CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain. Department of Automatic Control (ESAII), Biomedical Engineering Research Centre (CREB), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain. Institut de Recerca Pediàtrica Hospital Sant Joan de Déu, Barcelona, Spain.
J Neural Eng. 2020 Apr 22;17(2):026032. doi: 10.1088/1741-2552/ab8345.
We propose a novel automated method called the S-Transform Gaussian Mixture detection algorithm (SGM) to detect high-frequency oscillations (HFO) combining the strengths of different families of previously published detectors.
This algorithm does not depend on parameter tuning on a subject (or database) basis, uses time-frequency characteristics, and relies on non-supervised classification to determine if the events standing out from the baseline activity are HFO or not. SGM consists of three steps: the first stage computes the signal baseline using the entropy of the autocorrelation; the second uses the S-Transform to obtain several time-frequency features (area, entropy, and time and frequency widths); and in the third stage Gaussian mixture models cluster time-frequency features to decide if events correspond to HFO-like activity. To validate the SGM algorithm we tested its performance in simulated and real environments.
We assessed the algorithm on a publicly available simulated stereoelectroencephalographic (SEEG) database with varying signal-to-noise ratios (SNR), obtaining very good results for medium and high SNR signals. We further tested the SGM algorithm on real signals from patients with focal epilepsy, in which HFO detection was performed visually by experts, yielding a high agreement between experts and SGM.
The SGM algorithm displayed proper performance in simulated and real environments and therefore can be used for non-supervised detection of HFO. This non-supervised algorithm does not require previous labelling by experts or parameter adjustment depending on the subject or database considered. SGM is not a computationally intensive algorithm, making it suitable to detect and characterize HFO in long-term SEEG recordings.
我们提出了一种新的自动方法，称为 S-变换高斯混合检测算法（SGM），该方法结合了之前发表的不同探测器家族的优势，用于检测高频振荡（HFO）。
该算法不依赖于对个体（或数据库）进行参数调整，利用时频特征，并依靠无监督分类来确定从基线活动中突出的事件是否为 HFO。SGM 由三个步骤组成：第一阶段使用自相关熵计算信号基线；第二阶段使用 S-变换获取几个时频特征（面积、熵以及时间和频率宽度）；第三阶段，高斯混合模型对时频特征进行聚类，以确定事件是否对应于 HFO 样活动。为了验证 SGM 算法，我们在具有不同信噪比（SNR）的公开可用的模拟立体脑电图（SEEG）数据库中测试了其性能，对中高 SNR 信号获得了非常好的结果。我们进一步在有局灶性癫痫患者的真实信号上测试了 SGM 算法，HFO 的检测由专家进行视觉评估，专家和 SGM 之间具有很高的一致性。
SGM 算法在模拟和真实环境中表现出良好的性能，因此可用于 HFO 的非监督检测。这种无监督算法不需要专家进行先前的标记，也不需要根据所考虑的个体或数据库进行参数调整。SGM 不是一种计算密集型算法，适用于检测和描述长期 SEEG 记录中的 HFO。
