Electroencephalography (EEG) based epilepsy diagnosis via multiple feature space fusion using shared hidden space-driven multi-view learning

被引:0
作者
Hu, Xiujian [1 ]
Xie, Yicheng [1 ]
Zhao, Hui [1 ]
Sheng, Guanglei [1 ,2 ]
Lai, Khin Wee [3 ]
Zhang, Yuanpeng [4 ]
机构
[1] Bozhou Univ, Dept Elect & Informat Engn, Bozhou, Anhui, Peoples R China
[2] Xian Univ Technol, Sch Comp Sci & Engn, Xian, Shanxi, Peoples R China
[3] Univ Malaya, Dept Biomed Engn, Kuala Lumpur, Malaysia
[4] Nantong Univ, Dept Med Informat, Nantong, Jiangsu, Peoples R China
关键词
Multi-view learning; EEG; Epilepsy; Shared hidden space; SYSTEM;
D O I
10.7717/peerj-cs.1874
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Epilepsy is a chronic, non-communicable disease caused by paroxysmal abnormal synchronized electrical activity of brain neurons, and is one of the most common neurological diseases worldwide. Electroencephalography (EEG) is currently a crucial tool for epilepsy diagnosis. With the development of artificial intelligence, multi-view learning-based EEG analysis has become an important method for automatic epilepsy recognition because EEG contains difficult types of features such as time-frequency features, frequency-domain features and time-domain features. However, current multi-view learning still faces some challenges, such as the difference between samples of the same class from different views is greater than the difference between samples of different classes from the same view. In view of this, in this study, we propose a shared hidden space-driven multi-view learning algorithm. The algorithm uses kernel density estimation to construct a shared hidden space and combines the shared hidden space with the original space to obtain an expanded space for multi-view learning. By constructing the expanded space and utilizing the information of both the shared hidden space and the original space for learning, the relevant information of samples within and across views can thereby be fully utilized. Experimental results on a dataset of epilepsy provided by the University of Bonn show that the proposed algorithm has promising performance, with an average classification accuracy value of 0.9787, which achieves at least 4% improvement compared to single-view methods.
引用
收藏
页码:2 / 18
页数:18
相关论文
共 22 条
  • [11] Robust kernel principal component analysis with optimal mean
    Li, Pei
    Zhang, Wenlin
    Lu, Chengjun
    Zhang, Rui
    Li, Xuelong
    [J]. NEURAL NETWORKS, 2022, 152 : 347 - 352
  • [12] A Novel Locally Linear KNN Method With Applications to Visual Recognition
    Liu, Qingfeng
    Liu, Chengjun
    [J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2017, 28 (09) : 2010 - 2021
  • [13] Liu Y, 2019, 2019 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI 2019), P2608, DOI 10.1109/SSCI44817.2019.9002782
  • [14] Automated identification system for seizure EEG signals using tunable-Q wavelet transform
    Reddy, G. Ravi Shankar
    Rao, Rameshwar
    [J]. ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH, 2017, 20 (05): : 1486 - 1493
  • [15] Analysis of WMN-HC Simulation System Data Using Friedman Test
    Sakamoto, Shinji
    Lala, Algenti
    Oda, Tetsuya
    Kolici, Vladi
    Barolli, Leonard
    Xhafa, Fatos
    [J]. 2015 9TH INTERNATIONAL CONFERENCE ON COMPLEX, INTELLIGENT, AND SOFTWARE INTENSIVE SYSTEMS CISIS 2015, 2015, : 254 - 259
  • [16] Deep Multi-View Feature Learning for EEG-Based Epileptic Seizure Detection
    Tian, Xiaobin
    Deng, Zhaohong
    Ying, Wenhao
    Choi, Kup-Sze
    Wu, Dongrui
    Qin, Bin
    Wang, Jun
    Shen, Hongbin
    Wang, Shitong
    [J]. IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2019, 27 (10) : 1962 - 1972
  • [17] Kernel Density Estimation, Kernel Methods, and Fast Learning in Large Data Sets
    Wang, Shitong
    Wang, Jun
    Chung, Fu-lai
    [J]. IEEE TRANSACTIONS ON CYBERNETICS, 2014, 44 (01) : 1 - 20
  • [18] Deep multi-view learning methods: A review
    Yan, Xiaoqiang
    Hu, Shizhe
    Mao, Yiqiao
    Ye, Yangdong
    Yu, Hui
    [J]. NEUROCOMPUTING, 2021, 448 : 106 - 129
  • [19] A Multi-View Deep Learning Framework for EEG Seizure Detection
    Yuan, Ye
    Xun, Guangxu
    Jia, Kebin
    Zhang, Aidong
    [J]. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2019, 23 (01) : 83 - 94
  • [20] A Multiview and Multiexemplar Fuzzy Clustering Approach: Theoretical Analysis and Experimental Studies
    Zhang, Yuanpeng
    Chung, Fu-Lai
    Wang, Shitong
    [J]. IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2019, 27 (08) : 1543 - 1557