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应用因果分析方法对癫痫发作间期头皮脑电信号进行致痫灶定侧 被引量:1

Epileptic foci lateralization from interictal scalp EEG by applying causal analysis
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摘要 目的利用因果分析方法对癫痫发作间期的头皮脑电信号进行致痫灶定侧。方法在频域因果分析方法——自适应直接传递函数(ADTF)的基础上提出功率谱加权ADTF(psADTF)法,以给定频段内信号的功率谱对ADTF的标准化做加权,以适应不同癫痫波频域信息不同的特点。利用该方法对2组共30例患者的头皮脑电信号进行分析,含组1癫痫手术患者15例,组2门诊癫痫患者15例。其中组1患者共截取癫痫波样本数104个,组2患者共截取癫痫波样本数98个。结果组1患者通过psADTF分析对致痫灶定侧与手术侧一致的有96个,平均单个癫痫波致痫灶定侧准确率可达92%;组2患者通过psADTF分析对致痫灶定侧与专家判读结果一致的有94个,平均单个癫痫波致痫灶定侧准确率达96%。结论发作间期头皮脑电癫痫波信号的psADTF分析能够很好地辅助临床致痫灶定侧。 Objective To lateralize epileptic foci in interictal electroencephalography (EEG) signal by using causal analysis method. Methods On the basis of frequency domain causal analysis, the adapted directed transfer function (ADTF), power spectrum weighted ADTF(psADTF) method was proposed by applying the power spectrum information in defined frequency band with normalized ADTF, to fit the frequency information features of different epileptic wave. EEG signals of two groups of 30 epilepsy patients were analyzed by psADTF. Group I included 15 pre- surgical patients and 104 epilepsy spikes were picked from EEG signals. Group II included 15 outpatient patients and 98 epilepsy spikes were picked from EEG signals. Results 96 of 104 spikes of group I were lateralized properly by psADTF according to surgery sites, and the accuracy rate was 92%. 94 of 98 spikes of group II were lateralized properly by psADTF according to three technicians' judgments, with the accuracy rate of 96%. Conclusion Our results proves that the psADTF method over interictal EEG spikes can be a great help to clinical epileptic foci lateralization.
出处 《国际生物医学工程杂志》 CAS 2013年第5期261-265,I0001,共6页 International Journal of Biomedical Engineering
基金 卫生部行业公益基金资助项目(200902004-11)
关键词 癫痫 因果分析 发作间期脑电图 自适应直接传递函数 Epilepsy Causal analysis Interictal EEG Adapted directed transfer function
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参考文献19

  • 1Kaminski MJ, Blinowska KJ. A new method of the description of the infonnation flow in the brain structures[J]. Biol Cybern, 1991, 65(3): 203-210.
  • 2Astolfi L, Cincotti F, Mattia D, et al. Assessing cortical functional connectivity by linear inverse estimation and directed transfer func?tion: simulations and application to real data[J]. Clin Neurophysiol, 2005, 116(4): 920-932.
  • 3Gomez-Herrero G, Atienza M, Egiazarian K, et al. Measuring directional coupling between EEG sources[J]. Neuroimage, 2008, 43 (3): 497-508.
  • 4Wilke C, van Drongelen W, Kohnnan M, et al. Neocortical seizure foci localization by means of a directed transfer function method[J]. Epilepsia, 2010, 51(4): 564-572.
  • 5Ding Lei, Worrell GA, Lagerlund TD, et al. Ictal source analysis: localization and imaging of causal interactions in humans[J]. Neuroimage, 2007,34(2): 575-586.
  • 6Ding Ming-zhou, Bressler SL, Yang Wei-ming, et al. Short-window spectral analysis of cortical event-related potentials by adaptive multivariate autoregressive modeling: data preprocessing, model validation, and variability assessment[J]. Bioi Cybern, 2000, 83(1): 35-45.
  • 7Liang Hua-lou, Ding Ming-zhou, Nakamura R, et al. Causal influences in primate cerebral cortex during visual pattern discrimination[J]. Neuroreport, 2000, 11(13): 2875-2880.
  • 8Wilke C, Ding Lei, He Bin. Estimation of time-varying connectivity patterns through the use of an adaptive directed transfer function[J]. IEEE Trans Biomed Eng, 2008, 55(11): 2557-2564.
  • 9Astolfi L, Cincotti F, Mattia D, et al. Tracking the time-varying cortical connectivity patterns by adaptive multivariate estimators[J]. IEEE Trans Biomed Eng, 2008, 55(3): 902-913.
  • 10Wilke C, van Drongelen W, Kohnnan M, et al. Identification of epileptogenic foci from causal analysis of ECoG interictal spike activity[J]. Clin Neurophysiol, 2009, 120(8): 1449-1456.

二级参考文献20

  • 1Bressler SL, Seth AK. Wiener-Granger causality: a well established methodology[J|. Neuroimalge, 2011, 58(2): 323-329.
  • 2Bernasconi C, Konig P. On the directionality of cortical interactions studied by structural analysis of electrophysiological recordings [J]. Biol Cyberu, 1999, 81(3): 199-210.
  • 3Hesse W, Moiler E, Arnold M, et al. The use of time-variant EEG Granger causality for inspecting directed interdependencies of neural assemblies[J]. J Neurosci Methods, 2003, 124(1): 27-44.
  • 4Goebel R, Roebroeck A, Kim DS, et al. Investigating directed cortical interactions in time-resolved fMRI data using vector autoregressive modeling and Granger causality mapping[J]. Magn Reson Imaging, 2003, 21(10): 1251-1261.
  • 5Greene WH. Econometric Analysis[M]. 5th ed. New Jersey: Prentice Hall, 2002.
  • 6Geweke J. Measurement of linear dependence and feedback between multiple time series[J]. J Am Stat Assoc, 1982, 77(378): 304-313.
  • 7Geweke JF. Measures of conditional linear dependence and feedback between time series[J]. J Am Stat Assoc, 1984, 79(388): 907-915.
  • 8Chen G. Connedtivity analysis [EB/OL]. [2011-02-03]. http://afni. nimh.nih.gov/sscc/gangc/.
  • 9Roebroeck A, Formisano E, Goebel R. Mapping directed influence over the brain using Granger causality and fMRI [J]. Neuroimage, 2005, 25(1): 230-242.
  • 10Ding Ming-zhou, Bressler SL, Yang Wei-ming, et al. Short-window spectral analysis of cortical event-related potentials by adaptive multivariate autoregressive modeling: data preprocessing, model validation, and variability assessment[J]. Biol Cybern, 2000, 83(1): 35-45.

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  • 1ARBONELL F, NAGANO-SAITO A, LEYTON M, et al. Dopamine precursor depletion impairs structure and efficiency of resting state brain functional networks[J]. Neuropharmacology, 2014, 84: 90-100.
  • 2GRABBERR L, JOLA C, BERRA G, et al. Motor imagery training improves precision of an upper limb movement in patients with hemiparesis[J]. NeuroRehabilitation, 2015, 37(2): 263-271.
  • 3DING M, CHEN Y, and BRESSLER S L. Granger causality: basic theory and application to neuroscience[J]. Handbook of Time Series Analysis: Recent Theoretical Developments and Applications, 2006, 17: 437-460.
  • 4EPSTEIN C M, ADHIKARI B M, GROSS R, et al. Application of high-frequency Granger causality to analysis of epileptic seizures and surgical decision making[J]. Epilepsia, 2014, 55(12): 2038-2047.
  • 5NICOLAOU N, HOURR S, ALEXANDROU P, et al. EEG- based automatic classification of ‘awake’ versus ‘anesthetized’ state in general anesthesia using Granger causality[J]. PLoS One, 2012, 7(3): e33869.
  • 6ARNOLD A, LIU Y, and ABE N. Temporal causal modeling with graphical granger methods[C]. Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, 2007: 66-75.
  • 7BENJAMIN B, DORNHERE G, KRAUEDAT M, et al. The non-invasive Berlin brain-computer interface: fast acquisition of effective performance in untrained subjects[J]. NeuroImage, 2007, 37(2): 539-550.
  • 8JOHN S . Cortical functions[Z]. Routledge, 1999: 30-45.
  • 9GAO Qing, DUAN Xujun, and CHEN Huafu. Evaluation of effective connectivity of motor areas during motor imagery and execution using conditional Granger causality[J]. NeuroImage, 2011, 54(2): 1280-1288.
  • 10FRASER A M and SWINNEY H L. Independent coordinates for strange attractors from mutual information[J]. Physical Review A, 1986, 33(2): 1134-1140.

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