# Enhanced Epileptic Seizure Detection through Wavelet-Based Analysis of EEG Signal Processing
## 2 Related Works  
The realm of epileptic seizure detection in EEG signals has been considered as a significant progression in recent years, which is characterized by the introduction of novel techniques and approaches rooted in machine learning (ML). A comprehensive review of the literature unveils a diverse range of methodologies utilized in studying epileptic seizures in EEG signals, all united by the shared goal of enhancing diagnostic accuracy while streamlining analysis resources and time. In the following, a summary is presented regarding the latest advancements in this field, encompassing both recent advancements and pertinent classical research. Deep learning (DL) models have become widely used in EEG signal analysis due to their high accuracy. Convolutional neural networks (CNNs), in particular, are favored for their pooling layers that efficiently reduce data dimensionality and automate feature selection. This makes them a preferred choice among researchers. Despite their advantages, DL models are complex. They require substantial computational resources, involve numerous nonlinear functions, and need large datasets for effective generalization. Training these models can be time-consuming and involves careful tuning of parameters to avoid overfitting. Additionally, the inner workings of hidden layers can be opaque, posing challenges for implementation in simpler applications. To mitigate some of these challenges, [9] used a one-dimensional CNN (1D-CNN) with a single convolutional layer, achieving an impressive accuracy of 99.4%. Similarly, [10] utilized time and frequency domain signals in a three-layer CNN, reaching 97.5% accuracy. [11] achieved 92.37% accuracy using 2D CNNs for single electrodes and 3D CNNs for images derived from multiple electrodes. Recurrent neural networks (RNNs) have also proven effective in capturing the temporal dynamics of EEG signals. Long short-term memory (LSTM) networks and their variants, such as Bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and fully convolutional nested LSTM (FC-NLSTM), have shown remarkable accuracies of 99.3%, 100%, and 100%,respectively, as reported by [12; 13; 14]. The authors agree that these models, despite their high accuracy, require substantial computational resources. Transfer learning, exemplified by [15] combining networks like ResNet and Inception, achieves 100% accuracy. However, optimizing these large models for computational efficiency remains challenging due to their complex hidden layers. Another high-performing model is proposed by [16], utilizing a recurrent topic-synchronized variational autoencoder (RTS-RCVAE) with an accuracy of 98.43%, albeit at significant computational expense. Several studies have integrated DL techniques with signal processing, incorporating manual feature extraction. Many of these studies utilize Wavelet Transform (WT) for signal decomposition. The objective of employing these techniques is to reduce signal dimensionality, extracting signal representations across different frequency bands, thereby reducing the volume of data processed by DL models. Models combining WT with DL have consistently attained high accuracy performance. For instance, [17] utilized multiresolution Wavelet Transform (WT) in conjunction with an artificial neural network (ANN), achieving an accuracy of 99.6%. [18] explored continuous Wavelet Transform (CWT) for statistical feature extraction and classification using an LSTM, achieving 100% accuracy. Another model proposed by [19] employed Wavelet Packet Decomposition (WPD) and Fast Fourier Transform (FFT), reaching an accuracy of 98.33% through CNN training. The approach of combining Discrete Wavelet Transform (DWT) with LSTM training, proposed by [20], obtained 96.1% accuracy in classifying statistical features. In [21], Tunable-Q Wavelet Transform (TQWT) was applied to signals to compute multiple linear and nonlinear features, including statistical, frequency, and nonlinear aspects such as fractal dimensions (FDs) and entropy.