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

• Main Author: Soumya Chatterjee
• Format: Article
• Language: English
• Published: Wiley 2019-04-01
• Series: Healthcare Technology Letters
• Subjects: 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
• Online Access: https://digital-library.theiet.org/content/journals/10.1049/htl.2018.5036

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.