Sampling intervals dependent feature extraction for state transfer networks of epileptic signals_Journal of Biomedical Engineering

Authors：

 ZHANG Lei , YAN Shuang , GU Changgui

Department of Systems Science, Business School, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China;

Corresponding?author：

ZHANG Lei, Email: ra496799985@163.com

Keywords：

Stereo-electroencephalography; Visibility graphlet; State transfer network; Motifs; Feature extraction

DOI：

10.7507/1001-5515.202406023

Video：

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Abstract Full text Figures/Tables Video References Cited by

Epileptic seizures and the interictal epileptiform discharges both have similar waveforms. And a method to effectively extract features that can be used to distinguish seizures is of crucial importance both in theory and clinical practice. We constructed state transfer networks by using visibility graphlet at multiple sampling intervals and analyzed network features. We found that the characteristics waveforms in ictal periods were more robust with various sampling intervals, and those feature network structures did not change easily in the range of the smaller sampling intervals. Inversely, the feature network structures of interictal epileptiform discharges were stable in range of relatively larger sampling intervals. Furthermore, the feature nodes in networks during ictal periods showed long-term correlation along the process, and played an important role in regulating system behavior. For stereo-electroencephalography at around 500 Hz, the greatest difference between ictal and the interictal epileptiform occurred at the sampling interval around 0.032 s. In conclusion, this study effectively reveals the correlation between the features of pathological changes in brain system and the multiple sampling intervals, which holds potential application value in clinical diagnosis for identifying, classifying, and predicting epilepsy.

Citation： ZHANG Lei, YAN Shuang, GU Changgui. Sampling intervals dependent feature extraction for state transfer networks of epileptic signals. Journal of Biomedical Engineering, 2024, 41(6): 1128-1136. doi: 10.7507/1001-5515.202406023 Copy

1.	Bartolomei F, Lagarde S, Wendling F, et al. 癲癇網絡的定義: 立體腦電圖和信號分析的貢獻, 鄭舒暢, 譯. 癲癇雜志, 2018, 4(2): 135-149.
2.	單寶蓮, 張力新, 徐舫舟, 等. 基于腦電信號的癲癇發作預測特征及識別. 生物化學與生物物理進展, 2023, 50(2): 322-333.
3.	Thanh L T, Dao N T A, Dung N V, et al. Multi-channel EEG epileptic spike detection by a new method of tensor decomposition. J Neural Eng, 2020, 17(1): 016023.
4.	朱丹. 癲癇的診斷與治療—臨床實踐與思考. 北京: 人民衛生出版社, 2017: 624-700.
5.	蹇兆鑫. 顳葉癲癇小鼠海馬網絡中的高—低頻信號研究. 成都: 電子科技大學, 2023.
6.	王小艷. 基于多尺度轉移網絡的非線性時間序列分析. 上海: 華東師范大學, 2023.
7.	Gao Z, Yang Y, Cai Q. Temporal complex network analysis//Hu L, Zhang Z. EEG signal processing and feature extraction. Cham: Springer International Publishing, 2019: 287-300.
8.	汪文杰, 姚旭峰. 基于人工智能的癲癇發作預測研究綜述. 軟件工程, 2024, 27(4): 1-5.
9.	Yang Y, Zhou M, Niu Y, et al. Epileptic seizure prediction based on permutation entropy. Front Comput Neurosci, 2018: 12-55.
10.	張瑞, 宋江玲, 胡文鳳. 癲癇腦電的特征提取方法綜述. 西北大學學報(自然科學版), 2016, 46(6): 781-788,794.
11.	Lacasa L, Luque B, Ballesteros F, et al. From time series to complex networks: the visibility graph. Proc Natl Acad Sci U S A, 2008, 105(13): 4972-4975.
12.	任彥霖. 基于復雜網絡拓撲結構的腦電信號非線性分析. 江蘇: 中國礦業大學, 2023.
13.	李霞, 李守偉. 基于EMD與DVG的非線性時間序列預測模型及其應用研究. 中國管理科學, 2022, 30(9): 275-286.
14.	Ribeiro P M. Efficient and scalable algorithms for network motifs discovery. Portugal: Universidade Do Porto, 2011.
15.	Sporns O, K?tter R. Motifs in brain networks. PLoS Biology, 2004, 2(11): e369.
16.	Buckner R L, DiNicola L M. The brain's default network: updated anatomy, physiology and evolving insights. Nat Rev Neurosci, 2019, 20(10): 593-608.
17.	Shen-Orr S S, Milo R, Mangan S, et al. Network motifs in the transcriptional regulation network of Escherichia coli. Nat Genet, 2002, 31(1): 64-68.
18.	Stephen M, Gu C, Yang H. Visibility graph based time series analysis. PLoS ONE, 2015, 10(11): e0143015.
19.	韋雷. 難治性癲癇腦電背景活動的非線性特征. 南寧: 廣西醫科大學, 2020.
20.	熊馨, 羅劍花, 武瑞鋒, 等. 基于微狀態方法的癲癇腦電信號識別研究. 傳感技術學報, 2022, 35(12): 1671-1677.
21.	蔡冬梅, 周衛東, 劉凱, 等. 基于Hurst指數和SVM的癲癇腦電檢測方法. 中國生物醫學工程學報, 2010, 29(6): 836-840.
22.	崔剛強, 夏良斌, 梁建峰, 等. 基于小波多尺度分析和極限學習機的癲癇腦電分類算法. 生物醫學工程學雜志, 2016, 33(6): 1025-1030,1038.
23.	Wang Y, Weng T, Deng S, et al. Sampling frequency dependent visibility graphlet approach to time series. Chaos, 2019, 29(2): 023109.
24.	Milo R, Shen-Orr S, Itzkovitz S, et al. Network motifs: simple building blocks of complex networks. Science, 2002, 298(5594): 824-827.
25.	Hamed K H. Improved finite-sample Hurst exponent estimates using rescaled range analysis. Water Resour Res, 2007, 43: W04413.
26.	Bassingthwaighte J B, Raymond G M. Evaluating rescaled range analysis for time series. Ann Biomed Eng, 1994, 22: 432-444.
27.	Bernabei J M, Li A, Revell A Y, et al. OpenNeuro. (2022-04-17)[2023-03-22]. https://openneuro.org/datasets/ds004100/versions/1.1.3.
28.	Bartolomei F, Chauvel P, Wendling F. Epileptogenicity of brain structures in human temporal lobe epilepsy: a quantified study from intracerebral EEG. Brain, 2008, 131(Pt 7): 1818-1830.
29.	劉曉燕. 臨床腦電圖學(第2版). 北京: 人民衛生出版社, 2011: 179-193.
30.	Sleimen-Malkoun R, Perdikis D, Müller V, et al. Brain dynamics of aging: multiscale variability of EEG signals at rest and during an auditory oddball task. eNeuro, 2015, 2(3): ENEURO. 0067-14.
31.	Hagen E, Magnusson S H, Ness T V, et al. Brain signal predictions from multi-scale networks using a linearized framework. PLoS Comput Biol, 2022, 18(8): e1010353.
32.	Delic J, Alhilali L M, Hughes M A, et al. White matter injuries in mild traumatic brain injury and posttraumatic migraines: Diffusion entropy analysis. Radiology, 2016, 279(3): 859-866.
33.	Pan X, Hou L, Stephen M, et al. Evaluation of scaling invariance embedded in short time series. PLoS One, 2014, 9(12): e116128.
34.	Cohen M X. Effects of time lag and frequency matching on phase-based connectivity. J Neurosci Methods, 2015, 250: 137-146.
35.	蔣絲麗, 羅華, 阮江海. 基于EEG的失神癲癇發作間期腦功能連接動態改變. 北京生物醫學工程, 2022, 41(4): 368-373.
36.	Hadra M, Omidvarnia A, Mesbah M. Temporal complexity of EEG encodes human alertness. Physiol Meas, 2022, 43(9): 095002.
37.	Liu Y, Zeng W, Pan N, et al. EEG complexity correlates with residual consciousness level of disorders of consciousness. BMC Neurol. 2023, 23(1): 140.

1. Bartolomei F, Lagarde S, Wendling F, et al. 癲癇網絡的定義: 立體腦電圖和信號分析的貢獻, 鄭舒暢, 譯. 癲癇雜志, 2018, 4(2): 135-149.
2. 單寶蓮, 張力新, 徐舫舟, 等. 基于腦電信號的癲癇發作預測特征及識別. 生物化學與生物物理進展, 2023, 50(2): 322-333.
3. Thanh L T, Dao N T A, Dung N V, et al. Multi-channel EEG epileptic spike detection by a new method of tensor decomposition. J Neural Eng, 2020, 17(1): 016023.
4. 朱丹. 癲癇的診斷與治療—臨床實踐與思考. 北京: 人民衛生出版社, 2017: 624-700.
5. 蹇兆鑫. 顳葉癲癇小鼠海馬網絡中的高—低頻信號研究. 成都: 電子科技大學, 2023.
6. 王小艷. 基于多尺度轉移網絡的非線性時間序列分析. 上海: 華東師范大學, 2023.
7. Gao Z, Yang Y, Cai Q. Temporal complex network analysis//Hu L, Zhang Z. EEG signal processing and feature extraction. Cham: Springer International Publishing, 2019: 287-300.
8. 汪文杰, 姚旭峰. 基于人工智能的癲癇發作預測研究綜述. 軟件工程, 2024, 27(4): 1-5.
9. Yang Y, Zhou M, Niu Y, et al. Epileptic seizure prediction based on permutation entropy. Front Comput Neurosci, 2018: 12-55.
10. 張瑞, 宋江玲, 胡文鳳. 癲癇腦電的特征提取方法綜述. 西北大學學報(自然科學版), 2016, 46(6): 781-788,794.
11. Lacasa L, Luque B, Ballesteros F, et al. From time series to complex networks: the visibility graph. Proc Natl Acad Sci U S A, 2008, 105(13): 4972-4975.
12. 任彥霖. 基于復雜網絡拓撲結構的腦電信號非線性分析. 江蘇: 中國礦業大學, 2023.
13. 李霞, 李守偉. 基于EMD與DVG的非線性時間序列預測模型及其應用研究. 中國管理科學, 2022, 30(9): 275-286.
14. Ribeiro P M. Efficient and scalable algorithms for network motifs discovery. Portugal: Universidade Do Porto, 2011.
15. Sporns O, K?tter R. Motifs in brain networks. PLoS Biology, 2004, 2(11): e369.
16. Buckner R L, DiNicola L M. The brain's default network: updated anatomy, physiology and evolving insights. Nat Rev Neurosci, 2019, 20(10): 593-608.
17. Shen-Orr S S, Milo R, Mangan S, et al. Network motifs in the transcriptional regulation network of Escherichia coli. Nat Genet, 2002, 31(1): 64-68.
18. Stephen M, Gu C, Yang H. Visibility graph based time series analysis. PLoS ONE, 2015, 10(11): e0143015.
19. 韋雷. 難治性癲癇腦電背景活動的非線性特征. 南寧: 廣西醫科大學, 2020.
20. 熊馨, 羅劍花, 武瑞鋒, 等. 基于微狀態方法的癲癇腦電信號識別研究. 傳感技術學報, 2022, 35(12): 1671-1677.
21. 蔡冬梅, 周衛東, 劉凱, 等. 基于Hurst指數和SVM的癲癇腦電檢測方法. 中國生物醫學工程學報, 2010, 29(6): 836-840.
22. 崔剛強, 夏良斌, 梁建峰, 等. 基于小波多尺度分析和極限學習機的癲癇腦電分類算法. 生物醫學工程學雜志, 2016, 33(6): 1025-1030,1038.
23. Wang Y, Weng T, Deng S, et al. Sampling frequency dependent visibility graphlet approach to time series. Chaos, 2019, 29(2): 023109.
24. Milo R, Shen-Orr S, Itzkovitz S, et al. Network motifs: simple building blocks of complex networks. Science, 2002, 298(5594): 824-827.
25. Hamed K H. Improved finite-sample Hurst exponent estimates using rescaled range analysis. Water Resour Res, 2007, 43: W04413.
26. Bassingthwaighte J B, Raymond G M. Evaluating rescaled range analysis for time series. Ann Biomed Eng, 1994, 22: 432-444.
27. Bernabei J M, Li A, Revell A Y, et al. OpenNeuro. (2022-04-17)[2023-03-22]. https://openneuro.org/datasets/ds004100/versions/1.1.3.
28. Bartolomei F, Chauvel P, Wendling F. Epileptogenicity of brain structures in human temporal lobe epilepsy: a quantified study from intracerebral EEG. Brain, 2008, 131(Pt 7): 1818-1830.
29. 劉曉燕. 臨床腦電圖學(第2版). 北京: 人民衛生出版社, 2011: 179-193.
30. Sleimen-Malkoun R, Perdikis D, Müller V, et al. Brain dynamics of aging: multiscale variability of EEG signals at rest and during an auditory oddball task. eNeuro, 2015, 2(3): ENEURO. 0067-14.
31. Hagen E, Magnusson S H, Ness T V, et al. Brain signal predictions from multi-scale networks using a linearized framework. PLoS Comput Biol, 2022, 18(8): e1010353.
32. Delic J, Alhilali L M, Hughes M A, et al. White matter injuries in mild traumatic brain injury and posttraumatic migraines: Diffusion entropy analysis. Radiology, 2016, 279(3): 859-866.
33. Pan X, Hou L, Stephen M, et al. Evaluation of scaling invariance embedded in short time series. PLoS One, 2014, 9(12): e116128.
34. Cohen M X. Effects of time lag and frequency matching on phase-based connectivity. J Neurosci Methods, 2015, 250: 137-146.
35. 蔣絲麗, 羅華, 阮江海. 基于EEG的失神癲癇發作間期腦功能連接動態改變. 北京生物醫學工程, 2022, 41(4): 368-373.
36. Hadra M, Omidvarnia A, Mesbah M. Temporal complexity of EEG encodes human alertness. Physiol Meas, 2022, 43(9): 095002.
37. Liu Y, Zeng W, Pan N, et al. EEG complexity correlates with residual consciousness level of disorders of consciousness. BMC Neurol. 2023, 23(1): 140.

Journal of Biomedical Engineering

Sampling intervals dependent feature extraction for state transfer networks of epileptic signals

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