| Title |
Comparison of Deep Learning-based Seizure Prediction with Wavelet Transform and Preprocessing of EEG |
| Authors |
조용운(Yong Un Jo) ; 오도창(Do Chang Oh) |
| DOI |
https://doi.org/10.5573/ieie.2025.62.3.133 |
| Keywords |
Epilepsy; Seizure prediction; EEG; Wavelet transform; Deep learning |
| Abstract |
Epilepsy is a disease in which seizures occur irregularly. Sudden seizures during daily life can lead to serious accidents, and to prevent this, seizure prediction technology has been continuously researched. In this paper, we used the CHB-MIT database, including the period of seizures and their prodromes. We selected a portion of the database, applied three wavelet transforms, DWT, CWT, and TQWT, and classified them into ictal prodrome (preictal) and interictal period using a deep learning model. The results of three transform techniques are compared and a technique suitable for predicting patient seizures in real time is presented. Additionally, the size of the sliding window and the number of windows used were varied, and the prediction interval and predictable time were compared under various conditions. As a result, TQWT showed the best performance with 0.99 sensitivity, 0.94 f1 score, 0.09 FDR, and an average of 12 minutes in advance seizure prediction. For sliding window, using thirty windows of 30 seconds each showed the best performance. |