Epilepsy, a neurological disorder, affects millions of persons worldwide. It is distinguished by causing recurrent seizures in patients, which can conduct to severe health problems. Consequently, it is essential to offer a method capable of timely predicting a seizure before its appearance, so patients can avoid possible injuries by taking preventive action. In this sense, a method based on the integration of discrete wavelet transform (DWT), fractal dimension, and support vector machine (SVM) is presented for the prediction of an epileptic seizure up to 30$$min before its onset through the analysis of electroencephalogram (EEG) signals. DWT is initially applied to the EEG signals to obtain their main neurological bands; then, five fractal dimension indices (e.g. Sevcik, Petrosian, Box, Higuchi, and Katz) are explored as potential seizure indicators. Finally, an SVM is developed to predict the epileptic seizure automatically. The effectiveness of the proposal to predict an epileptic crisis is validated by employing a database of 14 subjects with 42 epileptic seizures provided by the Massachusetts Institute of Technology and the Children’s Hospital Boston. The results demonstrate that the proposal can predict an epileptic seizure up to 30$$min before its onset with a high accuracy of 93.33%.

癫痫是一种神经系统疾病，影响着全世界数百万人。它的特点是导致患者反复癫痫发作，这可能导致严重的健康问题。因此，必须提供一种能够在发作前及时预测发作的方法，以便患者通过采取预防措施来避免可能的伤害。在这个意义上，提出了一种基于离散小波变换 (DWT)、分形维数和支持向量机 (SVM) 集成的方法，用于预测高达 30 次的癫痫发作。$$通过脑电图 (EEG) 信号的分析，在其发病前的分钟。DWT 最初应用于 EEG 信号以获得它们的主要神经频带；然后，探索了五个分形维数指数（例如 Sevcik、Petrosian、Box、Higuchi 和 Katz）作为潜在的缉获指标。最后，开发了一个支持向量机来自动预测癫痫发作。通过使用麻省理工学院和波士顿儿童医院提供的 14 名受试者和 42 次癫痫发作的数据库，验证了预测癫痫危机的建议的有效性。结果表明，该提案可以预测癫痫发作高达 30$$发病前的分钟，准确率高达 93.33%。