In this paper, we present an approach for the anticipation of electroencephalography (EEG) seizures using different families of wavelet transform. Different signal attributes are investigated to anticipate the seizure onset based on the wavelet transform. These attributes comprise amplitude, local mean, local median, local variance, derivative, and entropy of the wavelet-transformed signals. Different wavelet families are considered including Haar, Daubechies (db4, and db8), Symlets (Sym4), and Coiflets (Coif4) wavelets. The seizure prediction process is intended to be simple to be applied on a mobile application accompanying the patient to give him alerts of possible incoming seizures. The proposed approach is performed on long-term EEG recordings from the available CHB-MIT scalp dataset. It gives the best results in comparison with the other previous algorithms. It achieves a high sensitivity of 100% with Daubechies wavelet transform (db4) in addition to a low average False Prediction Rate (FPR) of 0.0818 h⁻¹ and a high average Prediction Time (PT) of 38.1676 min. Therefore, it can help specialists for the prediction of epileptic seizures as early as possible.

在本文中，我们提出了一种使用不同家族的小波变换预测脑电图（EEG）发作的方法。研究了不同的信号属性，以基于小波变换预测癫痫发作。这些属性包括小波变换信号的幅度，局部均值，局部中值，局部方差，导数和熵。考虑使用不同的小波族，包括Haar，Daubechies（db4和db8），Symlets（Sym4）和Coiflets（Coif4）小波。癫痫发作预测过程旨在简单地应用于伴随患者的移动应用程序，以提醒他可能的癫痫发作。建议的方法是对来自可用CHB-MIT头皮数据集的长期EEG记录执行的。与其他先前算法相比，它提供了最佳结果。使用Daubechies小波变换（db4）可获得100％的高灵敏度，此外平均误报率也很低（FPR）为0.0818 h ^-1，平均预测时间（PT）为38.1676分钟。因此，它可以帮助专家尽早预测癫痫发作。