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首页> 外文期刊>NeuroImage >Detection of hemodynamic responses to epileptic activity using simultaneous Electro-EncephaloGraphy (EEG)/Near Infra Red Spectroscopy (NIRS) acquisitions.
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Detection of hemodynamic responses to epileptic activity using simultaneous Electro-EncephaloGraphy (EEG)/Near Infra Red Spectroscopy (NIRS) acquisitions.

机译:使用同步脑电图(EEG)/近红外光谱(NIRS)采集来检测对癫痫活动的血液动力学响应。

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摘要

Simultaneous recordings of Electro-EncephaloGraphy (EEG) with Near InfraRed Spectroscopy (NIRS) allow measuring hemodynamic changes (changes in the concentration of oxy- and deoxyhemoglobin) at the time of epileptic discharges detected on scalp EEG. Two NIRS detection methods based on the General Linear Model (GLM) respectively in the time domain and in the time-frequency domain are investigated in this study using realistic simulations of spontaneous interictal epileptic activity. We evaluated the sensitivity at different Signal to Noise Ratios (SNR), the effect of either a large or a small number of discharges and the impact of model misspecification (e.g. omission or false detection of epileptic discharges). We also explored the effect on the estimation of key parameters, which set the model order. Simulations showed that both methods become inaccurate in lower SNR conditions, leading to many false positive detections. However, the time-frequency estimator showed better performance than the time-domain one. Key parameters for each algorithm were identified and results suggest to model confounds in the GLM differently for oxy- and deoxyhemoglobin. We also demonstrated that an inaccurate marking of epileptic events has a small impact on the detection statistics whereas an inaccurate specification of the hemodynamic response function delay decreases drastically the detection abilities. Finally, we illustrated the two methods on clinical EEG/NIRS data of one patient with focal epilepsy, showing an increase of regional Cerebral Blood Volume (rCBV) spatially concordant with the presumed epileptogenic focus.
机译:通过近红外光谱(NIRS)同时记录脑电图(EEG),可以测量在头皮EEG上发现癫痫放电时的血流动力学变化(氧和脱氧血红蛋白浓度的变化)。使用自发性发作间期癫痫活动的真实模拟,研究了两种分别基于时域和时频域的基于通用线性模型(GLM)的NIRS检测方法。我们评估了不同信噪比(SNR)下的灵敏度,大量放电或少量放电的影响以及模型规格不正确的影响(例如遗漏或对癫痫放电的错误检测)。我们还探讨了对关键参数估计的影响,确定了模型的顺序。仿真表明,这两种方法在较低的SNR条件下变得不准确,从而导致许多误报检测。但是,时频估计器的性能优于时域估计器。确定了每种算法的关键参数,结果表明,对于氧和脱氧血红蛋白,GLM中的混杂建模方式不同。我们还证明,癫痫事件的不正确标记对检测统计数据影响很小,而血液动力学响应函数延迟的不准确说明会大大降低检测能力。最后,我们在一名局灶性癫痫患者的临床EEG / NIRS数据上举例说明了这两种方法,结果显示区域性脑血容量(rCBV)在空间上与假定的致癫痫病灶一致。

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