Classification Epileptic Seizures in EEG Using Time-Frequency Image and Block Texture Features

• Published in: IEEE access Vol. 8; pp. 9770 - 9781
• Main Authors: Li, Mingyang, Sun, Xiaoying, Chen, Wanzhong, Jiang, Yun, Zhang, Tao
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Blocking; Brain modeling; Diagnosis; EEG; Electroencephalography; Entropy; Epilepsy; Feature extraction; Fourier transforms; Frequency ranges; Image classification; kernel entropy component analysis; Medical imaging; multi-scale blocking; Optimization; Physicians; quadratic feature selection; Seizure; Seizures; Support vector machines; Texture; texture features; Time-frequency analysis; two-dimensional time-frequency image; Workflow
• DOI: 10.1109/ACCESS.2019.2960848
• ISSN: 2169-3536

With the rapid development in technology, computer aided detection or diagnosis has become an indispensable part of the medical industry. Automatic detection of epileptic events is one of the important subjects that have aroused wide interest from more and more investigators. This paper proposes a new model in classification of multi-category electroencephalogram (EEG) signals using time-frequency image and block texture features. The one-dimensional EEG is first mapped to time-frequency domain by means of short-time Fourier transform (STFT), which is adapted to obtain a two-dimensional time-frequency image (2D-TFI). With the idea of multi-scale blocking, the obtained phase images and amplitude images are divided into several sub-blocks corresponding to different frequency ranges and time periods. Then the texture features are calculated to describe the behaviour of EEG signals. Particularly, a novel quadratic feature selection method based on kernel entropy component analysis (KECA) and Kruskal-Wallis test (KW) has been proposed for dimension reduction, by which the features that contained most distinctive information were provided. Eventually, the optimal KECA-based features are fed to support vector machine (SVM) for deciding the class of corresponding EEG. The proposed model is found to achieve at least 99.30% accuracy, 98.0% sensitivity and 100% specificity for each of the eight clinical problems. Our scheme is proven to be effective for seizure detection, which can help doctors optimize the diagnosis workflow, reduce workload and improve detection precision.