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Scattering transform-based features for the automatic seizure detection

机译：基于变换的自动癫痫发作检测的特征

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摘要

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.
机译：开发自动检测系统对于使用脑电图（EEG）信号来辅助神经泌素检测癫痫症的临床意义很大。在这项研究中，我们探讨了一种新开发算法在癫痫发作检测中命名的散射变换的能力。预处理信号最初用采用散射变换的各种订单和尺度分解成散射系数。可以获得亚带系数的模糊熵（Fuzzyen）和对数能熵（Logen）以表征癫痫癫痫发作信号。然后将关节功能送入五个分类器，包括支持向量机（SVM），最小二乘 - 支持向量机（LS-SVM），遗传算法支持向量机（GA-SVM），极端学习机（ELM）和概率神经网络网络（PNN）用于验证所提出的方案的有效性。最后，我们不仅比较分类结果和来自不同分类器的时间效率，还探讨了基于十大不同分类任务的提出方法的辨别性能，具有良好的临床意义。突出的分类精度（ACC）分别使用上述五分类机实现99.87％，99.59％，99.58％，99.56％和99.80％。基于所有任务，也会产生99.75％和0.99的平均ACC和MATTHEWS相关系数（MCC）。此外，Kruskal-Wallis试验的结果用于核实统计学意义证实了该提案的可靠性。与最新技术的比较表明该提案的卓越性能。在我们的工作中完成了拟议方法的分类准确性和时间复杂性之间的权衡，并且还证明了临床应用的可能性。 （c）2019年纳雷斯州博士科学学院生物医学研究所。 elsevier b.v出版。保留所有权利。

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