Cheng Liming, Xiong Jiaqi, Duan Junwei, Zhang Yuhang, Chen Chun, Zhong Jingxin, Zhou Zhiguo, Quan Yujuan
Department of Cerebral Function, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
College of Information Science and Technology, Jinan University, Guangzhou, China.
Front Comput Neurosci. 2024 Jul 11;18:1379368. doi: 10.3389/fncom.2024.1379368. eCollection 2024.
Epilepsy 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.
To 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.
The experimental results indicate that our suggested strategy has the potential to perform as well as current machine learning approaches.
癫痫是一种常见的神经系统疾病，在全球影响着大量人群。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，图正则化广义学习系统（GBLS）凭借其扁平结构和耗时较少的训练过程取得了卓越的性能提升。然而，GBLS中的特征节点和增强节点数量是预先确定的。这些节点设置也是随机选择的，并且在整个训练过程中保持不变。因此，随机性特征更容易产生非最优节点，这些节点对解决优化问题贡献不大。
为了获得更多用于优化的最优节点并实现卓越的自动检测性能，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。自适应进化算法可以基于生成用于选择网络参数的解的经验在策略池中构建变异策略，该算法被纳入SaE-GBLS模型以优化节点参数。我们提出的SaE-GBLS模型基于三个公开可用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。
实验结果表明，我们提出的策略有潜力与当前的机器学习方法表现相当。
