Automated focal EEG signal detection based on third order cumulant function

被引:35
|
作者
Sharma, Rahul [1 ]
Sircar, Pradip [1 ]
Pachori, Ram Bilas [2 ]
机构
[1] Indian Inst Technol Kanpur, Dept Elect Engn, Kanpur 208016, UP, India
[2] Indian Inst Technol Indore, Discipline Elect Engn, Indore 453552, MP, India
关键词
EEG; Non-focal and focal; Higher-order statistics; LSDA; Kemal; SVM; CLASSIFICATION; FEATURES; EMD;
D O I
10.1016/j.bspc.2020.101856
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Epilepsy is a chronic neurological disorder which occurs due to recurrent seizures. The epilepsy surgery is the only cure of epileptic seizures as it cannot be controlled with medication. Hence, it becomes the primary task to localize the epileptogenic zone for successful epilepsy surgery. The epileptic surgical area can be recognized by the focal intracranial electroencephalogram (EEG) signals. In this paper, a nonlinear third-order cumulant has been proposed for the classification of the non-focal and focal intracranial EEG signals efficiently. The attributes are measured from the logarithm of the diagonal slice of third-order cumulant. It provides relevant subtle information about the nonlinear dynamics of EEG signals. A data reduction technique, locality sensitive discriminant analysis (LSDA), has been introduced to map the measured features at higher dimensional space and ranked them according to the probability of discrimination. The achieved results reveal that the ranked LSDA features with the support vector machine (SVM) classifier have yielded maximum classification accuracy of 99% on the Bern Barcelona EEG database. Thus, the proposed algorithm helps a clinician to localize the epileptogenic zone for successful brain surgery. (C) 2020 Published by Elsevier Ltd.
引用
收藏
页数:8
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