Temko Andriy, Sarkar Achintya, Lightbody Gordon
Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:6582-5. doi: 10.1109/EMBC.2015.7319901.
A system for detection of seizures in intracranial EEG is presented that is based on a combination of generative, discriminative and hybrid approaches. We present a methodology to effectively benefit from the advantages each classifier offers. In particular, Gaussian mixture models, Support Vector Machines, hybrid likelihood ratio and Gaussian supervector approaches are developed and combined for the task. This system participated in the UPenn and Mayo Clinic's Seizure Detection Challenge, ranking in the top 5 of over 200 participants. The drawbacks of the proposed method with respect to the winning solutions are critically assessed.
本文提出了一种基于生成式、判别式和混合方法相结合的颅内脑电图癫痫检测系统。我们提出了一种方法，以有效利用每个分类器的优势。特别是，针对该任务开发并组合了高斯混合模型、支持向量机、混合似然比和高斯超向量方法。该系统参加了宾夕法尼亚大学和梅奥诊所的癫痫检测挑战赛，在200多名参与者中排名前5。对所提方法相对于获胜方案的缺点进行了严格评估。
