利用伪三维 CNN 基于脑电图预测癫痫发作

IF 2.5 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Xin Liu, Chunyang Li, Xicheng Lou, Haohuan Kong, Xinwei Li, Zhangyong Li, Lisha Zhong
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引用次数: 0

摘要

癫痫发作具有突发性和不可预测性的特点,给患者的日常生活带来极大风险。准确可靠的癫痫发作预测系统可以在癫痫发作前发出警报,并为患者和护理人员提供足够的时间采取适当的措施。本研究提出了一种基于深度学习并结合手工特征的有效癫痫发作预测方法。手工特征通过最大相关性和最小冗余度(mRMR)进行选择,以获得最佳特征集。为了从融合的多维结构中提取癫痫特征,我们设计了一个 P3D-BiConvLstm3D 模型,它是伪三维卷积神经网络(P3DCNN)和双向卷积长短期记忆三维模型(BiConvLstm3D)的结合。我们还将脑电信号转换为融合空间、人工特征和时间信息的多维结构。然后,将多维结构输入 P3DCNN 以提取空间和人工特征以及特征与特征之间的依赖关系,再输入 BiConvLstm3D 以探索时间依赖关系,同时保留空间特征,最后,实施通道关注机制,以强调多通道输出中更具代表性的信息。在 CHB-MIT 头皮脑电图数据库中,所提模型的平均准确率为 98.13%,平均灵敏度为 98.03%,平均精确度为 98.30%,平均特异度为 98.23%。将所提出的模型与其他基线方法进行了比较,以确认通过时空非线性特征融合获得的特征具有更好的性能。结果表明,通过时空非线性特征融合预测癫痫的 P3DCNN-BiConvLstm3D-Attention3D 方法是有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Epileptic seizure prediction based on EEG using pseudo-three-dimensional CNN
Epileptic seizures are characterized by their sudden and unpredictable nature, posing significant risks to a patient’s daily life. Accurate and reliable seizure prediction systems can provide alerts before a seizure occurs, as well as give the patient and caregivers provider enough time to take appropriate measure. This study presents an effective seizure prediction method based on deep learning that combine with handcrafted features. The handcrafted features were selected by Max-Relevance and Min-Redundancy (mRMR) to obtain the optimal set of features. To extract the epileptic features from the fused multidimensional structure, we designed a P3D-BiConvLstm3D model, which is a combination of pseudo-3D convolutional neural network (P3DCNN) and bidirectional convolutional long short-term memory 3D (BiConvLstm3D). We also converted EEG signals into a multidimensional structure that fused spatial, manual features, and temporal information. The multidimensional structure is then fed into a P3DCNN to extract spatial and manual features and feature-to-feature dependencies, followed by a BiConvLstm3D input to explore temporal dependencies while preserving the spatial features, and finally, a channel attention mechanism is implemented to emphasize the more representative information in the multichannel output. The proposed has an average accuracy of 98.13%, an average sensitivity of 98.03%, an average precision of 98.30% and an average specificity of 98.23% for the CHB-MIT scalp EEG database. A comparison of the proposed model with other baseline methods was done to confirm the better performance of features through time–space nonlinear feature fusion. The results show that the proposed P3DCNN-BiConvLstm3D-Attention3D method for epilepsy prediction by time–space nonlinear feature fusion is effective.
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来源期刊
Frontiers in Neuroinformatics
Frontiers in Neuroinformatics MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
4.80
自引率
5.70%
发文量
132
审稿时长
14 weeks
期刊介绍: Frontiers in Neuroinformatics publishes rigorously peer-reviewed research on the development and implementation of numerical/computational models and analytical tools used to share, integrate and analyze experimental data and advance theories of the nervous system functions. Specialty Chief Editors Jan G. Bjaalie at the University of Oslo and Sean L. Hill at the École Polytechnique Fédérale de Lausanne are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Neuroscience is being propelled into the information age as the volume of information explodes, demanding organization and synthesis. Novel synthesis approaches are opening up a new dimension for the exploration of the components of brain elements and systems and the vast number of variables that underlie their functions. Neural data is highly heterogeneous with complex inter-relations across multiple levels, driving the need for innovative organizing and synthesizing approaches from genes to cognition, and covering a range of species and disease states. Frontiers in Neuroinformatics therefore welcomes submissions on existing neuroscience databases, development of data and knowledge bases for all levels of neuroscience, applications and technologies that can facilitate data sharing (interoperability, formats, terminologies, and ontologies), and novel tools for data acquisition, analyses, visualization, and dissemination of nervous system data. Our journal welcomes submissions on new tools (software and hardware) that support brain modeling, and the merging of neuroscience databases with brain models used for simulation and visualization.
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