Determining the Quantitative Threshold of High-Frequency Oscillation Distribution to Delineate the Epileptogenic Zone by Automated Detection - Jiang, Chenxi; Li, Xiaonan; Yan, Jiaqing; Yu, Tao; Wang, Xueyuan; Ren, Zhiwei; Li, Donghong; Liu, Chang; Du, Wei; Zhou, Xiaoxia; Xing, Yue; Ren, Guoping; et al. (Frontiers in Neurology, 2018)
Objective: We propose an improved automated high frequency oscillations (HFOs) detector that could not only be applied to various intracranial electrodes, but also automatically remove false HFOs caused by high-pass filtering. We proposed a continuous resection ratio of high order HFO channels and compared this ratio with each patient's post-surgical outcomes to determine the quantitative threshold of HFO distribution to delineate epileptogenic zone (EZ). Methods: We enrolled a total of 43 refractory epilepsy patients. They were used to optimize parameters for SEEG electrodes, to test the algorithm for identifying false HFOs, and to calculate the continuous resection ratio of high order HFO channels. The ratio can be used to determine a quantitative threshold to locate the epileptogenic zone Results: Following optimization, the sensitivity and specificity of our detector were 66.84% and 73.20% (ripples) and 69.76% and 66.13% (FRs), respectively. The sensitivity and specificity of our algorithm for removing false HFOs were 76.82% and 94.54% (ripples) and 72.55% and 94.87% (FRs). The median of the continuous resection ratio of high order HFO channels in the patients with good surgical outcomes was significantly higher than in the patients with poor outcome for both ripples and FRs (P<0.05 ripples and P<0.001 FRs). Conclusions: Our automated detector has the advantage of not only applying to various intracranial electrodes but also removing false HFOs. Based on the continuous resection ratio of high order HFO channels, we can set the quantitative threshold for locating epileptogenic zones.