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An EEG abnormality detection algorithm based on graphic attention network

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Abstract

The incidence of brain diseases has increased yearly, threatening human life and health seriously. The Electroencephalogram (EEG) has been playing an important role in clinical practice for diagnosing brain diseases. However, due to the interference of noise and the limitations of manual observation, experts need to spend a lot of time and energy on EEG interpretation, and also is affected by subjective factors, which is prone to misjudgment. Therefore, the establishment of EEG-assisted diagnosis system is of great significance for the clinical diagnosis of brain diseases. With the application of artificial intelligence in EEG auxiliary system, researchers have proposed a series of EEG automatic analysis and anomaly detection algorithms based on deep learning. However, the existing algorithms still have some shortcomings such as inadequate extraction of potential spatio-temporal features in EEG signals. In this paper, the method based on GATs-LSTM is proposed. The comparative analysis of the final experiment shows that, It has demonstrated superior performance on the benchmark dataset with sensitive and specificity as 99.21%, 99.73% and 94.15%, 95.67%. The proposed work shows great results on the benchmark datasets and a big potential for clinics as a support system with medical experts monitoring the patients.

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Data availability

The datasets CHB-MIT analyzed during the current study are available in the physionet repository, https://physionet.org/content/chbmit/1.0.0

The datasets Bonn Epilepsy analyzed during the current study are available in http://qcsdn.com/q/a/3118833.html

The datasets Clinical data set analyzed during the current study are not publicly available due to Privacy Policy but are available from the corresponding author on reasonable request.

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Acknowledgments

This work was partially supported by National Natural Science Foundation of China under grant agreements Nos. 61973250, 82150301, 61973249, 62002271, 61902296, 62073218, 62133012, 62273232, 62273231, 62003279, 61936006, and 62073255. The Innovation Capability Support Program of Shaanxi under Grant 2021TD-05. The Key Research and Development Program of Shaanxi under Grant 2020ZDLGY04-07, 2021ZDLGY02-06 and 2021GY-077, Young science and technology nova of Shaanxi Province: 2022KJXX-73, 2023KJXX-136. Qin Chuangyuan project 2021QCYRC4-49, National Defense Science and Technology Key Laboratory Fund Project No. 6142101210202, Qinchuangyuan Scientist + Engineer No. 2022KXJ-169, National Key R & D program of China (No. 2022YFB4300700), Shaanxi Association for Science and Technology Young Talent Lifting Program (No.XXJS202242).

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Author notes

  1. Junwei Duan and Fei Xie contributed equally.

Authors and Affiliations

  1. School of Information Science and Technology, Northwest University, Xi’an, China

    Junwei Duan, Ningyuan Huang & Ziyu Guan

  2. Frontier Cross Research Institute, Xidian University, Xi’an, China

    Fei Xie

  3. Key Laboratory of Human-Machine Integration and Control Technology for Intelligent Rehabilitation, Xijing University, 710123, Xi’an, China

    Fei Xie

  4. Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Ningdi Luo

  5. School of Computer Science and Technology, Xidian University, Xi’an, China

    Wei Zhao

  6. Shanghai Yiran Health Consulting Co., Ltd., Shanghai, China

    Gang Gao

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  1. Junwei Duan
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  2. Fei Xie
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Correspondence to Ziyu Guan.

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Duan, J., Xie, F., Huang, N. et al. An EEG abnormality detection algorithm based on graphic attention network. Multimed Tools Appl 83, 17941–17960 (2024). https://doi.org/10.1007/s11042-023-16280-2

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  • DOI: https://doi.org/10.1007/s11042-023-16280-2

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