SEEG-Net: An explainable and deep learning-based cross-subject pathological activity detection method for drug-resistant epilepsy.

Journal: Computers in biology and medicine
PMID: 35791972

Abstract

OBJECTIVE: Precise preoperative evaluation of drug-resistant epilepsy (DRE) requires accurate analysis of invasive stereoelectroencephalography (SEEG). With the tremendous breakthrough of Artificial intelligence (AI), previous studies can help clinical experts to identify pathological activities automatically. However, they still face limitations when applied in real-world clinical DRE scenarios, such as sample imbalance, cross-subject domain shift, and poor interpretability. Our objective is to propose a model that can address the above problems and realizes high-sensitivity SEEG pathological activity detection based on two real clinical datasets.

Authors

  • Yiping Wang
    Shenzhen Key Laboratory of Photonic Devices and Sensing Systems for Internet of Things, Guangdong and Hong Kong Joint Research Centre for Optical Fibre Sensors, State Key Laboratory of Radio Frequency Heterogeneous Integration, Shenzhen University, Shenzhen 518060, China.
  • Yanfeng Yang
    Department of Neurosurgery, Xuan Wu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing, 100053, China.
  • Gongpeng Cao
    Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Haidian District, Beijing, 100876, China.
  • Jinjie Guo
    Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Haidian District, Beijing, 100876, China.
  • Penghu Wei
    Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China; China International Neuroscience Institute (CHINA-INI), Beijing, China.
  • Tao Feng
    School of Pharmacy, Anhui University of Chinese Medicine, Anhui Key Laboratory of Modern Chinese Materia Medica Hefei 230012 People's Republic of China tfeng@mail.scuec.edu.cn wanggk@ahtcm.edu.cn.
  • Yang Dai
    Institute of Cardiovascular Diseases, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai, 200025, PR China.
  • Jinguo Huang
    Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Haidian District, Beijing, 100876, China.
  • Guixia Kang
    School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China.
  • Guoguang Zhao
    Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China; China International Neuroscience Institute (CHINA-INI), Beijing, China; Center of Epilepsy, Beijing Institute for Brain Disorders, Beijing, China.

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