机构：[1]Center of Epilepsy, Center for Brain Disorders Research, Capital Medical University, Beijing, China,[2]Center of Epilepsy, Beijing Institute of Brain Disorders, Beijing, China,[3]Neuroelectrophysiological Laboratory, Xuanwu Hospital, Capital Medical University, Beijing, China,[4]College of Electrical and Control Engineering, North China University of Technology, Beijing, China,[5]Department of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China,神经外科[6]Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
Objective: We proposed 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 outcome, to determine the quantitative threshold of HFO distribution to delineate the epileptogenic zone (EZ). Methods: We enrolled a total of 43 patients diagnosed with refractory epilepsy. The patients were used to optimize the 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% (fast ripples, 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), respectively. The median of the continuous resection ratio of high order HFO channels in patients with good surgical outcomes, was significantly higher than in 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.
第一作者机构：[1]Center of Epilepsy, Center for Brain Disorders Research, Capital Medical University, Beijing, China,[2]Center of Epilepsy, Beijing Institute of Brain Disorders, Beijing, China,[3]Neuroelectrophysiological Laboratory, Xuanwu Hospital, Capital Medical University, Beijing, China,
共同第一作者：
通讯作者：
通讯机构：[1]Center of Epilepsy, Center for Brain Disorders Research, Capital Medical University, Beijing, China,[2]Center of Epilepsy, Beijing Institute of Brain Disorders, Beijing, China,[3]Neuroelectrophysiological Laboratory, Xuanwu Hospital, Capital Medical University, Beijing, China,[5]Department of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China,
推荐引用方式(GB/T 7714)：
Chenxi Jiang,Xiaonan Li,Jiaqing Yan,et al.Determining the Quantitative Threshold of High-Frequency Oscillation Distribution to Delineate the Epileptogenic Zone by Automated Detection[J].FRONTIERS IN NEUROLOGY.2018,9(NOV):889.doi:10.3389/fneur.2018.00889.
APA：
Chenxi Jiang,Xiaonan Li,Jiaqing Yan,Tao Yu,Xueyuan Wang...&Xiaofeng Yang.(2018).Determining the Quantitative Threshold of High-Frequency Oscillation Distribution to Delineate the Epileptogenic Zone by Automated Detection.FRONTIERS IN NEUROLOGY,9,(NOV)
MLA：
Chenxi Jiang,et al."Determining the Quantitative Threshold of High-Frequency Oscillation Distribution to Delineate the Epileptogenic Zone by Automated Detection".FRONTIERS IN NEUROLOGY 9..NOV(2018):889
