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首页> 外文期刊>Biocybernetics and biomedical engineering >Epileptic seizure detection based on improved wavelet neural networks in long-term intracranial EEG
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Epileptic seizure detection based on improved wavelet neural networks in long-term intracranial EEG

机译：基于改进小波神经网络的长期颅内脑电图癫痫发作检测

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摘要

Automatic seizure detection is of great importance for speeding up the inspection process and relieving the workload of medical staff in the analysis of EEG recordings. In this study, a method based on an improved wavelet neural network (WNN) is proposed for automatic seizure detection in long-term intracranial EEG. WNN combines the traditional back propagation neural network (BPNN) with wavelet transform. Compared with classic WNN architectures, a modified point symmetry-based fuzzy c-means (MSFCM) algorithm is applied to the initialization of wavelet transform's translations, which has been successful in multiclass cancer classification. In addition, Fast-decaying Morlet wavelet is chosen as the activation function to make the WNN learn faster. Relative amplitude and relative fluctuation index are extracted as a feature vector to describe the variation of EEG signals, and the feature vector is then fed into WNN for classification. At last, post-processing including smoothing, channel fusion and collar technique is adopted to achieve more accurate and stable results. This system performs efficiently with the average sensitivity of 96.72%, specificity of 98.91% and false-detection rate of 0.27 h(-1). The proposed approach achieves high sensitivity and low false detection rate, which demonstrates its potential for clinical usage. (C) 2016 Nalecz Institute of Biocybemetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier Sp. z o.o. All rights reserved.
机译：自动癫痫发作检测对于加快检查过程和减轻医务人员在脑电图记录分析中的工作量至关重要。在这项研究中，提出了一种基于改进的小波神经网络（WNN）的方法，用于长期颅内脑电图的癫痫发作自动检测。 WNN将传统的反向传播神经网络（BPNN）与小波变换相结合。与经典的WNN架构相比，将基于点对称性的改进的模糊c均值（MSFCM）算法应用于小波变换的翻译初始化，该算法已成功用于多类癌症分类。另外，选择快速衰减的Morlet小波作为激活函数，以使WNN学习更快。提取相对振幅和相对波动指数作为特征向量来描述脑电信号的变化，然后将特征向量输入到WNN中进行分类。最后，采用包括平滑，通道融合和套环技术在内的后处理，以实现更加准确和稳定的结果。该系统以平均灵敏度96.72％，特异性98.91％和误检率0.27 h（-1）高效地运行。所提出的方法实现了高灵敏度和低误检率，这证明了其在临床上的应用潜力。 （C）2016波兰科学院Nalecz生物仿生和生物医学工程研究所。由Elsevier Sp。发行。动物园。版权所有。

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