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Symplectic geometry decomposition-based features for automatic epileptic seizure detection

机译：基于杂的几何分解的自动癫痫癫痫发作检测特征

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摘要

In this paper, a novel method employing symplectic geometry decomposition-based features is proposed for automatic seizure detection. This study explores the performance of the suggested method in signal representation, and it also investigates the discrimination ability of the proposal. To obtain the best tradeoff between classification results and computational complexity, a selection experiment for optimizing the embedding dimension d is introduced. Subsequently, simplified eigenvalues obtained from the symplectic geometry decomposition are adopted as vectors fed into a support vector machine (SVM) to verify the effectiveness of the proposed method. In the experimental processes, a comparison is made between the decomposition capacity of symplectic geometry under various intensities of artificial noise. Further, this study analyzes the efficiency and transferability of the proposed model for multi-class tasks with great clinical significance using different electroencephalogram (EEG) datasets, including the Bonn and (Children's Hospital Boston-Massachusetts Institute of Technology) CHB-MIT datasets. In all classification tasks for the Bonn dataset, the accuracies were greater than 99.17%. The average classification accuracy (ACC) and Matthews correlation coefficient (MCC) of 99.620% and 0.918 were respectively achieved using the CHB-MIT dataset. In comparison to the state-of-art methods, the superior competence of the proposed methodology has high accuracy and low complexity as shown in the experimental results. Furthermore, the transferable ability is verified. The proposed approach is beneficial as an assistant diagnostic tool for clinicians.
机译：本文提出了一种新的采用基于几何分解的特征的方法，用于自动癫痫发作检测。本研究探讨了在信号表示中提出建议的方法的性能，并调查了提案的歧视能力。为了获得分类结果与计算复杂性之间的最佳权衡，介绍了优化嵌入尺寸D的选择实验。随后，采用由辛几何分解获得的简化特征值作为馈送到支撑载体机（SVM）中的载体以验证所提出的方法的有效性。在实验过程中，在人工噪声的各种强度下辛几何的分解能力进行比较。此外，本研究分析了使用不同脑电图（EEG）数据集（包括Bonn和（儿童医院Boston-Massachusetts Theicals）CHB-MIT数据集的临床意义的多级任务模型的效率和可转换性。在Bonn DataSet的所有分类任务中，准确性高于99.17％。使用CHB-MIT数据集分别实现了99.620％和0.918的平均分类精度（ACC）和Matthews相关系数（MCC）。与最先进的方法相比，所提出的方法的卓越能力具有高精度和低复杂性，如实验结果所示。此外，验证可转移能力。该方法是临床医生的助理诊断工具是有益的。

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