Time series prediction of EEG signals based on a multi-scale approach

Aiming at the problem of difficulty in judgment and prediction due to non-periodicity, large amplitude and uncertainty of EEG signals, a multi-scale feature extraction based EEG signal classification method for epileptic seizure prediction is adopted, which uses null convolution to extract multi-scale features of EEG signals, in order to capture both global and local features of the signals. The experiments were performed on the CHB-MIT public dataset. The results show that the method significantly outperforms the traditional method in terms of accuracy, sensitivity and specificity, with an accuracy of 98.10%, a sensitivity of 92.95% and a specificity of 98.85%. It proves that the method based on multi-scale feature extraction has a good prospect in epileptic seizure prediction.