Background and Objective: Epilepsy is one of the most common diseases of the nervous system, affecting about 1% of the world’s population. The unpredictable nature of the epilepsy seizures deprives the patients and those around them of living a normal life. Therefore, the development of new methods that can help these patients will increase the life quality of these people and can bring a lot of economic savings in the health sector.

Methods: In this study, we introduced a new framework for seizure onset detection. Our framework provides a new modelling for brain activity using evolutionary game theory and Kalman filter. If the patterns in the electroencephalogram (EEG) signal violate the predicted patterns by the proposed model, using a novel detection algorithm that has been also introduced in this paper, it can be determined whether the observed violation is the result of the onset of an epileptic seizure or not.

Results: The proposed approach was able to detect the onset of all the seizures in CHB-MIT dataset with an average delay of $-$0.8 s and a false alarm of 0.39 per hour. Also, our proposed approach is about 20 times faster compared to recent studies.

Conclusions: The experimental results of applying the proposed framework on the CHB-MIT dataset show that our framework not only performed well with respect to the sensitivity, delay, and false alarm metrics but also performed much better in terms of run time compared to recent studies. This appropriate run time, along with other suitable metrics, makes it possible to use this framework in many cases where processing power or energy is limited and to think about creating new and inexpensive solutions for the treatment and care of people diagnosed with epilepsy.

背景与目的：癫痫病是神经系统最常见的疾病之一，约占世界人口的1％。癫痫发作的不可预测的性质剥夺了患者及其周围患者的正常生活。因此，开发可以帮助这些患者的新方法将提高这些人的生活质量，并可以为卫生部门带来大量的经济节省。

方法：在这项研究中，我们介绍了癫痫发作检测的新框架。我们的框架使用进化博弈论和卡尔曼滤波器为大脑活动提供了新的模型。如果脑电图（EEG）信号中的模式违反了提出的模型所预测的模式，则使用本文中还介绍的新型检测算法，可以确定观察到的违反是否是癫痫发作的结果癫痫发作与否。

结果：所提出的方法能够检测CHB-MIT数据集中所有癫痫发作的发生，平均延迟为$-$0.8 s和每小时0.39的错误警报。此外，与最近的研究相比，我们提出的方法要快20倍左右。

结论：将建议的框架应用于CHB-MIT数据集的实验结果表明，与最近的研究相比，我们的框架不仅在灵敏度，延迟和错误警报指标方面表现良好，而且在运行时间方面也表现出色。这种适当的运行时间以及其他适当的度量标准，使得有可能在处理能力或能量受到限制的许多情况下使用此框架，并考虑为治疗和护理被诊断为癫痫的人提供新的，廉价的解决方案。