International Journal of Frontiers in Engineering Technology, 2023, 5(4); doi: 10.25236/IJFET.2023.050404.
Chong Li, Shiyu Hu, Xinyan Zhang, Jiale Chen
School of Communication Engineering, Tongda College of Nanjing University of Posts and Telecommunications, Yangzhou, China
Epilepsy is a chronic disease with sudden abnormal discharge of brain neurons, leading to transient brain dysfunction, which has a great impact on the physical and mental health of patients. In this paper, we use the NISOMAP manifold algorithm to reduce the dimension of high-dimensional data randomly selected from the public epilepsy data set, aiming at the disadvantage that the traditional supervised model is nonlinear in feature extraction. The experimental results show that, compared with other dimension reduction algorithms, NISOMAP has a better dimension reduction effect. The visualized shape is oval, with certain regularity, and it is convenient to observe the attack point. It is superior to other algorithms in different sample data, and provides great help for the diagnosis of epilepsy patients.
Manifold learning; Visualization; Dimension reduction; Epilepsy; EEG signal
Chong Li, Shiyu Hu, Xinyan Zhang, Jiale Chen. Study on EEG signal of epileptic patients based on NLSOMAP algorithm. International Journal of Frontiers in Engineering Technology (2023), Vol. 5, Issue 4: 19-23. https://doi.org/10.25236/IJFET.2023.050404.
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