前沿 | SaE-GBLS:基于脑电图的癫痫发作自动检测的有效自适应进化优化图宽模型

IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Frontiers in Computational Neuroscience Pub Date : 2024-06-26 DOI:10.3389/fncom.2024.1379368
Liming Cheng, Jiaqi Xiong, Junwei Duan, Yuhang Zhang, Chun Chen, Jingxin Zhong, Zhiguo Zhou, Yujuan Quan
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引用次数: 0

摘要

导言癫痫是一种常见的神经系统疾病,影响着全球众多患者。癫痫的主要挑战之一是准确及时地检测癫痫发作。最近,与深度神经网络相比,图正则化广泛学习系统(GBLS)凭借其扁平化结构和较少的训练过程耗时,实现了卓越的性能提升。然而,GBLS 中特征节点和增强节点的数量是预先确定的。这些节点的设置也是随机选择的,并在整个训练过程中保持不变。为了获得更多优化节点,实现更优越的自动检测性能,我们提出了一种名为自适应进化图正则化广义学习系统(SaE-GBLS)的新型广义神经网络。在 SaE-GBLS 模型中加入了自适应进化算法,该算法可根据产生解决方案的经验在策略池中构建突变策略,以选择网络参数,从而优化节点参数。我们提出的 SaE-GBLS 模型基于三个公开的脑电图数据集和一个私人临床脑电图数据集自动检测癫痫发作。
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Frontiers | SaE-GBLS: an effective self-adaptive evolutionary optimized graph-broad model for EEG-based automatic epileptic seizure detection
IntroductionEpilepsy is a common neurological condition that affects a large number of individuals worldwide. One of the primary challenges in epilepsy is the accurate and timely detection of seizure. Recently, the graph regularized broad learning system (GBLS) has achieved superior performance improvement with its flat structure and less time-consuming training process compared to deep neural networks. Nevertheless, the number of feature and enhancement nodes in GBLS is predetermined. These node settings are also randomly selected and remain unchanged throughout the training process. The characteristic of randomness is thus more easier to make non-optimal nodes generate, which cannot contribute significantly to solving the optimization problem.MethodsTo obtain more optimal nodes for optimization and achieve superior automatic detection performance, we propose a novel broad neural network named self-adaptive evolutionary graph regularized broad learning system (SaE-GBLS). Self-adaptive evolutionary algorithm, which can construct mutation strategies in the strategy pool based on the experience of producing solutions for selecting network parameters, is incorporated into SaE-GBLS model for optimizing the node parameters. The epilepsy seizure is automatic detected by our proposed SaE-GBLS model based on three publicly available EEG datasets and one private clinical EEG dataset.Results and discussionThe experimental results indicate that our suggested strategy has the potential to perform as well as current machine learning approaches.
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来源期刊
Frontiers in Computational Neuroscience
Frontiers in Computational Neuroscience MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
5.30
自引率
3.10%
发文量
166
审稿时长
6-12 weeks
期刊介绍: Frontiers in Computational Neuroscience is a first-tier electronic journal devoted to promoting theoretical modeling of brain function and fostering interdisciplinary interactions between theoretical and experimental neuroscience. Progress in understanding the amazing capabilities of the brain is still limited, and we believe that it will only come with deep theoretical thinking and mutually stimulating cooperation between different disciplines and approaches. We therefore invite original contributions on a wide range of topics that present the fruits of such cooperation, or provide stimuli for future alliances. We aim to provide an interactive forum for cutting-edge theoretical studies of the nervous system, and for promulgating the best theoretical research to the broader neuroscience community. Models of all styles and at all levels are welcome, from biophysically motivated realistic simulations of neurons and synapses to high-level abstract models of inference and decision making. While the journal is primarily focused on theoretically based and driven research, we welcome experimental studies that validate and test theoretical conclusions. Also: comp neuro
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