在小波散射变换、双向加权(2D)2PCA和KELM统一框架下识别癫痫性脑电图和充血性心力衰竭脑电图

IF 5.3 2区 医学 Q1 ENGINEERING, BIOMEDICAL Biocybernetics and Biomedical Engineering Pub Date : 2023-01-01 DOI:10.1016/j.bbe.2023.01.002
Tao Zhang , Wanzhong Chen , Xiaojuan Chen
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引用次数: 2

摘要

为了实现各种脑电图和心电图的准确识别,本研究提出了小波散射变换(WST)、双向加权双向二维主成分分析(BW(2D)2PCA)和基于灰狼优化的核极限学习机(KELM)的统一框架。为了在WST域中提取更多的判别特征,在原有双向二维主成分分析的基础上,综合考虑特征值的贡献和相邻两个特征值的变化,提出了BW(2D)2PCA。研究了正常脑电图与发作期脑电图、非发作性脑电图与发作性脑电图、正常脑电图与充血性心力衰竭脑电图的15项分类任务。应用病人非特异性的策略,不少于99.300的方案报告ACCs ±0.121  波恩% 13分类情况下的数据集分类正常vs发作vs猝发的脑电图,MCC 90.947 ±0.128  % CHB-MIT区分non-seizure vs癫痫脑电图的数据集,和MCC 99.994 ±0.001  %识别正常的BBIH vs瑞士法郎ecg数据集。实验结果表明,基于BW(2D)2PCA的框架优于基于(2D)2PCA的方案,高性能的结果表明了框架的有效性,并且我们的方案优于大多数现有的方法。
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Identifying epileptic EEGs and congestive heart failure ECGs under unified framework of wavelet scattering transform, bidirectional weighted (2D)2PCA and KELM

In order to achieve the accurate identifications of various electroencephalograms (EEGs) and electrocardiograms (ECGs), a unified framework of wavelet scattering transform (WST), bidirectional weighted two-directional two-dimensional principal component analysis (BW(2D)2PCA) and grey wolf optimization based kernel extreme learning machine (KELM) was put forward in this study. To extract more discriminating features in the WST domain, the BW(2D)2PCA was proposed based on original two-directional two-dimensional principal component analysis, by considering both the contribution of eigenvalue and the variation of two adjacent eigenvalues. Totally fifteen classification tasks of classifying normal vs interictal vs ictal EEGs, non-seizure vs seizure EEGs and normal vs congestive heart failure (CHF) ECGs were investigated. Applying patient non-specific strategy, the proposed scheme reported ACCs of no less than 99.300 ± 0.121 % for all the thirteen classification cases of Bonn dataset in classifying normal vs interictal vs ictal EEGs, MCC of 90.947 ± 0.128 % in distinguishing non-seizure vs seizure EEGs of CHB-MIT dataset, and MCC of 99.994 ± 0.001 % in identifying normal vs CHF ECGs of BBIH dataset. Experimental results indicate BW(2D)2PCA based framework outperforms (2D)2PCA based scheme, the high-performance results manifest the effectiveness of the proposed framework and our proposal is superior to most existing approaches.

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来源期刊
CiteScore
16.50
自引率
6.20%
发文量
77
审稿时长
38 days
期刊介绍: Biocybernetics and Biomedical Engineering is a quarterly journal, founded in 1981, devoted to publishing the results of original, innovative and creative research investigations in the field of Biocybernetics and biomedical engineering, which bridges mathematical, physical, chemical and engineering methods and technology to analyse physiological processes in living organisms as well as to develop methods, devices and systems used in biology and medicine, mainly in medical diagnosis, monitoring systems and therapy. The Journal''s mission is to advance scientific discovery into new or improved standards of care, and promotion a wide-ranging exchange between science and its application to humans.
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