Epilepsy is a brain disorder related to alteration in the nervous system which affects around 65 million people among the world’s population. Few works are focused on prediction of seizure relied on deep learning approaches, but the capability of optimal design has no longer been absolutely exploited. This work is focused on the seizure prediction obtained from long-short time records using optimized deep learning network model (ODLN). In this paper, the synchronization patterns and its feasibility of distinguishing the pre-ictal from inter-ictal states are examined by utilizing the interaction graph model as a functional connectivity measure. An optimized deep learning network with short- long-term memory is computed for the prediction of epileptic seizures occurrences. For, the modelling of ODLN, pre-analysis is performed with three modules and memory layers. It is finalized from these results; a two-layer ODLN is optimum to perform the epileptic seizure prediction for four different window sizes from 15 to 120 min. The assessment is implemented on the CHB-MIT Scalp EEG data set, providing 100% sensitivity and low false prediction rate ranges from 0.10 to 0.02 for seizure prediction. The proposed ODLN methodology reveals a notable increase in the performance rate of seizure prediction when compared with existing machine learning and Convolutional neural networks methods.

癫痫病是一种与神经系统改变有关的脑部疾病，全世界约有6500万人受到影响。很少有研究将重点放在依赖于深度学习方法的癫痫发作预测上，但是最优设计的能力已不再被绝对利用。这项工作的重点是使用优化的深度学习网络模型（ODLN）从长短时间记录中获得的癫痫发作预测。在本文中，通过使用交互图模型作为功能连通性度量，研究了同步模式及其区分前期和后期状态的可行性。计算具有短期长期记忆的优化深度学习网络，以预测癫痫发作的发生。对于ODLN的建模，通过三个模块和存储层执行预分析。从这些结果中可以得出结论。两层ODLN最适合在15至120分钟内针对四种不同的窗口大小执行癫痫发作预测。该评估在CHB-MIT头皮EEG数据集上实施，可为癫痫发作预测提供100％的灵敏度和0.10至0.02的低错误预测率。与现有的机器学习和卷积神经网络方法相比，拟议的ODLN方法揭示了癫痫发作预测的执行率显着提高。提供100％的敏感性和低的虚假预测率（从0.10到0.02）用于癫痫发作预测。与现有的机器学习和卷积神经网络方法相比，拟议的ODLN方法揭示了癫痫发作预测的执行率显着提高。提供100％的敏感性和低的误判率（从0.10到0.02）用于癫痫发作预测。与现有的机器学习和卷积神经网络方法相比，拟议的ODLN方法揭示了癫痫发作预测的执行率显着提高。