.png){kind=icon}
返回
Scattering transform-based features for the automatic seizure detection
.png){kind=icon}
PRE
AI
DOI：10.1016/j.bbe.2019.11.002.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Developing the automatic detection system is of great clinical significance for assisting neurologists to detect epilepsy using electroencephalogram (EEG) signals. In this research, we explore the ability of a newly-developed algorithm named scattering transform in seizure detection. The preprocessed signal is initially decomposed into scattering coefficients with various orders and scales employing scattering transform. Fuzzy entropy (FuzzyEn) and Log energy entropy (LogEn) of the sub-band coefficients are obtained to characterize the epileptic seizure signals. Then the joint features are fed into five classifiers including support vector machine (SVM), least squares-support vector machine (LS-SVM), genetic algorithm-support vector machine (GA-SVM), extreme learning machine (ELM) and probabilistic neural network (PNN) for the verification of the effectiveness of the proposed scheme. Finally, we not only compare the classification results and the time efficiency derived from different classifiers, but also explore the discrimination performance of the proposed methodology based on ten different classification tasks with great clinical significance. The prominent classification accuracy (ACC) of 99.87 %, 99.59 %, 99.58 %, 99.56 % and 99.80 % are achieved using the above five classifiers respectively. The average ACC and Matthews correlation coefficient (MCC) of 99.75 % and 0.99 are also yielded based on all tasks. Furthermore, the result of Kruskal-Wallis Test for the verification of statistical significance confirms the reliability of the proposal. The comparison with the latest state-of-the-art techniques indicates the superior performance of the proposal. A tradeoff between classification accuracy and time complexity of the proposed approach is accomplished in our work and the possibility for clinical application is also demonstrated. (c) 2019 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.
Keyword:
EEG signals
Scattering transform
Fuzzy entropy
Log energy entropy
Seizure detection
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.6
论文数:
944
被引数:
3.3K
机构
引用论文
An overview of power topologies for micro-hydro turbines.png)
2012 3rd IEEE International Symposium on Power Electronics for Distributed Generation Systems (PEDG)
2012 3rd IEEE International Symposium on Power Electronics for Distributed Generation Systems (PEDG)IF0
PREAI
分享收藏分享收藏A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension一种利用解析时频柔性小波变换和分形维数表征癫痫发作的新方法
PREAI
分享收藏Performance evaluation of DWT based sigmoid entropy in time and frequency domains for automated detection of epileptic seizures using SVM classifier基于DWT的sigmoid熵在时域和频域中的性能评估，用于使用SVM分类器自动检测癫痫发作
OAAI
OAAI分享收藏分享收藏分享收藏
查看更多内容