估计脑电图中癫痫样放电的时变周期性

J. O’Toole, B. G. Zapirain, Iratxe Maestro Saiz, Alina Beatriz Anaya Chen, I. Y. Santamaria
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引用次数: 5

摘要

周期性偏侧癫痫样放电(PLEDs)是脑损伤或疾病后可能出现的脑电图波形。pled的时变周期性或瞬时频率是一个潜在的重要预测特征。然而,估计瞬时频率是困难的,因为同时存在背景活动和伪影。本文提出了一种增强联合时频域瞬时频率特征的方法。该方法(1)利用简单的能量算子在时域增强PLED尖峰;2)利用可分核分布变换到时频域;3)在时频域中使用同态滤波方法去除频谱调制。然后将现有的瞬时频率估计方法应用于这种增强的时频分布。我们展示了具有EEG时代的工作示例,但尚未在整个EEG数据库上测试该方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Estimating the time-varying periodicity of epileptiform discharges in the electroencephalogram
Periodic lateralized epileptiform discharges (PLEDs) are EEG waveforms that can occur after brain injury or disease. The time-varying periodicity, or instantaneous frequency, of the PLEDs is a potentially important prognostic feature. Estimating the instantaneous frequency, however, is difficult because of the concurrent presence of background activity and artefacts. Here we present a method to enhance the instantaneous frequency features in the joint time-frequency domain. The procedure 1) enhances the PLED spikes in the time-domain using a simple energy operator; 2) transforms to the time-frequency domain using a separable-kernel distribution; and 3) uses a homomorphic filtering approach, within the time-frequency domain, to remove spectral modulation. Existing methods for instantaneous-frequency estimation are then applied to this enhanced time-frequency distribution. We show working examples with EEG epochs but have yet to test the method over an entire EEG database.
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