Epileptic seizure detection based on improved wavelet neural networks in long-term intracranial EEG

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• 作者：Dongyun Geng^a ; ^b ; Weidong Zhou^a ; ^b ; ^{wdzhou@sdu.edu.cn" class="auth_mail" title="E-mail the corresponding author} ; Yanli Zhang^a ; ^b ; Shujuan Geng^a ; ^b
• 关键词：Seizure detection ; EEG ; Modified point symmetry-based fuzzy c-means ; Wavelet neural network
• 刊名：Biocybernetics and Biomedical Engineering
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：36
• 期：2
• 页码：375-384
• 全文大小：834 K

文摘

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.