IEEE Trans Neural Syst Rehabil Eng. 2018 Dec;26(12):2280-2289. doi: 10.1109/TNSRE.2018.2877820. Epub 2018 Oct 24.
This paper presents a new unsupervised detector for automatically detecting high-frequency oscillations (HFOs) using intracranial electroencephalogram (iEEG) signals. This detector does not presuppose a specific number of clusters and has a good performance. First, the HFO candidates are detected by an initial detection method which distinguishes HFOs from background activities. Then, as significant features, fuzzy entropy, short-time energy, power ratio, and spectral centroid of the HFO candidates are investigated and constructed as a feature vector. Finally, the feature vector is used as the input of the fuzzy- -means-quantization-error-modeling-based expectation-maximization-Gaussian mixture model clustering algorithm. This algorithm has the advantages of detecting HFOs and avoiding false detection caused by artifacts. The concentrations of detected HFOs are used to localize epileptic seizure onset zones in epileptic iEEG signal analysis. A comparison shows that our detector provides better localization performance in terms of sensitivity and specificity than five existing detectors.
本文提出了一种新的无监督检测器，用于使用颅内脑电图 (iEEG) 信号自动检测高频振荡 (HFOs)。该检测器不预先假定特定数量的聚类，并且具有良好的性能。首先，通过一种初始检测方法检测 HFO 候选者，该方法将 HFO 与背景活动区分开来。然后，作为显著特征，研究了 HFO 候选者的模糊熵、短时能量、功率比和频谱质心，并将其构建为特征向量。最后，将特征向量用作基于模糊均值量化误差建模的期望最大化-高斯混合模型聚类算法的输入。该算法具有检测 HFO 并避免由伪影引起的误检测的优点。检测到的 HFO 的浓度用于定位癫痫 iEEG 信号分析中的癫痫发作起始区。比较表明，我们的检测器在灵敏度和特异性方面提供了比五个现有检测器更好的定位性能。
