用新方法提升脑电图癫痫检测准确率，解决信号噪声大、数据少难题
A Unified Framework for EEG Seizure Detection Using Universum-Integrated Generalized Eigenvalues Proximal Support Vector Machine
- 引入通用样本约束优化分类器，增强对非平稳脑电信号的适应性
- 在两个脑电数据集上分别达到85%和80%最高准确率，平均超基准方法
- 适合脑电分析、医疗诊断领域研究者参考，尤其关注小样本场景
本文提出两种新型通用样本增强型分类器：基于广义特征值的近似支持向量机（U-GEPSVM）与改进型（IU-GEPSVM），用于脑电图（EEG）信号分类。结合广义特征值分解的计算效率与通用样本学习的泛化优势，有效应对脑电分析中的非平稳性、信噪比低及标注数据有限等挑战。U-GEPSVM通过比率型目标函数引入通用样本约束；IU-GEPSVM则采用加权差分形式，独立控制类别分离与通用样本对齐，提升稳定性。在波恩大学脑电数据集上，针对两类二分类任务（O vs S：闭眼健康 vs 癫痫；Z vs S：睁眼健康 vs 癫痫）进行评估，结果显示IU-GEPSVM在两任务中分别实现85%和80%的峰值准确率，平均准确率分别为81.29%和77.57%，优于基线方法。
原文摘要 · Abstract (English)
The paper presents novel Universum-enhanced classifiers: the Universum Generalized Eigenvalue Proximal Support Vector Machine (U-GEPSVM) and the Improved U-GEPSVM (IU-GEPSVM) for EEG signal classification. Using the computational efficiency of generalized eigenvalue decomposition and the generalization benefits of Universum learning, the proposed models address critical challenges in EEG analysis: non-stationarity, low signal-to-noise ratio, and limited labeled data. U-GEPSVM extends the GEPSVM framework by incorporating Universum constraints through a ratio-based objective function, while IU-GEPSVM enhances stability through a weighted difference-based formulation that provides independent control over class separation and Universum alignment. The models are evaluated on the Bonn University EEG dataset across two binary classification tasks: (O vs S)-healthy (eyes closed) vs seizure, and (Z vs S)-healthy (eyes open) vs seizure. IU-GEPSVM achieves peak accuracies of 85% (O vs S) and 80% (Z vs S), with mean accuracies of 81.29% and 77.57% respectively, outperforming baseline methods.
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