# An Efficient Hybrid Model for Patient-Independent Seizure Prediction Using Deep Learning
## 5 Conclusions  
A generalized epileptic seizure hybrid prediction model using DL techniques was introduced to minimize risks that can affect the patients during a seizure. The proposed model initially performed pre-processing on EEG signals, using the DWT technique, in order to acquire a noise-free signal, followed by deploying segmentation of the channel signal to different k-window length segments in order to enhance the performance of epileptic seizure prediction. The proposed model achieved an accuracy of 97.87% for 6 patients and 93.4% for 16 patients, respectively. Achieving improvement rates of 0.87% compared to the work in [19], 0.13% compared to those of [20], 0.1% compared to values in [21], and 3.54% compared to those of [23], respectively. These results show that the proposed work enhances the performance of epileptic seizure prediction through examining EEG signals with a high accuracy compared to some of state-of-the-art models in the literature. The findings in this paper could help to save patients' lives, learn more about the nature of pre-ictal vs. inter-ictal signals, and support patients to take important precautions to reduce risks during seizures.  
The limitation of the proposed work is that only two categories of seizure, pre-ictal and inter-ictal, are analyzed. Further work can be extended to deal with other categories of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Work** & **Approach/Dataset** & **Method** & **Accuracy (\%)** \\ \hline [PERSON] et al. [19] & Patient specific/CHB-MIT. 6 patients. & 1D CNN based model & 97\% \\ \hline [PERSON] et al. [20] & Patient specific/CHB-MIT. 2 channels only. & 1D CNN based model & 97.74\% \\ \hline [PERSON] et al. [21] & Patient specific/ CHB-MIT. 16 patients only. & 1D CNN based model & 93.3\% \\ \hline [PERSON] et al. [23] & Patient specific/CHB-MIT. 5 patients. & CNN based model & 94.33\% \\ \hline Proposed work & Non-patient specific/CHB-MIT. 6 patients. & 1D CNN based model & 97.87\% \\ \hline Proposed work & Non-patient specific/CHB-MIT. 16 patients. & 1D CNN based model & 93.4\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparative analysis study with previous state-of-the-art work in the same domain.  
Figure 10: Comparison of average accuracy for different k length windows.  
seizures. In addition, further research can examine the integration of the proposed model with cloud services and to extend the model for application to IoT platforms.  
Conceptualization, S.M.Y. and M.N.E.; methodology, S.M.Y. and M.N.E.; software, R.I.H.; validation, R.I.H., S.M.Y. and M.N.E.; formal analysis, R.I.H., S.M.Y. and M.N.E.; investigation, R.I.H., S.M.Y. and M.N.E.; resources, R.I.H. and M.N.E.; data curation, R.I.H.; writing--original draft preparation, R.I.H. and M.N.E.; writing--review and editing, R.I.H., S.M.Y. and M.N.E.; visualization, R.I.H., S.M.Y. and M.N.E.; supervision, S.M.Y. and M.N.E.; project administration, S.M.Y. and M.N.E.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability StatementData available in a publicly accessible repository. Publicly available datasets were analyzed in this study. This data can be found here: [[https://physionet.org/content/chbmit/1.0.0/](https://physionet.org/content/chbmit/1.0.0/)]([https://physionet.org/content/chbmit/1.0.0/](https://physionet.org/content/chbmit/1.0.0/)), (accessed on 30 April 2022) and [[https://www.upf.edu/web/ntsa/downloads](https://www.upf.edu/web/ntsa/downloads)]([https://www.upf.edu/web/ntsa/downloads](https://www.upf.edu/web/ntsa/downloads)), (accessed on 30 April 2022). The dataset files' license can be found at [[https://physionet.org/content/chbmit/view-licres/1.0.0/](https://physionet.org/content/chbmit/view-licres/1.0.0/)]([https://physionet.org/content/chbmit/view-licres/1.0.0/](https://physionet.org/content/chbmit/view-licres/1.0.0/)), (accessed on 30 April 2022).  
The authors declare no conflict of interest.