# Enhanced Epileptic Seizure Detection through Wavelet-Based Analysis of EEG Signal Processing
## 5 Conclusions  
Epilepsy is a disorder where early detection and treatment are crucial for improving the lives of millions of individuals. Thanks to continuous advancements in mathematical modeling and ML, we now have the ability to swiftly and affordably detect this condition through EEG signal analysis. This non-invasive approach enhances the quality of life for patients by offering quick and accurate diagnoses. The methodology proposed in this study has demonstrated satisfactory precision and reliability in identifying seizures within EEG signals, achieving the best accuracy of 92.82% for the alpha band. These results suggest that the model could be effectively utilized in epilepsy detection tasks, With the added advantage of its low computational complexity, enabling operation in resource-constrained computing environments. Additionally, the spectral signal analysis employed in this model, which focuses on specific brain activity frequency bands, can be scaled to detect various neurological disorders and general brain activities. Exploring future research opportunities could lead to further breakthroughs in signal processing techniques like image analysis or spectral analysis. These innovative approaches, along with traditional and deep learning models, have the potential to be extended beyond EEG signals, broadening their impact across different types of data.  
Conceptualization, S.U.F., A.D.F. and P.A.; methodology, S.U.F. and A.D.F.; software, S.U.F., A.D.F., P.A., D.Z.-B. and P.P.J.; validation, S.U.F., A.D.F. and P.A.; formal analysis, S.U.F., D.Z.-B., P.P.J. and C.A.-M.; investigation, S.U.F., A.D.F. and P.A.; resources, S.U.F. and A.D.F.; data curation, S.U.F. and A.D.F.; writing--original draft preparation, S.U.F. and P.A.; writing--review and editing, S.U.F., A.D.F., P.A., D.Z.-B., P.P.J. and C.A.-M.; visualization, S.U.F., A.D.F., P.A., D.Z.-B., P.P.J. and C.A.-M.; supervision, A.D.F.; project administration, A.D.F.; funding acquisition, A.D.F. All authors have read and agreed to the published version of the manuscript.  
The authors acknowledge the financial support from projects: ANID/FONDECYT Iniciacion No. 11230129, the Competition for Research Regular Projects, year 2021, code LPR21-02; Universidad Tecnologica Metropolitana, and cost center No: 02030402-999, Department of Electricity.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors acknowledge the financial support for the Projects: DICYT Regular No. 062313 AS and ANID/FONDECYT Iniciacion No. 11240799.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
ANN Artificial Neural Network  
BTD Block Term Decomposition  
CAE Convolutional Autoencoder  
CHB-MIT Children's Hospital Boston database  
CMIM Conditional Mutual Information Maximization  
CNN Convolutional Neural Network  
CPD Canonical Polyadic Decomposition  
CWT Continuous Wavelet Transform  
DCT Discrete Cosine Transform  
DE Deep Learning  
DWT Discrete Wavelet Transform  
EEG Electroencephalogram