Detrended fluctuation analysis of EEG recordings for epileptic seizure detection

被引：0
|
作者：
Adda, A. [1 ,2 ]
Benoudnine, H. [2 ,3 ]
机构：
[1] Abdelhamid Ibn Badis Univ, Elect Engn Dept, Signal & Applicat Lab, Fac Sci & Technol, BP 227,Route Belhacel, Mostaganem 27000, Algeria
[2] Abdelhamid Ibn Badis Univ, Lab Electromagnet & Guided Opt, BP 227,Route Belhacel, Mostaganem 27000, Algeria
[3] Univ Sci & Technol Oran, Elect Dept, Signal & Images Lab, BP 1505 El MNouer, Oran, Algeria
来源：
2016 INTERNATIONAL CONFERENCE ON BIO-ENGINEERING FOR SMART TECHNOLOGIES (BIOSMART) | 2016年
关键词：
Electroencephalogram (EEG); detrended fluctuation analysis (DFA); scaling exponent; ROC curves; RANGE TEMPORAL CORRELATIONS; COMPLEXITY; DYNAMICS; SIGNALS; AUTISM; DEPTH;
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：
In this paper, the detrended fluctuation analysis (DFA) is used to quantify the fractal-like scaling properties of the EEG signals. The power-law exponent of DFA of the EEG time series was used to distinguish subjects with epilepsy from healthy controls. The performance of the proposed method was evaluated using the receiver operating characteristics (ROC) curves. The obtained results show that the DFA technique achieves high detection accuracy rate (98%) on distinguishing the epileptic seizure from the normal healthy EEG.
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页数：4
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