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首页> 外文期刊>Neurological Research >Time from ictal subdural EEG seizure onset to clinical seizure onset: an electrocorticographic time factor associated with temporal lobe epileptogenicity
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Time from ictal subdural EEG seizure onset to clinical seizure onset: an electrocorticographic time factor associated with temporal lobe epileptogenicity

机译:从发作性硬脑膜下脑电图发作到临床发作的时间:与颞叶癫痫发生有关的电皮质时间因子

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摘要

Long-term subdural video/electroencephalographic (EEG) monitoring was performed in a series of patients with medically intractable complex partial seizures, in a study of diagnostic accuracy, to test the hypothesis that the time from ictal subdural EEG seizure onset to clinical seizure onset (ECOT) is correlated with temporal lobe epileptogenicity and confirm measures of validity of ECOT for predicting seizure-free outcome following anterior temporal lobectomy and amygdalohippocampectomy (ATL/AH). In 34 patients with refractory temporal lobe epilepsy, subdural EEG monitoring localized the ictal epileptogenic focus to a single temporal lobe. In each patient, ECOT was analysed for correlation with temporal lobe epileptogenicity as measured by seizure interval in hours. Patients in whom ECOT was equal to or less than the mean (i.e. subdural EEG seizure onset preceding clinical seizure onset by at least 11.7 seconds) had a significantly greater likelihood of becoming seizure-free following ATL/AH compared to patients in whom ECOT was greater than the mean (i.e. subdural EEG seizure onset preceding clinical seizure onset by less than 11.7 seconds) (x2 = 5.78, p<0.05). The validity of ECOT for predicting seizure-free outcome following ATL/AH is confirmed to have sensitivity of 55.0%, specificity of 85.7%, false positive rate of 15.4%, false negative rate of 42.9%, diagnostic value of 84.6% and diagnostic accuracy of 67.6%. In addition, a significant correlation, described by a second order polynomial relationship, was found between the natural exponential function of ECOT and seizure interval [f(x=0.415x2 −25.554x + 267.036, r= 0.731, df= 32, t =6.05, p<0.001, where f(x)=eECOT and x= seizure interval). This result provides the epileptologist with a quantitative tool capable of predicting seizure interval based on ECOT. The capability of ECOT to predict seizure interval may allow the patient and epileptologist to anticipate future seizure onset based on ECOT, potentially facilitating accurate timing of ictal seizure focus localization techniques and clinical intervention to abort seizure onset using various available central and peripheral nervous system stimulation therapeutic strategies. The results suggest a relationship between ECOT and seizure interval. Fundamental pathophysiologic processes involved in the transition from ictal EEG to clinical seizure onset may be responsible for temporal lobe epileptogenicity.
机译:为了诊断准确性,对一系列具有医学上难治性复杂部分性癫痫发作的患者进行了长期硬膜下视频/脑电图(EEG)监测,以检验从发作性硬膜下脑电图癫痫发作到临床发作的时间的假设( ECOT)与颞叶癫痫发生率相关,并确认ECOT用于预测前颞叶切除和杏仁核海马切除术(ATL / AH)后无癫痫发作结果的有效性。在34例难治性颞叶癫痫患者中,硬脑膜下脑电图监测将发作性癫痫灶集中在单个颞叶上。在每位患者中,分析ECOT与颞叶癫痫发生的相关性,如癫痫发作间隔(以小时为单位)。与ECOT较大的患者相比,ECOT等于或小于平均值的患者(即在临床癫痫发作之前硬膜下脑电图发作至少11.7秒)在ATL / AH后无癫痫发作的可能性要大得多。比平均水平高(即硬膜下脑电图发作在临床发作之前少于11.7秒)(x 2 = 5.78,p <0.05)。经证实,ECOT可以预测ATL / AH后无癫痫发作的有效性,灵敏度为55.0%,特异性为85.7%,假阳性率为15.4%,假阴性率为42.9%,诊断值为84.6%,诊断准确性为67.6%。此外,在ECOT的自然指数函数和癫痫发作间隔[f(x = 0.415x 2 -25.554x + 267.036,r = 0.731,df = 32,t = 6.05,p <0.001,其中f(x)= e ECOT ,x =发作间隔)。该结果为癫痫学家提供了一种能够基于ECOT预测癫痫发作间隔的定量工具。 ECOT预测癫痫发作间隔的能力可能使患者和癫痫学家根据ECOT预测将来的癫痫发作,潜在地促进发作发作重点定位技术的准确时机和使用各种可用的中枢和周围神经系统刺激疗法中止癫痫发作的临床干预策略。结果表明ECOT与癫痫发作间隔之间存在关系。从发作性脑电图过渡到临床发作开始的基本病理生理过程可能与颞叶癫痫发生有关。

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  • 来源
    《Neurological Research》 |2007年第8期|862-870|共9页
  • 作者单位

    Division of Neurosurgery, Department of Surgery, University of Arizona College of Medicine, Tucson, AZ, USA;

    Division of Neurosurgery, Department of Surgery, University of Arizona College of Medicine, Tucson, AZ, USA;

    Division of Neurosurgery, Department of Surgery, University of Arizona College of Medicine, Tucson, AZ, USA;

    Department of Neurology, University of Arizona College of Medicine, Tucson, AZ, USA;

    Department of Neurology, University of Arizona College of Medicine, Tucson, AZ, USA;

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