查看“癫痫小组/癫痫/Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals”的源代码

{{文章推荐|Reason=提出了利用熵测量对局灶和非局灶癫痫脑信号进行分类的方法，是熵分析手段在癫痫脑信号处理的重要研究，有重要的借鉴意义。|Journal=Entropy|PubYear=2015|DOI=10.3390/e17020669|Category_=研究性工作|Domain=癫痫|RecomBy=郑冬阳|RecomGrp=癫痫小组|ReviewBy=郝老师|Abstract=The brain is a complex structure made up of interconnected neurons, and its electrical activities can be evaluated using electroencephalogram (EEG) signals. The characteristics of the brain area affected by partial epilepsy can be studied using focal and non-focal EEG signals. In this work, a method for the classification of focal and non-focal EEG signals is presented using entropy measures. These entropy measures can be useful in assessing the nonlinear interrelation and complexity of focal and non-focal EEG signals. These EEG signals are first decomposed using the empirical mode decomposition (EMD) method to extract intrinsic mode functions (IMFs). The entropy features, namely, average Shannon entropy (ShEn(Avg)), average Renyi's entropy (RenEn(Avg)), average approximate entropy (ApEn(Avg)), average sample entropy (SpEn(Avg)) and average phase entropies (S1(Avg) and S2(Avg)), are computed from different IMFs of focal and non-focal EEG signals. These entropies are used as the input feature set for the least squares support vector machine (LS-SVM) classifier to classify into focal and non-focal EEG signals. Experimental results show that our proposed method is able to differentiate the focal and non-focal EEG signals with an average classification accuracy of 87% correct.|DetaialsDM=癫痫脑信号的熵分析}}