基于时频特征和多尺度混合神经网络的癫痫预测。

IF 3.5 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Wenwen Chang, Bingyang Ji, Dandan Li, Lei Zhen, Yaxuan Wei, Xuan Liu, Guanghui Yan
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引用次数: 0

摘要

癫痫发作的预测在很大程度上依赖于复杂的多维脑电图信号的精确嵌入和分类。由于脑电信号的个体差异性和动态非线性特性,提取具有高度判别性的时空特征是该领域的核心挑战。为了解决这一问题,本研究提出了一种基于多尺度混合神经网络(EPM-HNN)的癫痫预测新架构,该架构集成了自适应信道加权、多尺度空间特征提取和双向时间依赖建模。具体而言,我们在特征提取过程中引入了具有时空分辨率的滑动窗口机制,增强了模型对跨频段神经动力学的敏感性,并提高了其捕获微模式的能力。我们使用Res2Net-50多尺度特征提取器来增强卷积神经网络处理复杂局部微特征的能力,如多尖波和慢波复合物。此外,我们引入了挤压激励网络(SENet)来自适应捕获不同脑电信号通道之间的潜在有效特征。这种基于自适应注意的动态加权机制具有较强的鲁棒性和较高的泛化能力。此外,我们提出了一种非单一主题,非特定的跨主题训练和测试方法,证明了它在处理数据分布差异时对抗过拟合的能力。在CHB-MIT头皮脑电数据集上进行的实验,总体预测准确率达到97.7%,验证了EPM-HNN架构的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Epilepsy Prediction via Time-Frequency Features and Multi-Scale Hybrid Neural Networks.

The prediction of epileptic seizures heavily depends on the precise embedding and classification of complex, multi-dimensional electroencephalogram (EEG) signals. Due to individual variability and the dynamic non-linear nature of EEG signals, extracting highly discriminative spatiotemporal features is a core challenge in this field. In this study, to address this issue, we proposed a novel architecture based on the Epilepsy Prediction using Multi-Scale Hybrid Neural Network (EPM-HNN), which integrates adaptive channel weighting, multi-scale spatial feature extraction, and bidirectional temporal dependency modeling. Specifically, we incorporated a sliding window mechanism with spatiotemporal resolution into the feature extraction process, enhancing the model's sensitivity to neural dynamics across frequency bands and improving its ability to capture micro-patterns. We used the Res2Net-50 multi-scale feature extractor to enhance the convolutional neural network's capacity to process complex local micro-features, such as polyspike-and-slow-wave complexes. Additionally, we introduced Squeeze-and-Excitation Networks (SENet) to adaptively capture potential effective features between different EEG channels. This dynamic weighting mechanism based on adaptive attention demonstrates strong robustness and high generalization across individual subject data. Furthermore, we proposed a non-single-subject, non-specific cross-subject training and testing method, demonstrating its ability to combat overfitting when addressing differences in data distribution. Experiments on the CHB-MIT scalp EEG dataset achieved an overall prediction accuracy of 97.7%, validating the effectiveness of the proposed EPM-HNN architecture.

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来源期刊
Journal of Medical Systems
Journal of Medical Systems 医学-卫生保健
CiteScore
11.60
自引率
1.90%
发文量
83
审稿时长
4.8 months
期刊介绍: Journal of Medical Systems provides a forum for the presentation and discussion of the increasingly extensive applications of new systems techniques and methods in hospital clinic and physician''s office administration; pathology radiology and pharmaceutical delivery systems; medical records storage and retrieval; and ancillary patient-support systems. The journal publishes informative articles essays and studies across the entire scale of medical systems from large hospital programs to novel small-scale medical services. Education is an integral part of this amalgamation of sciences and selected articles are published in this area. Since existing medical systems are constantly being modified to fit particular circumstances and to solve specific problems the journal includes a special section devoted to status reports on current installations.
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