Epilepsy is a severe neurological disease which is diagnosed by analyzing Electroencephalogram. The epileptic seizure detection technique based on multiscale entropies and complete ensemble empirical mode decomposition (CEEMD) is proposed in this paper. CEEMD is used for the estimation of sub-bands and two multiscale entropies; multiscale dispersion entropy (MDE) and refined composite MDE are extracted from the sub-bands. The feature selection method, configured by hybridizing the filter based and wrapper based method, is used to select relevant multiscale entropies. The hybrid method has not only reduced features but also improved classification performance. An artificial neural network is trained with relevant features and performance is measured using classification accuracy, sensitivity and specificity. Five clinically relevant classification problems are used to assess the proposed technique. The performance is also compared with the state of the art techniques. The proposed technique has shown an improvement in detection of seizures and can be used to build the clinical system for epileptic seizure detection.

癫痫病是一种严重的神经系统疾病，可通过分析脑电图来诊断。提出了一种基于多尺度熵和完全集成经验模态分解（CEEMD）的癫痫发作检测技术。CEEMD用于估计子带和两个多尺度熵。从子带中提取多尺度色散熵（MDE）和精细的复合MDE。通过将基于过滤器的方法和基于包装器的方法混合配置的特征选择方法，用于选择相关的多尺度熵。混合方法不仅减少了特征，而且改善了分类性能。训练具有相关特征的人工神经网络，并使用分类准确性，敏感性和特异性来测量性能。五个临床相关的分类问题用于评估所提出的技术。还将性能与现有技术水平进行比较。所提出的技术已显示出癫痫发作检测的改善，可用于建立癫痫发作检测的临床系统。