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| 1 | Intracranial Epileptic Seizures Detection Based on an Optimized Neural Network Classifier显示文摘Automatic identification of intracranial electroencephalogram(iEEG)signals has become more and more important in the field of medical diagnostics.In this paper,an optimized neural network classifier is proposed based on an improved feature extraction method for the identification of iEEG epileptic seizures.Four kinds of entropy,Sample entropy,Approximate entropy,Shannon entropy,Log energy entropy are extracted from the database as the feature vectors of Neural network(NN)during the identification process.Four kinds of classification tasks,namely Pre-ictal v Post-ictal(CD),Pre-ictal v Epileptic(CE),Post-ictal v Epileptic(DE),Pre-ictal v Post-ictal v Epileptic(CDE),are used to test the effect of our classification method.The experimental results show that our algorithm achieves higher performance in all tasks than previous algorithms.The effect of hidden layer nodes number is investigated by a constructive approach named growth method.We obtain the optimized number ranges of hidden layer nodes for the binary classification problems CD,CE,DE,and the multitask classification problem CDE,respectively. | GONG Chen LIU Jiahui NIU Yunyun | 2021 | Chinese Journal of Electronics2021，30，3： | 1 |
| 2 | 基于线性估计的抑郁症脑磁图研究显示文摘目的为了研究抑郁症脑磁图的病理特征,使用信息熵对不同情绪刺激的脑磁图复杂度进行量化分析。方法采用线性估计的静态熵、条件熵和互信息,对正性、中性和负性情绪图片刺激下抑郁症患者脑磁图的特征进行对比性分析。结果在中性情绪图片刺激下,抑郁症脑磁图41个通道的静态熵和51个通道的互信息显著高于健康对照组(P<0.05),且均主要集中于额区和颞区。相比之下,条件熵在不同情绪刺激和不同维度下对抑郁症脑磁图的识别效果较差。当维度值改变时,对线性估计条件熵和互信息的区分效果影响较小。结论抑郁症患者较易受到情绪刺激影响,并且静态熵和互信息可以有效地表征抑郁症脑磁图非线性差异,为抑郁症的研究提供帮助。 | 年孟奇 乙万义 白登选 王琼 戴加飞 姚文坡 闫伟 王俊 | 2023 | 北京生物医学工程2023，42，6： | 0 |
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