标题:Epileptic seizure detection based on improved wavelet neural networks in long-term intracranial EEG
作者:Geng, Dongyun; Zhou, Weidong; Zhang, Yanli; Geng, Shujuan
作者机构:[Geng, Dongyun; Zhou, Weidong; Zhang, Yanli; Geng, Shujuan] Shandong Univ, Sch Informat Sci & Engn, 27 Shanda Rd, Jinan 250100, Peoples R China.; [G 更多
通讯作者:Zhou, WD
通讯作者地址:[Zhou, WD]Shandong Univ, Sch Informat Sci & Engn, 27 Shanda Rd, Jinan 250100, Peoples R China.
来源:BIOCYBERNETICS AND BIOMEDICAL ENGINEERING
出版年:2016
卷:36
期:2
页码:375-384
DOI:10.1016/j.bbe.2016.03.001
关键词:Seizure detection; EEG; Modified point symmetry-based fuzzy c-means;; Wavelet neural network
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.
收录类别:SCOPUS;SCIE
WOS核心被引频次:1
Scopus被引频次:2
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962209716&doi=10.1016%2fj.bbe.2016.03.001&partnerID=40&md5=4e93fa84a56774e16c44edcbf32f8a37
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