首页|Capital Medical University Reports Findings in Machine Learning (Practical measu rements distinguishing physiological and pathological stereoelectroencephalograp hy channels based on high-frequency oscillations in the human brain)
Capital Medical University Reports Findings in Machine Learning (Practical measu rements distinguishing physiological and pathological stereoelectroencephalograp hy channels based on high-frequency oscillations in the human brain)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting from Beijing, People’s Republ ic of China, by NewsRx journalists, research stated, “The present study aimed to identify various distinguishing features for use in the accurate classification of stereoelectroencephalography (SEEG) channels based on high-frequency oscilla tions (HFOs) inside and outside the epileptogenic zone (EZ). HFOs were detected in patients with focal epilepsy who underwent SEEG.” Funders for this research include National Natural Science Foundation of China, National Key Research and Development Program of China.
BeijingPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
