A comprehensive insight into the epileptiform discharges at the time of seizure onset can aid neurophysiologists in the diagnosis and treatment of epileptic seizures. Visual analysis of seizure onset patterns is often a complex and tedious task. These problems suggest the development of automated seizure onset detection systems. The present research work is oriented for automatic detection of epileptic seizures at the onset using statistical measures. A quadratic classifier with fourfold cross-validation is used to demarcate the seizure and non-seizure activity. The algorithm is evaluated for 24 patients from the CHB MIT scalp EEG database. Classifier performance is assessed in terms of sensitivity, specificity, accuracy, and latency.

癫痫发作时对癫痫样放电的全面了解可以帮助神经生理学家诊断和治疗癫痫发作。癫痫发作模式的视觉分析通常是一项复杂而乏味的任务。这些问题表明自动癫痫发作发作检测系统的发展。本研究工作的重点是使用统计学方法在发作时自动检测癫痫发作。具有四重交叉验证的二次分类器用于区分癫痫发作和非癫痫发作活动。从CHB MIT头皮EEG数据库对24位患者进行了算法评估。根据敏感度，特异性，准确性和潜伏期评估分类器性能。