Epilepsy is the most common neurological disorder in humans. Electroencephalogram is a prevalent tool for diagnosing the epileptic seizure activity in clinical, which provides valuable information for understanding the physiological mechanisms behind epileptic disorders. Approximate entropy and recurrence quantification analysis are nonlinear analysis tools to quantify the complexity and recurrence behaviors of non-stationary signals, respectively. Convolutional neural networks are powerful class of models. In this paper, a new method for automatic epileptic electroencephalogram recordings based on the approximate entropy and recurrence quantification analysis combined with a convolutional neural network were proposed. The Bonn dataset was used to assess the proposed approach. The results indicated that the performance of the epileptic seizure detection by approximate entropy and recurrence quantification analysis is good (all of the sensitivities, specificities and accuracies are greater than 80%); especially the sensitivity, specificity and accuracy of the recurrence rate achieved 92.17%, 91.75% and 92.00%. When combines the approximate entropy and recurrence quantification analysis features with convolutional neural networks to automatically differentiate seizure electroencephalogram from normal recordings, the classification result can reach to 98.84%, 99.35% and 99.26%. Thus, this makes automatic detection of epileptic recordings become possible and it would be a valuable tool for the clinical diagnosis and treatment of epilepsy.

癫痫病是人类最常见的神经系统疾病。脑电图是诊断临床上癫痫发作活动的一种流行工具，可为了解癫痫病背后的生理机制提供有价值的信息。近似熵和递归量化分析是分别对非平稳信号的复杂性和递归行为进行量化的非线性分析工具。卷积神经网络是强大的模型类别。提出了一种基于近似熵和递归量化分析结合卷积神经网络的癫痫脑电图自动记录新方法。波恩数据集用于评估提出的方法。结果表明，通过近似熵和复发定量分析，癫痫发作的检测效果良好（所有敏感性，特异性和准确性均大于80％）；尤其是复发率的敏感性，特异性和准确性达到了92.17％，91.75％和92.00％。当将近似熵和递归量化分析功能与卷积神经网络相结合以自动区分癫痫发作脑电图与正常记录时，分类结果可达到98.84％，99.35％和99.26％。因此，这使得癫痫记录的自动检测成为可能，并且它将是用于癫痫的临床诊断和治疗的有价值的工具。