数字农科院2.0

A causal attention network with time frequency channel feature fusion for epileptic seizure prediction

文献类型： 外文期刊

作者： Yimin Qu;Songhui Rao;Ting Li;Ying Li;Yan Niu;Ruiyun Chang;Xianchuan Chen;Bin Wang

作者机构：

关键词： Causal attention network;Deep learning;EEG;Seizure prediction;Signal processing

期刊名称： Journal of Neuroscience Methods

ISSN： 0165-0270

年卷期： 2026 年 428 卷

页码：

收录情况： SCIE（2025版）

摘要： Background: Epilepsy poses ongoing physical and mental threats and causes substantial economic burdens. Better seizure forecasting enables faster medical responses, improving patients’ quality of life and lowering healthcare costs. Research mainly focuses on early forecasting within a short preictal window, often too brief for effective drug administration. A major challenge is that a longer preictal phase may resemble the interictal state, making differentiation difficult. New methods: We propose a causal attention network (CANet) with a longer interictal and preictal of 1 h and 2 h respectively as the research object. In the feature extraction, a dilated causal convolution network is employed to extract local features. Causal attention is innovatively incorporated into epilepsy prediction to capture global correlation features. The complementary integration of these two methods enhances feature extraction and enables a more precise distinction between interictal and preictal periods. A double-layer dynamic window algorithm is developed for seizure prediction. Results: We evaluate the performance on Freiburg and CHB-MIT datasets. On the Freiburg dataset, the sensitivity(Sen) of the 1/2-hour preictal intervals was 100.00%/96.67%, with a false alarm rate per hour (FAR) of 0.0077/h/0.0472/h, and the average prediction time (APT) was 97.59 min. On the CHB-MIT dataset, we achieved Sen of 97.06%/92.31%, FAR of 0.0251/h/0.0666/h, and APT of 94.85 min, under the same conditions. Comparison with existing methods and conclusion: Our approach outperforms most of the previous methods, and the intracranial EEG (Freiburg) can more effectively distinguish interictal and preictal periods than scalp EEG (CHB-MIT).

分类号：

  • 相关文献

[1]Analysis of the genetic diversity in Saccharum spontaneum L. accessions from Guangxi Province of China with RAPD-PCR. Li, Yang-Rui,He, Wei-Zhong,He, Hong,Liu, Xi-Hui,Song, Huan-Zhong,Liu, Hai-Bin,Fang, Wei-Kuan,Zhang, Ge-min,Li, Yang-Rui,He, Hong,Li, Yang-Rui,He, Hong,Zhu, Ru-Cai,Li, Yang-Rui. 2010

[2]Design and Test of Cleaning Loss Kernel Recognition System for Corn Combine Harvester. Min Zhang,Lan Jiang,Chongyou Wu,Gang Wang. 2022

[3]Design and Test of Sensor for Monitoring Corn Cleaning Loss. Wei, Dexin,Wu, Chongyou,Jiang, Lan,Wang, Gang,Chen, Hui. 2023

[4]Advancing olfactory perception research with EEG analysis: a dynamic approach of understanding brain responses to almond deterioration. Jianxun Li,Jinhua Han,Shengfan Chen,Bei Li,Lijun Wu,Qianqian Li. 2025

[5]Deep learning based weed detection and target spraying robot system at seedling stage of cotton field. Xiangpeng Fan,Xiujuan Chai,Jianping Zhou,Tan Sun. 2023

[6]Inter-Continental Transfer of Pre-Trained Deep Learning Rice Mapping Model and Its Generalization Ability. Lingbo Yang,Ran Huang,Jingcheng Zhang,Jingfeng Huang,Limin Wang,Jiancong Dong,Jie Shao. 2023

[7]Semantic Segmentation Based on Temporal Features: Learning of Temporal-Spatial Information From Time-Series SAR Images for Paddy Rice Mapping. Yang, Lingbo,Huang, Ran,Huang, Jingfeng,Lin, Tao,Wang, Limin,Mijiti, Ruzemaimaiti,Wei, Pengliang,Tang, Chao,Shao, Jie,Li, Qiangzi,Du, Xin. 2021

[8]A Novel Physics-Statistical Coupled Paradigm for Retrieving Integrated Water Vapor Content Based on Artificial Intelligence. Ruyu Mei,Kebiao Mao,Jiancheng Shi,Jeffrey Nielson,Sayed M. Bateni,Fei Meng,Guoming Du. 2023

[9]Machine learning for image-based multi-omics analysis of leaf veins. Yubin Zhang,Ning Zhang,Xiujuan Chai,Tan Sun. 2023

[10]Auction-based deep learning-driven smart agricultural supply chain mechanism. Yu Feng,Dong Mei,Hua Zhao. 2023

[11]Research on machine vision and deep learning based recognition of cotton seedling aphid infestation level. Xin Xu,Jing Shi,Yongqin Chen,Qiang He,Liangliang Liu,Tong Sun,Ruifeng Ding,Yanhui Lu,Chaoqun Xue,Hongbo Qiao. 2023

[12]Integrating satellite-derived climatic and vegetation indices to predict smallholder maize yield using deep learning. Liangliang Zhang,Zhao Zhang,Yuchuan Luo,Juan Cao,Ruizhi Xie,Shaokun Li. 2021

[13]A rapid, low-cost deep learning system to classify strawberry disease based on cloud service. Guo feng YANG,Yong YANG,Zi kang HE,Xin yu ZHANG,Yong HE. 2022

[14]Deep learning for change detection in remote sensing: a review. Bai, Ting,Wang, Le,Yin, Dameng,Sun, Kaimin,Chen, Yepei,Li, Wenzhuo,Li, Deren. 2022

[15]Mapping of lakes in the Qinghai-Tibet Plateau from 2016 to 2021: trend and potential regularity. Zhichong Yang,Si Bo Duan,Xiaoai Dai,Yingwei Sun,Meng Liu. 2022

[16]Bionic technology and deep learning in agricultural engineering: Current status and future prospects. Chunlei Tu,Jinxia Li,Xingsong Wang,Shen Cheng,Jie Li. 2021

[17]Automatic freezing-tolerant rapeseed material recognition using UAV images and deep learning. Lili Li,Jiangwei Qiao,Jian Yao,Jie Li,Li Li. 2022

[18]Evaluation of deep learning for automatic multi‐view face detection in cattle. Beibei Xu,Wensheng Wang,Leifeng Guo,Guipeng Chen,Yaowu Wang,Wenju Zhang,Yongfeng Li. 2021

[19]Semi-Supervised Transformation and Deep Embedding-Based Anomaly Identification for Agricultural Internet of Things. Xiang Yin,Letian Wang,Weikuan Jia,Chengqian Jin. 2021

[20]Identification method of wheat cultivars by using a convolutional neural network combined with images of multiple growth periods of wheat. Jiameng Gao,Chengzhong Liu,Junying Han,Qinglin Lu,Hengxing Wang,Jianhua Zhang,Xuguang Bai,Jiake Luo. 2021

作者其他论文 更多>>