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

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5.	蹇兆鑫. 颞叶癫痫小鼠海马网络中的高—低频信号研究. 成都: 电子科技大学, 2023.
6.	王小艳. 基于多尺度转移网络的非线性时间序列分析. 上海: 华东师范大学, 2023.
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8.	汪文杰, 姚旭峰. 基于人工智能的癫痫发作预测研究综述. 软件工程, 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.	张瑞, 宋江玲, 胡文凤. 癫痫脑电的特征提取方法综述. 西北大学学报(自然科学版), 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.	任彦霖. 基于复杂网络拓扑结构的脑电信号非线性分析. 江苏: 中国矿业大学, 2023.
13.	李霞, 李守伟. 基于EMD与DVG的非线性时间序列预测模型及其应用研究. 中国管理科学, 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, Kötter 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.	韦雷. 难治性癫痫脑电背景活动的非线性特征. 南宁: 广西医科大学, 2020.
20.	熊馨, 罗剑花, 武瑞锋, 等. 基于微状态方法的癫痫脑电信号识别研究. 传感技术学报, 2022, 35(12): 1671-1677.
21.	蔡冬梅, 周卫东, 刘凯, 等. 基于Hurst指数和SVM的癫痫脑电检测方法. 中国生物医学工程学报, 2010, 29(6): 836-840.
22.	崔刚强, 夏良斌, 梁建峰, 等. 基于小波多尺度分析和极限学习机的癫痫脑电分类算法. 生物医学工程学杂志, 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.	刘晓燕. 临床脑电图学(第2版). 北京: 人民卫生出版社, 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.	蒋丝丽, 罗华, 阮江海. 基于EEG的失神癫痫发作间期脑功能连接动态改变. 北京生物医学工程, 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. 癫痫网络的定义: 立体脑电图和信号分析的贡献, 郑舒畅, 译. 癫痫杂志, 2018, 4(2): 135-149.
2. 单宝莲, 张力新, 徐舫舟, 等. 基于脑电信号的癫痫发作预测特征及识别. 生物化学与生物物理进展, 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. 癫痫的诊断与治疗—临床实践与思考. 北京: 人民卫生出版社, 2017: 624-700.
5. 蹇兆鑫. 颞叶癫痫小鼠海马网络中的高—低频信号研究. 成都: 电子科技大学, 2023.
6. 王小艳. 基于多尺度转移网络的非线性时间序列分析. 上海: 华东师范大学, 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. 基于人工智能的癫痫发作预测研究综述. 软件工程, 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. 张瑞, 宋江玲, 胡文凤. 癫痫脑电的特征提取方法综述. 西北大学学报(自然科学版), 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. 任彦霖. 基于复杂网络拓扑结构的脑电信号非线性分析. 江苏: 中国矿业大学, 2023.
13. 李霞, 李守伟. 基于EMD与DVG的非线性时间序列预测模型及其应用研究. 中国管理科学, 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, Kötter 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. 韦雷. 难治性癫痫脑电背景活动的非线性特征. 南宁: 广西医科大学, 2020.
20. 熊馨, 罗剑花, 武瑞锋, 等. 基于微状态方法的癫痫脑电信号识别研究. 传感技术学报, 2022, 35(12): 1671-1677.
21. 蔡冬梅, 周卫东, 刘凯, 等. 基于Hurst指数和SVM的癫痫脑电检测方法. 中国生物医学工程学报, 2010, 29(6): 836-840.
22. 崔刚强, 夏良斌, 梁建峰, 等. 基于小波多尺度分析和极限学习机的癫痫脑电分类算法. 生物医学工程学杂志, 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. 刘晓燕. 临床脑电图学(第2版). 北京: 人民卫生出版社, 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. 蒋丝丽, 罗华, 阮江海. 基于EEG的失神癫痫发作间期脑功能连接动态改变. 北京生物医学工程, 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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