Global research progress and trends of artificial intelligence applications in epilepsy_Journal of Epilepsy

Authors：

 WANG Guangxin ^1,2 , CHEN Zhiqiang ^1,3 , YANG Jingwei ²

1. Innovation Center of Intelligent Diagnosis, Jinan Central Hospital, Jinan 250013, China;
2. Department of Pediatrics, Central Hospital Affiliated to Shandong First Medical University, Jinan 250013, China;
3. School of Clinical Medicine, Weifang Medical University, Weifang 261053, China;

Corresponding author：

WANG Guangxin, Email: 2280887385@qq.com

Keywords：

Artificial intelligence; Epilepsy; Bibliometric analysis; Machine learning; Deep learning

DOI：

10.7507/2096-0247.202309004

Video：

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With the development of artificial intelligence (AI) technology, great progress has been made in the application of AI in the medical field. While foreign journals have published a large number of papers on the application of AI in epilepsy, there is a dearth of studies within domestic journals. In order to understand the global research progress and development trend of AI applications in epilepsy, a total of 895 papers on AI applications in epilepsy included in the Web of Science Core Collection and published before December 31, 2022 were selected as the research objects. The annual number of papers and their cited times, the most published authors, institutions and countries, and their cooperative relationships were analyzed, and the research hotspots and future trends in this field were explored by using bibliometrics and other methods. The results showed that before 2016, the annual number of papers on the application of AI in epilepsy increased slowly, and after 2017, the number of publications increased rapidly. The United States had the largest number of papers (n=273), followed by China (n=195). The institution with the largest number of papers was the University of London (n=36), and Capital Medical University in China had 23 papers. The author with the most published papers was Gregory Worrell (n=14), and the scholar with the most published articles in China was Guo Jiayan from Xiamen University (n=7). The application of machine learning in the diagnosis and treatment of epilepsy is an early research focus in this field, while the seizure prediction model based on EEG feature extraction, deep learning especially convolutional neural network application in epilepsy diagnosis, and cloud computing application in epilepsy healthcare, are the current research priorities in this field. AI-based EEG feature extraction, the application of deep learning in the diagnosis and treatment of epilepsy, and the Internet of things to solve epilepsy health-related problems are the research aims of this field in the future.

Citation： WANG Guangxin, CHEN Zhiqiang, YANG Jingwei. Global research progress and trends of artificial intelligence applications in epilepsy. Journal of Epilepsy, 2023, 9(6): 487-491. doi: 10.7507/2096-0247.202309004 Copy

1.	Tang R, Zhang S, Ding C, et al. Artificial intelligence in intensive care medicine: Bibliometric analysis. J Med Internet Res, 2022, 24(11): e42185.
2.	Mintz Y, Brodie R. Introduction to artificial intelligence in medicine. Minim Invasive Ther Allied Technol, 2019, 28(2): 73-81.
3.	Tran BX, Vu GT, Ha GH, et al. Global evolution of research in artificial intelligence in health and medicine: a bibliometric study. J Clin Med, 2019, 8(3): 360.
4.	Liu PR, Lu L, Zhang JY, et al. Application of artificial intelligence in medicine: An overview. Curr Med Sci, 2021, 41(6): 1105-1115.
5.	Beam AL, Drazen JM, Kohane IS, et al. Artificial intelligence in medicine. N Engl J Med, 2023, 388(13): 1220-1221.
6.	Devinsky O, Vezzani A, O'Brien TJ, et al. Epilepsy. Nat Rev Dis Primers, 2018, 4: 18024.
7.	张远鹏, 蔡可夫, 姚敏, 等. 基于深度堆叠式稀疏回归的癫痫患者脑电信号特征选择. 南通大学学报(医学版), 2021, 41(3): 212-216.
8.	钟霁媛, 陈思翰, 王晗. 基于深度神经网络多模态融合的颞叶内侧癫痫鉴别. 现代计算机, 2019, 19(7): 13-17,40.
9.	石淼, 吕光洙. 国际人工智能与教师教育研究的进展与趋势. 继续教育研究, 2023, 23(5): 19-25.
10.	Kaur T, Diwakar A, Kirandeep, et al. Artificial intelligence in epilepsy. Neurol India, 2021, 69(3): 560-566.
11.	Amin HU, Yusoff MZ, Ahmad RF. A novel approach based on wavelet analysis and arithmetic coding for automated detection and diagnosis of epileptic seizure in EEG signals using machine learning techniques. Biomed Signal Process Control, 2020, 56: 101707.
12.	Raghu S, Sriraam N, Temel Y, et al. EEG based multi-class seizure type classification using convolutional neural network and transfer learning. Neural Networks, 2020, 124: 202-212.
13.	Proix T, Bartolomei F, Guye M, et al. Individual brain structure and modelling predict seizure propagation. Brain, 2017, 140: 641-654.
14.	Jirsa VK, Proix T, Perdikis D, et al. The virtual epileptic patient: individualized whole-brain models of epilepsy spread. Neuroimage, 2017, 145(Pt B): 377-388.
15.	Abbasi B, Goldenholz DM. Machine learning applications in epilepsy. Epilepsia, 2019, 60(10): 2037-2047.
16.	Pandey A, Singh SK, Udmale SS, et al. Epileptic seizure classification using battle royale search and rescue optimization-based deep LSTM. IEEE J Biomed Health Inform, 2022, 26(11): 5494-5505.
17.	Lu L, Zhang F, Wu Y, et al. A multi-frame network model for predicting seizure based on sEEG and iEEG data. Front Comput Neurosci, 2022, 16: 1059565.
18.	Wang Y, Li Z, Zhang Y, et al. Classification of partial seizures based on functional connectivity: a MEG study with support vector machine. Front Neuroinform, 2022, 16: 934480.
19.	Singh K, Malhotra J. Prediction of epileptic seizures from spectral features of intracranial EEG recordings using deep learning approach. Multimedia Tools and Applications, 2022, 81(20): 28875-28898.
20.	Türk Ö, Özerdem MS. Epilepsy detection by using scalogram based convolutional neural network from EEG signals. Brain Sci, 2019, 9(5): 115.
21.	Fernandez-Blanco E, Rivero D, Rabuñal J, et al. Automatic seizure detection based on star graph topological indices. J Neurosci Methods, 2012, 209(2): 410-419.
22.	Li W, Wang G, Lei X, et al. Seizure detection based on wearable devices: a review of device, mechanism, and algorithm. Acta Neurol Scand, 2022, 146(6): 723-731.
23.	Vani S, ChandraSekhar P, Ramanarayan Sankriti, et al. Detection of normal and epileptic EEG signals using by lifting based HAAR wavelet transform and artificial neural network. Int J Syst Assur Eng Manag, 2021, 2021: 1-7.
24.	Singh K, Malhotra J. IoT and cloud computing based automatic epileptic seizure detection using HOS features based random forest classification. J Ambient Intell Hum Comput, 2019, 2019: 1-16.
25.	刘荣. 智能医学(第1版). 北京: 人民卫生出版社, 2018: 30-35.
26.	Webber WR, Litt B, Wilson K, et al. Practical detection of epileptiform discharges (EDs) in the EEG using an artificial neural network: a comparison of raw and parameterized EEG data. Electroencephalogr Clin Neurophysiol, 1994, 91(3): 194-204.
27.	Acharya UR, Oh SL, Hagiwara Y, et al. Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Comput Biol Med, 2018, 100: 270-278.
28.	Slimen IB, Boubchir L, Mbarki Z, et al. EEG epileptic seizure detection and classification based on dual-tree complex wavelet transform and machine learning algorithms. J Biomed Res, 2020, 34(3): 151-161.
29.	Hakeem H, Feng W, Chen Z, et al. Development and validation of a deep learning model for predicting treatment response in patients with newly diagnosed epilepsy. JAMA Neurol, 2022, 79(10): 986-996.
30.	García-Ramó KB, Sanchez-Catasus CA, Winston GP. Deep learning in neuroimaging of epilepsy. Clin Neurol Neurosurg, 2023, 232: 107879.

1. Tang R, Zhang S, Ding C, et al. Artificial intelligence in intensive care medicine: Bibliometric analysis. J Med Internet Res, 2022, 24(11): e42185.
2. Mintz Y, Brodie R. Introduction to artificial intelligence in medicine. Minim Invasive Ther Allied Technol, 2019, 28(2): 73-81.
3. Tran BX, Vu GT, Ha GH, et al. Global evolution of research in artificial intelligence in health and medicine: a bibliometric study. J Clin Med, 2019, 8(3): 360.
4. Liu PR, Lu L, Zhang JY, et al. Application of artificial intelligence in medicine: An overview. Curr Med Sci, 2021, 41(6): 1105-1115.
5. Beam AL, Drazen JM, Kohane IS, et al. Artificial intelligence in medicine. N Engl J Med, 2023, 388(13): 1220-1221.
6. Devinsky O, Vezzani A, O'Brien TJ, et al. Epilepsy. Nat Rev Dis Primers, 2018, 4: 18024.
7. 张远鹏, 蔡可夫, 姚敏, 等. 基于深度堆叠式稀疏回归的癫痫患者脑电信号特征选择. 南通大学学报(医学版), 2021, 41(3): 212-216.
8. 钟霁媛, 陈思翰, 王晗. 基于深度神经网络多模态融合的颞叶内侧癫痫鉴别. 现代计算机, 2019, 19(7): 13-17,40.
9. 石淼, 吕光洙. 国际人工智能与教师教育研究的进展与趋势. 继续教育研究, 2023, 23(5): 19-25.
10. Kaur T, Diwakar A, Kirandeep, et al. Artificial intelligence in epilepsy. Neurol India, 2021, 69(3): 560-566.
11. Amin HU, Yusoff MZ, Ahmad RF. A novel approach based on wavelet analysis and arithmetic coding for automated detection and diagnosis of epileptic seizure in EEG signals using machine learning techniques. Biomed Signal Process Control, 2020, 56: 101707.
12. Raghu S, Sriraam N, Temel Y, et al. EEG based multi-class seizure type classification using convolutional neural network and transfer learning. Neural Networks, 2020, 124: 202-212.
13. Proix T, Bartolomei F, Guye M, et al. Individual brain structure and modelling predict seizure propagation. Brain, 2017, 140: 641-654.
14. Jirsa VK, Proix T, Perdikis D, et al. The virtual epileptic patient: individualized whole-brain models of epilepsy spread. Neuroimage, 2017, 145(Pt B): 377-388.
15. Abbasi B, Goldenholz DM. Machine learning applications in epilepsy. Epilepsia, 2019, 60(10): 2037-2047.
16. Pandey A, Singh SK, Udmale SS, et al. Epileptic seizure classification using battle royale search and rescue optimization-based deep LSTM. IEEE J Biomed Health Inform, 2022, 26(11): 5494-5505.
17. Lu L, Zhang F, Wu Y, et al. A multi-frame network model for predicting seizure based on sEEG and iEEG data. Front Comput Neurosci, 2022, 16: 1059565.
18. Wang Y, Li Z, Zhang Y, et al. Classification of partial seizures based on functional connectivity: a MEG study with support vector machine. Front Neuroinform, 2022, 16: 934480.
19. Singh K, Malhotra J. Prediction of epileptic seizures from spectral features of intracranial EEG recordings using deep learning approach. Multimedia Tools and Applications, 2022, 81(20): 28875-28898.
20. Türk Ö, Özerdem MS. Epilepsy detection by using scalogram based convolutional neural network from EEG signals. Brain Sci, 2019, 9(5): 115.
21. Fernandez-Blanco E, Rivero D, Rabuñal J, et al. Automatic seizure detection based on star graph topological indices. J Neurosci Methods, 2012, 209(2): 410-419.
22. Li W, Wang G, Lei X, et al. Seizure detection based on wearable devices: a review of device, mechanism, and algorithm. Acta Neurol Scand, 2022, 146(6): 723-731.
23. Vani S, ChandraSekhar P, Ramanarayan Sankriti, et al. Detection of normal and epileptic EEG signals using by lifting based HAAR wavelet transform and artificial neural network. Int J Syst Assur Eng Manag, 2021, 2021: 1-7.
24. Singh K, Malhotra J. IoT and cloud computing based automatic epileptic seizure detection using HOS features based random forest classification. J Ambient Intell Hum Comput, 2019, 2019: 1-16.
25. 刘荣. 智能医学(第1版). 北京: 人民卫生出版社, 2018: 30-35.
26. Webber WR, Litt B, Wilson K, et al. Practical detection of epileptiform discharges (EDs) in the EEG using an artificial neural network: a comparison of raw and parameterized EEG data. Electroencephalogr Clin Neurophysiol, 1994, 91(3): 194-204.
27. Acharya UR, Oh SL, Hagiwara Y, et al. Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Comput Biol Med, 2018, 100: 270-278.
28. Slimen IB, Boubchir L, Mbarki Z, et al. EEG epileptic seizure detection and classification based on dual-tree complex wavelet transform and machine learning algorithms. J Biomed Res, 2020, 34(3): 151-161.
29. Hakeem H, Feng W, Chen Z, et al. Development and validation of a deep learning model for predicting treatment response in patients with newly diagnosed epilepsy. JAMA Neurol, 2022, 79(10): 986-996.
30. García-Ramó KB, Sanchez-Catasus CA, Winston GP. Deep learning in neuroimaging of epilepsy. Clin Neurol Neurosurg, 2023, 232: 107879.

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