针对癫痫脑电信号的多模态特征融合与多头自我关注。

IF 2.6 4区 工程技术 Q1 Mathematics
Ning Huang, Zhengtao Xi, Yingying Jiao, Yudong Zhang, Zhuqing Jiao, Xiaona Li
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引用次数: 0

摘要

对脑电图(EEG)信号进行自动分类对于癫痫的诊断和治疗非常重要。目前,主流的单模态特征提取方法无法涵盖不同模态的信息,导致现有方法的分类性能不佳,尤其是多分类问题。我们提出了一种针对癫痫脑电信号的多模态特征融合(MMFF)方法。首先,通过核主成分分析提取时域特征,通过短时傅里叶提取变换提取频域特征,通过计算样本熵提取非线性动态特征。在此基础上,通过多头自我注意机制交互学习这三种模式的特征,并同时训练注意权重。通过合并特征表示的值向量获得融合特征,同时保留时间、频率和非线性动力学信息,以筛选出更具代表性的癫痫特征,提高特征提取的准确性。最后,将特征融合方法应用于癫痫脑电信号分类。实验结果表明,所提出的方法在癫痫脑电信号的五类分类任务中达到了 92.76 ± 1.64% 的分类准确率。多头自注意机制促进了多模态特征的融合,为癫痫的诊断和治疗提供了一种高效而新颖的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-modal feature fusion with multi-head self-attention for epileptic EEG signals.

It is important to classify electroencephalography (EEG) signals automatically for the diagnosis and treatment of epilepsy. Currently, the dominant single-modal feature extraction methods cannot cover the information of different modalities, resulting in poor classification performance of existing methods, especially the multi-classification problem. We proposed a multi-modal feature fusion (MMFF) method for epileptic EEG signals. First, the time domain features were extracted by kernel principal component analysis, the frequency domain features were extracted by short-time Fourier extracted transform, and the nonlinear dynamic features were extracted by calculating sample entropy. On this basis, the features of these three modalities were interactively learned through the multi-head self-attention mechanism, and the attention weights were trained simultaneously. The fused features were obtained by combining the value vectors of feature representations, while the time, frequency, and nonlinear dynamics information were retained to screen out more representative epileptic features and improve the accuracy of feature extraction. Finally, the feature fusion method was applied to epileptic EEG signal classifications. The experimental results demonstrated that the proposed method achieves a classification accuracy of 92.76 ± 1.64% across the five-category classification task for epileptic EEG signals. The multi-head self-attention mechanism promotes the fusion of multi-modal features and offers an efficient and novel approach for diagnosing and treating epilepsy.

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来源期刊
Mathematical Biosciences and Engineering
Mathematical Biosciences and Engineering 工程技术-数学跨学科应用
CiteScore
3.90
自引率
7.70%
发文量
586
审稿时长
>12 weeks
期刊介绍: Mathematical Biosciences and Engineering (MBE) is an interdisciplinary Open Access journal promoting cutting-edge research, technology transfer and knowledge translation about complex data and information processing. MBE publishes Research articles (long and original research); Communications (short and novel research); Expository papers; Technology Transfer and Knowledge Translation reports (description of new technologies and products); Announcements and Industrial Progress and News (announcements and even advertisement, including major conferences).
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