School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214000, China.
Institute of Commerce of Digital Art Animation, Wuxi 214000, China.
Comput Math Methods Med. 2020 May 1;2020:2598140. doi: 10.1155/2020/2598140. eCollection 2020.
Epilepsy is marked by seizures stemming from abnormal electrical activity in the brain, causing involuntary movement or behavior. Many scientists have been working hard to explore the cause of epilepsy and seek the prevention and treatment. In the field of machine learning, epileptic diagnosis based on EEG signal has been a very hot research topic; many methods have been proposed, and considerable progress has been achieved. However, resorting the epileptic diagnosis techniques based on EEG to the reality applications still faces many challenges. Low signal-to-noise ratio (SNR) is one of the most important methodological challenges for EEG data collection and analysis. This paper discusses an automated diagnostic method for epileptic detection using a Fréchet Mean embedded in the Grassmann manifold analysis. Fréchet mean-based Grassmann discriminant analysis (FMGDA) algorithm to implement the EEG data dimensionality reduction and clustering task. The method is resorted to reduce Grassmann data from high-dimensional data to a relative lower-dimensional data and maximize between-class distance and minimize within-class distance simultaneously. Every EEG feature is mapped into the Grassmann manifold space first and then resort the Fréchet mean to represent the clustering center to carry out the clustering work. We designed a detailed experimental scheme to test the performance of our proposed algorithm; the test is assessed on several benchmark datasets. Experimental results have delivered that our approach leads to a significant improvement over state-of-the-art Grassmann manifold methods.
癫痫是由大脑异常电活动引起的癫痫发作，导致不自主运动或行为。许多科学家一直在努力探索癫痫的病因，并寻求预防和治疗方法。在机器学习领域，基于脑电图信号的癫痫诊断一直是一个非常热门的研究课题；已经提出了许多方法，并取得了相当大的进展。然而，将基于 EEG 的癫痫诊断技术应用于现实应用仍然面临许多挑战。低信噪比（SNR）是脑电图数据采集和分析中最重要的方法学挑战之一。本文讨论了一种使用 Grassmann 流形分析中嵌入的 Fréchet 均值来进行癫痫检测的自动诊断方法。Fréchet 均值 Grassmann 判别分析（FMGDA）算法实现 EEG 数据降维和聚类任务。该方法用于将 Grassmann 数据从高维数据降低到相对较低的维数，并同时最大化类间距离和最小化类内距离。首先将每个 EEG 特征映射到 Grassmann 流形空间，然后使用 Fréchet 均值表示聚类中心来进行聚类工作。我们设计了一个详细的实验方案来测试我们提出的算法的性能；该测试在几个基准数据集上进行评估。实验结果表明，我们的方法在 Grassmann 流形方法方面取得了显著的改进。
