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Time- and frequency-resolved covariance analysis for detection and characterization of seizures from intracraneal EEG recordings.
Biological Cybernetics ( IF 1.7 ) Pub Date : 2020-07-12 , DOI: 10.1007/s00422-020-00840-y
Melisa Maidana Capitán 1 , Nuria Cámpora 2 , Claudio Sebastián Sigvard 1 , Silvia Kochen 2 , Inés Samengo 1
Affiliation  

The amount of power in different frequency bands of the electroencephalogram (EEG) carries information about the behavioral state of a subject. Hence, neurologists treating epileptic patients monitor the temporal evolution of the different bands. We propose a covariance-based method to detect and characterize epileptic seizures operating on the band-filtered EEG signal. The algorithm is unsupervised and performs a principal component analysis of intra-cranial EEG recordings, detecting transient fluctuations of the power in each frequency band. Its simplicity makes it suitable for online implementation. Good sampling of the non-ictal periods is required, while no demands are imposed on the amount of data during ictal activity. We tested the method with 32 seizures registered in 5 patients. The area below the resulting receiver-operating characteristic curves was 87% for the detection of seizures and 91% for the detection of recruited electrodes. To identify the behaviorally relevant correlates of the physiological signal, we identified transient changes in the variance of each band that were correlated with the degree of loss of consciousness, the latter assessed by the so-called Consciousness Seizure Scale, summarizing the performance of the subject in a number of behavioral tests requested during seizures. We concluded that those crisis with maximal impairment of consciousness tended to exhibit an increase in variance approximately 40 s after seizure onset, with predominant power in the theta and alpha bands and reduced delta and beta activity.



中文翻译:

用于检测和表征来自颅内 EEG 记录的癫痫发作的时间和频率分辨协方差分析。

脑电图 (EEG) 不同频段的功率大小携带有关受试者行为状态的信息。因此,治疗癫痫患者的神经科医生会监测不同波段的时间演变。我们提出了一种基于协方差的方法来检测和表征对带滤波 EEG 信号进行操作的癫痫发作。该算法是无监督的,对颅内 EEG 记录执行主成分分析,检测每个频带中的功率瞬态波动。它的简单性使其适合在线实施。需要对非发作期进行良好的采样,而对发作期活动期间的数据量没有要求。我们用 5 名患者的 32 次癫痫发作测试了该方法。所得接收器操作特征曲线下方的面积对于癫痫发作的检测为 87%,对于募集电极的检测为 91%。为了确定生理信号的行为相关相关性,我们确定了与意识丧失程度相关的每个波段方差的瞬态变化,后者通过所谓的意识癫痫量表评估,总结了受试者的表现在癫痫发作期间要求的许多行为测试中。我们得出的结论是,那些意识障碍最大的危机往往在癫痫发作后约 40 秒内表现出方差增加,在 theta 和 alpha 波段中占主导地位,而 delta 和 beta 活动减少。我们确定了与意识丧失程度相关的每个波段方差的瞬时变化,后者通过所谓的意识癫痫量表进行评估,总结了受试者在癫痫发作期间要求的许多行为测试中的表现。我们得出的结论是,那些具有最大意识障碍的危机往往在癫痫发作后约 40 秒内表现出方差增加,θ 和 alpha 波段占主导地位,而 delta 和 beta 活动减少。我们确定了与意识丧失程度相关的每个波段方差的瞬时变化,后者通过所谓的意识癫痫量表进行评估,总结了受试者在癫痫发作期间要求的许多行为测试中的表现。我们得出的结论是,那些意识障碍最大的危机往往在癫痫发作后约 40 秒内表现出方差增加,在 theta 和 alpha 波段中占主导地位,而 delta 和 beta 活动减少。

更新日期:2020-07-13
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