Automated Feature Extraction of Epileptic EEG Using Approximate Entropy

被引：0
|
作者：
Kale, Kirti K. [1 ]
Gawande, J. P. [1 ]
机构：
[1] Instrumentat & Control Cummins Coll Engn Women, Pune, Maharashtra, India
来源：
2012 12TH INTERNATIONAL CONFERENCE ON HYBRID INTELLIGENT SYSTEMS (HIS) | 2012年
关键词：
Electroencephalogram (EEG); approximate entropy (ApEn); epilepsy; standard deviation (SD);
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The disease epilepsy is characterized by a sudden and recurrent malfunction of the brain that is termed seizer. The electroencephalogram (EEG) signals play an important role in the diagnosis of epilepsy. Nonlinear analysis quantifies the EEG signal to address randomness and predictability of brain activity. In this study we evaluate differences between epileptic EEG and normal EEG by computing Approximate Entropy (ApEn). The methodology is applied to two different EEG signals: 1) Normal 2) Epileptic. ApEn were calculated. The effectiveness of ApEn in comparison between two signals is investigated. It is observed that values of ApEn drops during an epileptic seizures.
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页码：474 / 477
页数：4
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