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Distinguishing false and true positive detections of high frequency oscillations.
Journal of Neural Engineering ( IF 4 ) Pub Date : 2020-10-08 , DOI: 10.1088/1741-2552/abb89b Stephen V Gliske 1, 2, 3 , Zihan Qin 3 , Katy Lau 4 , Catalina Alvarado-Rojas 5 , Pariya Salami 6 , Rina Zelmann 6 , William C Stacey 2, 3
Journal of Neural Engineering ( IF 4 ) Pub Date : 2020-10-08 , DOI: 10.1088/1741-2552/abb89b Stephen V Gliske 1, 2, 3 , Zihan Qin 3 , Katy Lau 4 , Catalina Alvarado-Rojas 5 , Pariya Salami 6 , Rina Zelmann 6 , William C Stacey 2, 3
Affiliation
Objective. High frequency oscillations (HFOs) are a promising biomarker of tissue that instigates seizures. However, ambiguous data and random background fluctuations can cause any HFO detector (human or automated) to falsely label non-HFO data as an HFO (a false positive detection). The objective of this paper was to identify quantitative features of HFOs that distinguish between true and false positive detections. Approach. Feature selection was performed using background data in multi-day, interictal intracranial recordings from ten patients. We selected the feature most similar between randomly selected segments of background data and HFOs detected in surrogate background data (false positive detections by construction). We then compared these results with fuzzy clustering of detected HFOs in clinical data to verify the feature’s applicability. We validated the feature is sensitive to false versus true positive HFO detections by using an independent data set (...
中文翻译:
区分高频振荡的假阳性和真阳性检测。
客观的。高频振荡 (HFO) 是一种很有前途的诱发癫痫发作的组织生物标志物。但是,模糊数据和随机背景波动可能导致任何 HFO 检测器(人工或自动)将非 HFO 数据错误地标记为 HFO(误报检测)。本文的目的是确定 HFO 的定量特征,以区分真假阳性检测。方法。使用来自 10 名患者的多天发作间期颅内记录中的背景数据进行特征选择。我们在随机选择的背景数据片段和在替代背景数据中检测到的 HFO 之间选择了最相似的特征(通过构建进行的误报检测)。然后,我们将这些结果与临床数据中检测到的 HFO 的模糊聚类进行比较,以验证该特征的适用性。
更新日期:2020-10-12
中文翻译:
区分高频振荡的假阳性和真阳性检测。
客观的。高频振荡 (HFO) 是一种很有前途的诱发癫痫发作的组织生物标志物。但是,模糊数据和随机背景波动可能导致任何 HFO 检测器(人工或自动)将非 HFO 数据错误地标记为 HFO(误报检测)。本文的目的是确定 HFO 的定量特征,以区分真假阳性检测。方法。使用来自 10 名患者的多天发作间期颅内记录中的背景数据进行特征选择。我们在随机选择的背景数据片段和在替代背景数据中检测到的 HFO 之间选择了最相似的特征(通过构建进行的误报检测)。然后,我们将这些结果与临床数据中检测到的 HFO 的模糊聚类进行比较,以验证该特征的适用性。