Epilepsy detection method based on hybrid attention Transformer(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2026年第6期
- Page:
- 811-817
- Research Field:
- 医学影像物理
- Publishing date:
Info
- Title:
- Epilepsy detection method based on hybrid attention Transformer
- Author(s):
- YU Hongshi 1; 2; LIN Yue1; LI Hongyu1
- 1. School of Electronic and Information Engineering, Liaoning Technical University, Huludao 125105, China 2. Liaoning Key Laboratory of Radio Frequency Big Data Intelligent Application, Liaoning Technical University, Huludao 125105, China
- Keywords:
- Keywords: electroencephalogram signal epilepsy tensor hybrid attention Transformer
- PACS:
- R318；TP391
- DOI:
- DOI:10.3969/j.issn.1005-202X.2026.06.015
- Abstract:
- Abstract: Given that existing epilepsy detection methods adopt single-dimensional features as inputs and fail to fully extract multidimensional features, this study proposes a multidimensional feature tensor which integrates frequency-, nonlinear-, and time-domain electroencephalogram features: phase locking value, mutual information, and Kendall correlation coefficient [τb]. Accordingly, a hybrid attention Transformer (HA-Trans) model is developed to effectively extract multidimensional EEG features via spatial attention, cross attention, and temporal attention mechanisms, and the extracted features are then input into the fully connected layer for classification. The proposed method achieves a classification accuracy of 99.17%, specificity of 99.03%, and sensitivity of 98.95% on the CHB-MIT dataset, outperforming existing methods and enabling effective epilepsy detection.
Last Update: 2026-06-29