Application of spike automatic detection in long-term EEG of patients with temporal lobe epilepsy_Journal of Epilepsy

Authors：

LU Yeting ¹ , LAI Feng ¹ , LI Bingyi ² , ZHU Xiangyi ¹ , PAN Shengjie ¹ , XU Min ¹ , FANG Qi ¹ ,  HU Xiaowei ¹

1. Department of Neurology, the First Affiliated Hospital of Soochow University, Suzhou 215006, China;
2. Suzhou University of Science and Technology, Suzhou 215009, China;

Corresponding author：

HU Xiaowei, Email: huxiaowei@suda.edu.cn

Keywords：

Epilepsy; Interictal epileptic discharge; Spike wave; Automatic detection

DOI：

10.7507/2096-0247.202209011

Video：

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Objective To explore the clinical value of Persyst automatic detection of spike waves in adult patients with temporal lobe epilepsy. Methods EEG recordings were continuously concluded from the Epilepsy Unit of the First Affiliated Hospital of Soochow University during 2019.1.1 to 2019.12.31. Two EEG experts certified by the Chinese Anti-Epileptic Association marked interictal epileptic discharge in the long-time electroencephalogram that meet the criteria. Consistent results of the two experts were seen as the "golden standard". The sensitivity and false positive rates were calculated compared with the automatic test results of Persyst version 11, 13 and 14. Results 7 cases were included, each with a recording time of 24~25 hours and a total of 169 hours. Two expert readers achieved the consistency of 43.09%. Spike waves detected automatically were much more than manually. The sensitivity was as high as 62.26%, 77.0% and 67.28%. The lowest false positive rate was 0.37/min, 0.85/min and 0.46/min respectively. Automatic analysis achieved an average workload reduction of 14.59%~37.05%. Conclusions Persyst automatic spike detection has the acceptable sensitivity and false positive rate. It differs from versions and need to be further combined with expert readers.Less workload and accuracy can be balanced by setting reasonable perception parameter.

Citation： LU Yeting, LAI Feng, LI Bingyi, ZHU Xiangyi, PAN Shengjie, XU Min, FANG Qi, HU Xiaowei. Application of spike automatic detection in long-term EEG of patients with temporal lobe epilepsy. Journal of Epilepsy, 2023, 9(1): 5-10. doi: 10.7507/2096-0247.202209011 Copy

1.	Jing J, Herlopian A, Karakis I, et al. Interrater reliability of experts in identifying interictal epileptiform discharges in electroencephalograms. JAMA Neurol, 2020, 77(1): 49-57.
2.	Fürbass F, Kural MA, Gritsch G, et al. An artificial intelligence-based EEG algorithm for detection of epileptiform EEG discharges: Validation against the diagnostic gold standard. Clin Neurophysiol, 2020, 131(6): 1174-1179.
3.	Reus EEM, Visser GH, Cox FME. Using sampled visual EEG review in combination with automated detection software at the EMU. Seizure, 2020, 80: 96-99.
4.	Golmohammadi M, Harati Nejad Torbati AH, Lopez de Diego S, et al. Automatic analysis of eegs using big data and hybrid deep learning architectures. Front Hum Neurosci, 2019, 13: 76.
5.	Wilson SB, Harner RN, Duffy FH, et al. Spike detection. I. correlation and reliability of human experts. Electroencephalogr Clin Neurophysiol, 1996, 98(3): 186-198.
6.	Wilson SB, Turner CA, Emerson RG, et al. Spike detection II: automatic, perception-based detection and clustering. Clin Neurophysiol, 1999, 110(3): 404-411.
7.	Hughes JR. The significance of the interictal spike discharge: a review. J Clin Neurophysiol, 1989, 6(3): 207-226.
8.	Asadollahi M, Noorbakhsh M, Salehifar V, et al. The significance of interictal spike frequency in temporal lobe epilepsy. Clin EEG Neurosci, 2020, 51(3): 180-184.
9.	Krendl R, Lurger S, Baumgartner C. Absolute spike frequency predicts surgical outcome in TLE with unilateral hippocampal atrophy. Neurology, 2008, 71(6): 413-418.
10.	Fisher RS, Acevedo C, Arzimanoglou A, et al. ILAE official report: a practical clinical definition of epilepsy. Epilepsia, 2014, 55(4): 475-482.
11.	Proposal for revised classification of epilepsies and epileptic syndromes. Commission on Classification and Terminology of the International League Against Epilepsy. Epilepsia, 1989, 30(4): 389-399.
12.	Engel JJr. A proposed diagnostic scheme for people with epileptic seizures and with epilepsy: report of the ILAE Task Force on Classification and Terminology. Epilepsia, 2001, 42(6): 796-803.
13.	Fisher RS, Cross JH, French JA, et al. Operational classification of seizure types by the International League Against Epilepsy: Position Paper of the ILAE Commission for Classification and Terminology. Epilepsia, 2017, 58(4): 522-530.
14.	Kane N, Acharya J, Benickzy S, et al. A revised glossary of terms most commonly used by clinical electroencephalographers and updated proposal for the report format of the EEG findings. Revision 2017. Clin Neurophysiol Pract, 2017, 2: 170-185.
15.	Black MA, Jones RD, Carroll GJ, et al. Real-time detection of epileptiform activity in the EEG: a blinded clinical trial. Clin Electroencephalogr, 2000, 31(3): 122-130.
16.	Scheuer ML, Bagic A, Wilson SB. Spike detection: Inter-reader agreement and a statistical Turing test on a large data set. Clin Neurophysiol, 2017, 128(1): 243-250.
17.	Gotman J, Gloor P, Schaul N. Comparison of traditional reading of the EEG and automatic recognition of interictal epileptic activity. Electroencephalogr Clin Neurophysiol, 1978, 44(1): 48-60.
18.	Stroink H, Schimsheimer RJ, de Weerd AW, et al. Interobserver reliability of visual interpretation of electroencephalograms in children with newly diagnosed seizures. Dev Med Child Neurol, 2006, 48(5): 374-377.
19.	Webber WR, Litt B, Lesser RP, et al. Automatic EEG spike detection: what should the computer imitate? Electroencephalogr Clin Neurophysiol, 1993, 87(6): 364-373.
20.	Bagheri E, Dauwels J, Dean BC, et al. Interictal epileptiform discharge characteristics underlying expert interrater agreement. Clin Neurophysiol, 2017, 128(10): 1994-2005.
21.	Halford JJ, Arain A, Kalamangalam GP, et al. Characteristics of EEG interpreters associated with higher interrater agreement. J Clin Neurophysiol, 2017, 34(2): 168-173.
22.	Halford JJ, Westover MB, LaRoche SM, et al. Interictal epileptiform discharge detection in eeg in different practice settings. J Clin Neurophysiol, 2018, 35(5): 375-380.
23.	Halford JJ, Pressly WB, Benbadis SR, et al. Web-based collection of expert opinion on routine scalp EEG: software development and interrater reliability. J Clin Neurophysiol, 2011, 28(2): 178-184.
24.	Halford JJ, Schalkoff RJ, Zhou J, et al. Standardized database development for EEG epileptiform transient detection: EEGnet scoring system and machine learning analysis. J Neurosci Methods, 2013, 212(2): 308-316.
25.	Joshi CN, Chapman KE, Bear JJ, et al. Semiautomated spike detection software persyst 13 is noninferior to human readers when calculating the spike-wave index in electrical status epilepticus in sleep. J Clin Neurophysiol, 2018, 35(5): 370-374.
26.	Reus EEM, Visser GH, Cox FME. Determining the spike-wave index using automated detection software. J Clin Neurophysiol, 2021, 38(3): 198-201.

1. Jing J, Herlopian A, Karakis I, et al. Interrater reliability of experts in identifying interictal epileptiform discharges in electroencephalograms. JAMA Neurol, 2020, 77(1): 49-57.
2. Fürbass F, Kural MA, Gritsch G, et al. An artificial intelligence-based EEG algorithm for detection of epileptiform EEG discharges: Validation against the diagnostic gold standard. Clin Neurophysiol, 2020, 131(6): 1174-1179.
3. Reus EEM, Visser GH, Cox FME. Using sampled visual EEG review in combination with automated detection software at the EMU. Seizure, 2020, 80: 96-99.
4. Golmohammadi M, Harati Nejad Torbati AH, Lopez de Diego S, et al. Automatic analysis of eegs using big data and hybrid deep learning architectures. Front Hum Neurosci, 2019, 13: 76.
5. Wilson SB, Harner RN, Duffy FH, et al. Spike detection. I. correlation and reliability of human experts. Electroencephalogr Clin Neurophysiol, 1996, 98(3): 186-198.
6. Wilson SB, Turner CA, Emerson RG, et al. Spike detection II: automatic, perception-based detection and clustering. Clin Neurophysiol, 1999, 110(3): 404-411.
7. Hughes JR. The significance of the interictal spike discharge: a review. J Clin Neurophysiol, 1989, 6(3): 207-226.
8. Asadollahi M, Noorbakhsh M, Salehifar V, et al. The significance of interictal spike frequency in temporal lobe epilepsy. Clin EEG Neurosci, 2020, 51(3): 180-184.
9. Krendl R, Lurger S, Baumgartner C. Absolute spike frequency predicts surgical outcome in TLE with unilateral hippocampal atrophy. Neurology, 2008, 71(6): 413-418.
10. Fisher RS, Acevedo C, Arzimanoglou A, et al. ILAE official report: a practical clinical definition of epilepsy. Epilepsia, 2014, 55(4): 475-482.
11. Proposal for revised classification of epilepsies and epileptic syndromes. Commission on Classification and Terminology of the International League Against Epilepsy. Epilepsia, 1989, 30(4): 389-399.
12. Engel JJr. A proposed diagnostic scheme for people with epileptic seizures and with epilepsy: report of the ILAE Task Force on Classification and Terminology. Epilepsia, 2001, 42(6): 796-803.
13. Fisher RS, Cross JH, French JA, et al. Operational classification of seizure types by the International League Against Epilepsy: Position Paper of the ILAE Commission for Classification and Terminology. Epilepsia, 2017, 58(4): 522-530.
14. Kane N, Acharya J, Benickzy S, et al. A revised glossary of terms most commonly used by clinical electroencephalographers and updated proposal for the report format of the EEG findings. Revision 2017. Clin Neurophysiol Pract, 2017, 2: 170-185.
15. Black MA, Jones RD, Carroll GJ, et al. Real-time detection of epileptiform activity in the EEG: a blinded clinical trial. Clin Electroencephalogr, 2000, 31(3): 122-130.
16. Scheuer ML, Bagic A, Wilson SB. Spike detection: Inter-reader agreement and a statistical Turing test on a large data set. Clin Neurophysiol, 2017, 128(1): 243-250.
17. Gotman J, Gloor P, Schaul N. Comparison of traditional reading of the EEG and automatic recognition of interictal epileptic activity. Electroencephalogr Clin Neurophysiol, 1978, 44(1): 48-60.
18. Stroink H, Schimsheimer RJ, de Weerd AW, et al. Interobserver reliability of visual interpretation of electroencephalograms in children with newly diagnosed seizures. Dev Med Child Neurol, 2006, 48(5): 374-377.
19. Webber WR, Litt B, Lesser RP, et al. Automatic EEG spike detection: what should the computer imitate? Electroencephalogr Clin Neurophysiol, 1993, 87(6): 364-373.
20. Bagheri E, Dauwels J, Dean BC, et al. Interictal epileptiform discharge characteristics underlying expert interrater agreement. Clin Neurophysiol, 2017, 128(10): 1994-2005.
21. Halford JJ, Arain A, Kalamangalam GP, et al. Characteristics of EEG interpreters associated with higher interrater agreement. J Clin Neurophysiol, 2017, 34(2): 168-173.
22. Halford JJ, Westover MB, LaRoche SM, et al. Interictal epileptiform discharge detection in eeg in different practice settings. J Clin Neurophysiol, 2018, 35(5): 375-380.
23. Halford JJ, Pressly WB, Benbadis SR, et al. Web-based collection of expert opinion on routine scalp EEG: software development and interrater reliability. J Clin Neurophysiol, 2011, 28(2): 178-184.
24. Halford JJ, Schalkoff RJ, Zhou J, et al. Standardized database development for EEG epileptiform transient detection: EEGnet scoring system and machine learning analysis. J Neurosci Methods, 2013, 212(2): 308-316.
25. Joshi CN, Chapman KE, Bear JJ, et al. Semiautomated spike detection software persyst 13 is noninferior to human readers when calculating the spike-wave index in electrical status epilepticus in sleep. J Clin Neurophysiol, 2018, 35(5): 370-374.
26. Reus EEM, Visser GH, Cox FME. Determining the spike-wave index using automated detection software. J Clin Neurophysiol, 2021, 38(3): 198-201.

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Application of spike automatic detection in long-term EEG of patients with temporal lobe epilepsy

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