Hybrid Attention Network for Epileptic EEG Classification

被引:18
|
作者
Zhao, Yanna [1 ]
He, Jiatong [1 ]
Zhu, Fenglin [1 ]
Xiao, Tiantian [1 ]
Zhang, Yongfeng [1 ]
Wang, Ziwei [1 ]
Xu, Fangzhou [2 ,3 ]
Niu, Yi [1 ]
机构
[1] Shandong Normal Univ, Sch Informat Sci & Engn, Jinan 250358, Peoples R China
[2] Qilu Univ Technol, Shandong Acad Sci, Int Sch Optoelect Engn, Jinan 250353, Peoples R China
[3] Jinan Engn Lab Human Machine Intelligent Cooperat, Jinan 250353, Peoples R China
基金
中国国家自然科学基金;
关键词
Seizure detection; EEG; graph attention network; transformer; focal loss; SEIZURE DETECTION;
D O I
10.1142/S0129065723500314
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Automatic seizure detection from electroencephalography (EEG) based on deep learning has been significantly improved. However, existing works have not adequately excavate the spatial-temporal information between EEG channels. Besides, most works mainly focus on patient-specific scenarios while cross-patient seizure detection is more challenging and meaningful. Regarding the above problems, we propose a hybrid attention network (HAN) for automatic seizure detection. Specifically, the graph attention network (GAT) extracts spatial features at the front end, and Transformer gets time features as the back end. HAN leverages the attention mechanism and fully extracts the spatial-temporal correlation of EEG signals. The focal loss function is introduced to HAN to deal with the imbalance of the dataset accompanied by seizure detection based on EEG. Both patient-specific and patient-independent experiments are carried out on the public CHB-MIT database. Experimental results demonstrate the efficacy of HAN in both experimental settings.
引用
收藏
页数:14
相关论文
共 50 条
  • [1] EEG Classification Using Hybrid Convolutional Neural Network with Attention Mechanism
    Ciurea, Alexe
    Manoila, Cristina-Petruta
    Ionescu, Bogdan
    ADVANCES IN DIGITAL HEALTH AND MEDICAL BIOENGINEERING, VOL 1, EHB-2023, 2024, 109 : 783 - 791
  • [2] Hybrid Deep Learning Network with Convolutional Attention for Detecting Epileptic Seizures from EEG Signals
    Mekruksavanich, Sakorn
    Jitpattanakul, Anuchit
    INTELLIGENT SYSTEMS AND APPLICATIONS, VOL 4, INTELLISYS 2024, 2024, 1068 : 1 - 10
  • [3] Neural Network Based Epileptic EEG Detection and Classification
    Gupta, Shivam
    Meena, Jyoti
    Gupta, O. P.
    ADCAIJ-ADVANCES IN DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE JOURNAL, 2020, 9 (02): : 23 - 32
  • [4] Epileptic EEG Classification via Graph Transformer Network
    Lian, Jian
    Xu, Fangzhou
    INTERNATIONAL JOURNAL OF NEURAL SYSTEMS, 2023, 33 (08)
  • [5] Automatic epileptic EEG classification based on differential entropy and attention model
    Zhang, Jian
    Wei, Zuochen
    Zou, Junzhong
    Fu, Hao
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2020, 96
  • [6] Classification of epileptic EEG using neural network and wavelet transform
    Petrosian, A
    Homan, R
    Prokhorov, D
    Wunsch, D
    WAVELET APPLICATIONS IN SIGNAL AND IMAGE PROCESSING IV, PTS 1 AND 2, 1996, 2825 : 834 - 843
  • [7] A hybrid ensemble voting-based residual attention network for motor imagery EEG Classification
    Jindal, K.
    Upadhyay, R.
    Singh, H. S.
    ANALOG INTEGRATED CIRCUITS AND SIGNAL PROCESSING, 2024, 119 (01) : 165 - 184
  • [8] A hybrid ensemble voting-based residual attention network for motor imagery EEG Classification
    K. Jindal
    R. Upadhyay
    H. S. Singh
    Analog Integrated Circuits and Signal Processing, 2024, 119 : 165 - 184
  • [9] Classification of Epileptic and Non-Epileptic EEG Events
    Pippa, Evangelia
    Zacharaki, Evangelia I.
    Mporas, Iosif
    Megalooikonomou, Vasileios
    Tsirka, Vasiliki
    Richardson, Mark
    Koutroumanidis, Michael
    2014 EAI 4TH INTERNATIONAL CONFERENCE ON WIRELESS MOBILE COMMUNICATION AND HEALTHCARE (MOBIHEALTH), 2014, : 87 - 90
  • [10] TESANet: Self-attention network for olfactory EEG classification
    Tong, Chengxuan
    Ding, Yi
    Liang, Kevin Lim Jun
    Zhang, Zhuo
    Zhang, Haihong
    Guan, Cuntai
    2022 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2022,