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A multi-head self-attention deep learning approach for detection and recommendation of neuromagnetic high frequency oscillations in epilepsy

文献类型： 外文期刊

作者： Zhao, Xiangyu 1 ; Peng, Xueping 3 ; Niu, Ke 4 ; Li, Hailong 5 ; He, Lili 5 ; Yang, Feng 1 ; Wu, Ting 6 ; Chen, Duo 8 ; Zhang, Qiusi 1 ; Ouyang, Menglin 9 ; Guo, Jiayang 10 ; Pan, Yijie 12 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing, Peoples R China

2.Natl Engn Res Ctr Informat Technol Agr, Beijing, Peoples R China

3.Univ Technol Sydney, Australian Artificial Intelligence Inst, Fac Engn & Informat Technol, Ultimo, NSW, Australia

4.Beijing Informat Sci & Technol Univ, Comp Sch, Beijing, Peoples R China

5.Cincinnati Childrens Hosp Med Ctr, Imaging Res Ctr, Dept Radiol, Cincinnati, OH USA

6.Nanjing Univ Chinese Med, Jiangsu Prov Hosp Chinese Med, Dept Radiol, Affiliated Hosp, Nanjing, Peoples R China

7.Nanjing Med Univ, Nanjing Brain Hosp, Dept Magnetoencephalog, Nanjing, Peoples R China

8.Nanjing Univ Chinese Med, Sch Artificial Intelligence & Informat Technol, Nanjing, Peoples R China

9.Ningbo Univ, Med Sch, Affiliated Hosp, Ningbo, Peoples R China

10.Xiamen Univ, Natl Inst Data Sci Hlth & Med, Xiamen, Peoples R China

11.Xiamen Univ, Sch Med, Dept Hematol, Xiamen, Peoples R China

12.Tsinghua Univ, Dept Comp Sci & Technol, Beijing, Peoples R China

13.Chinese Acad Sci, Ningbo Inst Informat Technol Applicat, Ningbo, Peoples R China

关键词： high frequency oscillations (HFOs); magnetoencephalography; MEG; deep learning; multi-head self-attention; HFOs detection; HFOs recommendation

期刊名称：FRONTIERS IN NEUROINFORMATICS （ 影响因子：3.739； 五年影响因子：4.613 ）

ISSN：

年卷期： 2022 年 16 卷

页码：

收录情况： SCI

摘要： Magnetoencephalography is a noninvasive neuromagnetic technology to record epileptic activities for the pre-operative localization of epileptogenic zones, which has received increasing attention in the diagnosis and surgery of epilepsy. As reported by recent studies, pathological high frequency oscillations (HFOs), when utilized as a biomarker to localize the epileptogenic zones, result in a significant reduction in seizure frequency, even seizure elimination in around 80% of cases. Thus, objective, rapid, and automatic detection and recommendation of HFOs are highly desirable for clinicians to alleviate the burden of reviewing a large amount of MEG data from a given patient. Despite the advantage, the performance of existing HFOs rarely satisfies the clinical requirement. Consequently, no HFOs have been successfully applied to real clinical applications so far. In this work, we propose a multi-head self-attention-based detector for recommendation, termed MSADR, to detect and recommend HFO signals. Taking advantage of the state-of-the-art multi-head self-attention mechanism in deep learning, the proposed MSADR achieves a more superior accuracy of 88.6% than peer machine learning models in both detection and recommendation tasks. In addition, the robustness of MSADR is also extensively assessed with various ablation tests, results of which further demonstrate the effectiveness and generalizability of the proposed approach.

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