Automated detection of epileptic EEGs using a novel fusion feature and extreme learning machine
Automated detection of epileptic EEGs using a novel fusion feature and extreme learning machine
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使用新颖的融合功能和极限学习机自动检测癫痫脑电图
DOI：
10.1016/j.neucom.2015.10.070
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发表时间：
2016-01-29
期刊：
影响因子：
6
通讯作者：
Zhang, Rui
中科院分区：
文献类型：
--
作者：
Song, Jiang-Ling;Hu, Wenfeng;Zhang, Rui
近年来，利用脑电自动检测癫痫发作越来越受到人们的关注，并且在诊断和治疗方面越来越有帮助。如何设计合适的特征提取方法以及如何选择有效的分类器是成功实现的关键。本文首先在马氏距离和离散小波变换的基础上提出了一种新的基于马氏相似性的特征提取方法。为了进一步提高性能，本文在特征融合层设计了一种融合特征（MS-SE-FF），将基于马氏相似度的信号相似性特征和基于样本熵的信号复杂性特征相结合。最后，将新的融合特征MS-SE-FF与极限学习机（ELM）相结合，构建了一种自动癫痫发作检测方法FF-ELM-SD。实验结果表明，该方法FF-ELM-SD在保持效率和简单性的同时，在癫痫发作检测方面做得很好。(C)2015 Elsevier B. V.版权所有。
Automated seizure detection using EEG has gained increasing attraction in recent years and appeared more and more helpful in both diagnosis and treatment. How to design an appropriate feature extraction method and how to select an efficient classifier are recognized to be crucial in the successful realization. This paper first proposes a new Mahalanobis-similarity-based feature extraction method on the basis of the Mahalanobis distance and discrete wavelet transformation (DWT). Then in order to further improve the performance, this paper designs a fusion feature (MS-SE-FF) in the feature-fusion level, where the Mahalanobis-similarity-based feature characterizing the similarity between signals and the sample-entropy-based feature characterizing the complexity of signals are combined together. Finally, an automated seizure detection method FF-ELM-SD has been built, which is integrated between the novel fusion feature MS-SE-FF and extreme learning machine (ELM). Experimental results demonstrate that the proposed method FF-ELM-SD does a good job in the epileptic seizure detection while preserving the efficiency and simplicity. (C) 2015 Elsevier B.V. All rights reserved.