Preliminary analysis of epilepsy patients’ EEG big data based on CAAE new EEG reporting system_Journal of Epilepsy

Authors：

LIU Yi’ou ¹ , SHI Jie ¹ , LI Xuesong ² , YAN Chao ² , ZHANG Bingqing ¹ , HE Jing ¹ , WANG Siyu ¹ ,  ZHOU Wenjing ¹

1. Epilepsy Center of Yuquan Hospital Tsinghua University, Beijing 100040, China;
2. School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100040, China;

Corresponding author：

ZHOU Wenjing, Email: closejeo@hotmail.com

Keywords：

Epilepsy; New EEG reporting system; Big data

DOI：

10.7507/2096-0247.202209001

Video：

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Objective To discuss the scientific research and application value of the new China Association Against Epilepsy (CAAE) EEG reporting system, and to explore the model of establishing EEG database of tertiary comprehensive epilepsy center. Methods A retrospective study was performed on outpatients who underwent EEG examination at the Epilepsy Center of Tsinghua University Yuquan Hospital from May 2021 to May 2022, and who also received EEG reports using the CAAE new EEG reporting system. We integrated the data of these 6380 patients with the previous database of our Epilepsy Center, and combined the two for the preliminary big data analysis. Results Among 6380 patients, normal EEG was reported in 2253 cases (35.3%) ,abnormal EEG in 4031 cases (63.2%), no definite abnormality in 96 cases. According to age groups, there were 3290 cases in children (51.0%), 1372 cases in adults (22.0%), 753 cases in adolescents (12.0%), 730 cases in infants (11.0%) and 235 cases in infants (4.0%).A total of 1466 (23.0%) patients were recorded with paroxysmal events, including 874 (60.0%) epileptic events. 517 (35.0%) non-epileptic events. Conclusion The new EEG reporting system can provide a large number of researchable EEG data to guide clinical work, and it is an important tool for data sharing and big data research in the future.

Citation： LIU Yi’ou, SHI Jie, LI Xuesong, YAN Chao, ZHANG Bingqing, HE Jing, WANG Siyu, ZHOU Wenjing. Preliminary analysis of epilepsy patients’ EEG big data based on CAAE new EEG reporting system. Journal of Epilepsy, 2022, 8(6): 524-528. doi: 10.7507/2096-0247.202209001 Copy

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5.	Beniczky S, Aurlien H, Brøgger JC, et al. Standardized computer-based organized reporting of EEG: SCORE - second version. Clin Neurophysiol, 2017, 128(11): 2334-2346.
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7.	Bodenreider O. Biomedical ontologies in action: role in knowledge management, data integration and decision support. Yearb Med Inform, 2008: 67-79.
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9.	Kim LG, Johnson TL, Marson AG, et al. Prediction of risk of seizure recurrence after a single seizure and early epilepsy: further results from the MESS trial. Lancet Neurol, 2006, 5(4): 317-322.
10.	Gaspard N, Hirsch LJ, LaRoche SM, et al. Critical care EEG monitoring research consortium. Interrater agreement for Critical Care EEG Terminology. Epilepsia, 2014, 55(9): 1366-1373.
11.	Kearney H, Byrne S, Cavalleri GL, et al. Tackling epilepsy with high-definition precision medicine: a review. JAMA Neurol, 2019, 76(9): 1109-1116.
12.	Kini LG, Davis KA, Wagenaar JB. Data integration: combined imaging and electrophysiology data in the cloud. Neuroimage, 2016, 124(Pt B): 1175-1181.

1. Wang F, Casalino LP, Khullar D. Deep learning in medicine—promise, progress, and challenges. JAMA Intern Med, 2019, 179: 293-294.
2. Miotto R, Wang F, Wang S, et al. Deep learning for healthcare: review, opportunities and challenges. Brief Bioinform, 2018, 19(6): 1236-1246.
3. Lhatoo SD, Bernasconi N, Blumcke I, et al. Big data in epilepsy: clinical and research considerations. Report from the Epilepsy Big Data Task Force of the International League Against Epilepsy. Epilepsia, 2020, 61(9): 1869-1883.
4. Li X, Cui L, Zhang GQ, Lhatoo SD. Can big data guide prognosis and clinical decisions in epilepsy? Epilepsia, 2021, 62(Suppl 2): S106-S115.
5. Beniczky S, Aurlien H, Brøgger JC, et al. Standardized computer-based organized reporting of EEG: SCORE - second version. Clin Neurophysiol, 2017, 128(11): 2334-2346.
6. Beniczky S, Hirsch LJ, Kaplan PW, et al. Unified EEG terminology and criteria for nonconvulsive status epilepticus. Epilepsia, 2013, 54(Suppl 6): 28-29.
7. Bodenreider O. Biomedical ontologies in action: role in knowledge management, data integration and decision support. Yearb Med Inform, 2008: 67-79.
8. Schulze-Bonhage A, Feldwisch-Drentrup H, Ihle M. The role of high-quality EEG databases in the improvement and assessment of seizure prediction methods. Epilepsy Behav, 2011, 22(Suppl 1): S88-S93.
9. Kim LG, Johnson TL, Marson AG, et al. Prediction of risk of seizure recurrence after a single seizure and early epilepsy: further results from the MESS trial. Lancet Neurol, 2006, 5(4): 317-322.
10. Gaspard N, Hirsch LJ, LaRoche SM, et al. Critical care EEG monitoring research consortium. Interrater agreement for Critical Care EEG Terminology. Epilepsia, 2014, 55(9): 1366-1373.
11. Kearney H, Byrne S, Cavalleri GL, et al. Tackling epilepsy with high-definition precision medicine: a review. JAMA Neurol, 2019, 76(9): 1109-1116.
12. Kini LG, Davis KA, Wagenaar JB. Data integration: combined imaging and electrophysiology data in the cloud. Neuroimage, 2016, 124(Pt B): 1175-1181.

Journal of Epilepsy

Preliminary analysis of epilepsy patients’ EEG big data based on CAAE new EEG reporting system

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