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改进高频振荡的识别。

Improving the identification of High Frequency Oscillations.

作者信息

Zelmann Rina, Zijlmans Maeike, Jacobs Julia, Châtillon Claude-E, Gotman Jean

机构信息

Montreal Neurological Institute and Hospital, EEG Department, McGill University, Que., Canada.

出版信息

Clin Neurophysiol. 2009 Aug;120(8):1457-64. doi: 10.1016/j.clinph.2009.05.029. Epub 2009 Jul 2.

Abstract

OBJECTIVE

High Frequency Oscillations (HFOs), including Ripples (80-250Hz) and Fast Ripples (250-500Hz), can be recorded from intracranial macroelectrodes in patients with intractable epilepsy. We implemented a procedure to establish the duration for which a stable measurement of rate of HFOs is achieved.

METHODS

To determine concordance, Kappa coefficient was computed. The information gained when increasing the duration was analyzed in terms of HFO rates and ranking of channels with respect to HFO and spike rates.

RESULTS

In a group of 30 patients, Kappa was 0.7 for ripples, 0.7 for fast ripples and 0.67 for spikes. Five minutes provided the same information as 10min in terms of rates in 9/10 patients and with respect to ranking of channels in 8/10 patients; 5/30 patients did not achieve stable measurements of HFOs or spikes and needed marking for 10min.

CONCLUSION

We propose that 5min provides in most cases the same information as a longer interval when identifying HFOs and spikes in slow wave sleep, and present methods to identify when this is not the case.

SIGNIFICANCE

This procedure is useful to control for consistency between readers and to evaluate if the selected interval provides stable information, for automatic and visual identification of events.

摘要

目的

高频振荡(HFOs),包括涟漪波(80 - 250Hz)和快速涟漪波(250 - 500Hz),可从难治性癫痫患者的颅内宏观电极记录到。我们实施了一个程序来确定实现HFOs频率稳定测量所需的持续时间。

方法

为确定一致性,计算了kappa系数。根据HFOs频率以及通道相对于HFOs和棘波频率的排名,分析了增加持续时间时所获得的信息。

结果

在一组30名患者中,涟漪波的kappa值为0.7,快速涟漪波为0.7,棘波为0.67。就9/10患者的频率以及8/10患者的通道排名而言,5分钟提供的信息与10分钟相同;5/30的患者未实现HFOs或棘波的稳定测量,需要记录10分钟。

结论

我们提出,在慢波睡眠中识别HFOs和棘波时,大多数情况下5分钟与更长的时间间隔提供的信息相同,并给出了识别并非如此的情况的方法。

意义

该程序有助于控制读者之间的一致性,并评估所选间隔是否提供稳定信息,用于事件的自动和视觉识别。

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