twoclass eeg signal demonstr effect propos method sever experi conduct three public avail benchmark databas one epilept eeg data one mental imageri task eeg data anoth one motor imageri eeg data propos approach achiev averag sensit specif classif accuraci respect epilept eeg data respect motor imageri eeg data respect mental imageri task eeg data perform ctlssvm algorithm compar term classif accuraci execut run time previous studi simpl random sampl least squar support vector machin srslssvm employ eeg signal classif also compar propos method exist method literatur three databas experiment result show propos algorithm can produc better classif rate previous report method take much less execut time compar srslssvm techniqu research find paper indic propos approach effici classif twoclass eeg signal

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