Zandi Ali Shahidi, Dumont Guy A, Javidan Manouchehr, Tafreshi Reza, MacLeod Bernard A, Ries Craig R, Puil Ernie
Department of Electrical & Computer Engineering at The University of British Columbia, Vancouver, BC V6T 1Z4, Canada.
Annu Int Conf IEEE Eng Med Biol Soc. 2008;2008:919-22. doi: 10.1109/IEMBS.2008.4649304.
In this paper, we propose a novel wavelet-based algorithm for the detection of epileptic seizures. The algorithm is based on the recognition of rhythmic activities associated with ictal states in surface EEG recordings. Using a moving-window analysis, we first decomposed each EEG segment into a wavelet packet tree. Then, we extracted the coefficients corresponding to the frequency band of interest defined for rhythmic activities. Finally, a normalized index sensitive to both the rhythmicity and energy of the EEG signal was derived, based on the resulting coefficients. In our study, we evaluated this combined index for real-time detection of epileptic seizures using a dataset of approximately 11.5 hours of multichannel scalp EEG recordings from three patients and compared it to our previously proposed wavelet-based index. In this dataset, the novel combined index detected all epileptic seizures with a false detection rate of 0.52/hr.
在本文中，我们提出了一种用于检测癫痫发作的基于小波的新型算法。该算法基于对表面脑电图记录中与发作期状态相关的节律性活动的识别。通过移动窗口分析，我们首先将每个脑电图片段分解为一个小波包树。然后，我们提取了与为节律性活动定义的感兴趣频带相对应的系数。最后，基于所得系数得出了一个对脑电图信号的节律性和能量均敏感的归一化指标。在我们的研究中，我们使用来自三名患者的约11.5小时多通道头皮脑电图记录数据集，评估了该组合指标用于癫痫发作实时检测的性能，并将其与我们先前提出的基于小波的指标进行了比较。在该数据集中，新型组合指标检测到了所有癫痫发作，误检率为0.52次/小时。
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