Detection of focal electroencephalogram signals using higher-order moments in EMD-TKEO domain

Healthcare Technology Letters

article

Soumya Chatterjee¹ 

[1] Department of Electrical Engineering, Jadavpur University

关键词: brain;    support vector machines;    electroencephalography;    feature extraction;    medical signal processing;    medical disorders;    medical signal detection;    signal classification;    focal electroencephalogram signals;    higher-order moments;    EMD-TKEO domain;    epileptogenic focus;    electroencephalogram signal screening;    important pre-surgical step;    human brain;    empirical mode decomposition;    Teager–Kaiser energy operator;    nonfocal groups;    intrinsic mode functions;    IMF;    higher-order statistical moments;    focal seizures;    proposed EMD-TKEO based feature extraction method;    radial basis kernel function;    support vector machine classifier;    high discriminative capability;    statistical test;    statistical significance;    kurtosis;    skewness;   

DOI  :  10.1049/htl.2018.5036

学科分类：肠胃与肝脏病学

来源: Wiley

【 摘 要 】

Detection of epileptogenic focus based on electroencephalogram (EEG) signal screening is an important pre-surgical step to remove affected regions inside the human brain. Considering the fact above, in this work, a novel technique for detection of focal EEG signals is proposed using a combination of empirical mode decomposition (EMD) and Teager–Kaiser energy operator (TKEO). EEG signals belonging to focal (Fo) and non-focal (NFo) groups were at first decomposed into a set of intrinsic mode functions (IMFs) using EMD. Next, TKEO was applied on each IMF and two higher-order statistical moments namely skewness and kurtosis were extracted as features from TKEO of each IMF. The statistical significance of the selected features was evaluated using student's t -test and based on the statistical test, features from first three IMFs which show very high discriminative capability were selected as inputs to a support vector machine classifier for discrimination of Fo and NFo signals. It was observed that the classification accuracy of 92.65% is obtained in classifying EEG signals using a radial basis kernel function, which demonstrates the efficacy of proposed EMD-TKEO based feature extraction method for computer-based treatment of patients suffering from focal seizures.

【 授权许可】

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